Santa Fe Institute Events Wiki - User contributions [en]

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-29T02:01:22Z

Acherif: /* Radicalization Mechanism, Terrorist Networks and Reactive Control Theoretical Approach */

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

===Problem solving and mating - are they similar?===

The discussions on this project have been moved to a separate page: [[Problem solving]]

=== Interacting distribution networks ===
Moved to its own page: [[Interacting distribution networks]]

===The Effect of Gossip on Social Networks===
Moved to a separate page: [[Modeling gossip networks]]

===Radicalization of Islamic Diasporas and Reactive Control Theoretical Approach===
See [[Radicalization]]

===From Topology to Response===
[[From_Topology_to_Response]]

===[[Spiking Networks on the Cusp of Chaos]]===

Please click the title to be transported to the project page.
===Modeling behaviors between students and teachers===
Update and Details about this project, please click here !
[http://www.santafe.edu/events/workshops/index.php/Modeling_behaviors_student&teacher Modeling behaviors between students and teachers]

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

====Acupuncture/Chinese Alternative Medicine====

Here are some more papers regarding research that has been done on acupuncture. Some network analysis has been done. Very interesting stuff!

[[Media: AcupunctureOverview.pdf|Acupuncture Overview]]: Here is an overview of acupuncture from a journal entitled "Alternative Therapies" in 1998.

[[Media: AcupunctureGraphTheory.pdf| Acupuncture and Graph Theory]]: This paper was written in "Progress in Natural Science" in 2009 which implements the use of graph theory to make a model to understand the effects of acupunture on brain function.

[[Media: AcupunctureFibroblasts.pdf|Body-Wide Cellular Network of Fibroblast Cells]]: A paper relating the study of a body-wide network of fibroblasts to acupuncture. Written in "Histochemistry and Cell Biology" in 2004.

[[Media: AcupunctureNeedleAdmin.pdf|Acupuncture-Psychosocial Context]] And another which studies the effects of the procedure. Written in "Advanced Access Publication" in 2008.

Enjoy! [[Karen Simpson]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

[[Mahyar Malekpour]]: Update June 16, 2009 - Someone asked if there is an application for this. The answer is yes, categorically, any self-organizing system needs synchronization. However, my interest here are visualization and exploration using agent-based tools. I don not intend to develop a solution to this problem, rather build on an existing agent-based model (if there is any) and enhance its capabilities.

[[Massimo Mastrangeli]]: as someone said, there is vast literature on synchronization available; you can for example get a taste in [http://www.amazon.com/SYNC-Emerging-Science-Spontaneous-Order/dp/0786868449 Sync] by Steven Strogatz (also, check out his talk [http://www.ted.com/talks/steven_strogatz_on_sync.html at TED]). I am quite interested in the idea.

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski | Marek]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response | project page]] [[Marek Kwiatkowski | Marek]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

* [[Mauricio Gonzalez-Forero]]: I would like to hear more about this project. Can we meet sometime?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

*[[watson]]: hmmm ... yes. this is exactly what I am talking about. very cool to hear you have some experience with this Massimo. right now i am leaning on going ahead with this project. i think we have serious potential to make impact, elucidate new relationships and phenomena and educate in the process. and i think it could be a ton of fun. here are a couple of links i have found which could be of use:
**[http://fusionanomaly.net/polyrhythms.html this] page talks about history, theory and even mentions chaos.
**[http://web.mit.edu/cjoye/www/music/tabla/ this] is a good source for tabla samples. tabla is one of the simpler devices that has some melodic structure as well as rhythmic structure to it. we could work with others as well... one thought is even just a drum kit of different sounds (rock style).

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and has looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects, too. His papers might be a good starting point.

[[Nathan Hodas]] I'd like to be in on this. I've pondered a good deal about this since reading the book. Maybe we should contact Jared Diamond?

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

[[Margreth Keiler]]: Murad and I had the same idea yesterday, but we thought to make each week a surveys to see how the network change over time and to add also after CSSS surveys. Should we discuss our draft tomorrow at SFI?

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to
‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity
in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to
allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

===Economic Geography in the Lake Titicaca Basin===

Moved to [http://www.santafe.edu/events/workshops/index.php/Economic_Geography_and_State_Emergence Economic Geography and State Emergence]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

[[Image:Theoretical_framework.jpg|480px|thumb|Theoretical_framework]]

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Looking forward to exchanging ideas!

[[Steven Lade]] Hi Almut, As I've said, I think this is well suited to modelling with differential equations. Particularly if, as I assume to be the case, the GIS data comes already in a rectangular grid. Having said that, there are some complementary aspects for which ABM would be well-suited. For instance, following agents as they form streams, or if you were to have a localised thunderstorm. We could possibly do this in parallel and see if they match and/or use each method's particular advantages.

You may be interested in this paper, which I found through the SFI library database: [http://pubs.usgs.gov/sir/2007/5009/pdf/sir_2007-5009.pdf]. I think this one is more complicated though, because they need to consider a three-dimensional water table. More generally, what sort of modelling (if any) do people usually do in these sorts of topics?

[[Karen Simpson]] I am interested in this project! I have studied these concepts in many of my classes. Through past research, I've looked at storm/rain events, and how a large runoff from stormwater causes high contaminant concentrations in streams and rivers. This research was done for urban, forest, and agricultural landuse types.(I will try to find the results of this research soon). Another thing to think about is the time between rain events. A long timespan between rainfall events will cause the soil to become unsaturated, and the next rainfall may have little effect on the stream. I also will not be around much this weekend, so would it be possible to meet sometime tomorrow (Thursday 6/18)?

===Scalable (parallel) Spatial Agent-Based Models===

This project idea is an exploration of what happens to agent-based models “in the large?” For example,
* As the number of interacting agents in a model increases, what happens to the dynamics of the model?
* What happens as the size of the agents’ domain increases (e.g. simulating a neighborhood versus simulating a city or country)
* How do the properties of the model change? Are there scaling laws in effect ?

In order to investigate these issues, we need a scalable simulation, i.e. a parallel implementation of the model that allows us to introduce arbitrarily large numbers of agents. There are many approaches to doing this [lit review needed here!], but for this project, I would like to focus on spatial agent-based models: models where there are N agents who exist in a geographical domain and possess “vision,” where vision can be optical/eye-based, local communications (audible or electromagnetic line of site).
A couple such models which can serve as starting points include the flocking model (aka “boids”) and Epstein’s model of civil violence (or its derivative “Rebellion” model).

The idea is to decompose the spatial domain into independent subdomains, distribute those subdomains to nodes on a compute cluster, amalgamate the results, wash-rinse-repeat. One possible approach is to use an adaptive mesh refinement (AMR) such as those used by engineers for finite element analysis or by physicists in hydrodynamics simulations. One concrete example, using a quad-tree decomposition to keep agent density constant on each processor (and thereby keeping computational load balanced), is as follows:

[[Image:Particle.PNG|thumb|left|An example showing decomposition of a particle system using a quad-tree. Each resulting square has (roughly) the same number of particles in it. Can this approach be used for parallelizing spatial agent-based models ?]]
I have a cluster available for implementation, along with the MPI libraries for parallel programming. Other suggested areas of expertise that would greatly benefit the project include:
Someone interested in evaluating simulation results, who can help ensure that we don’t break the model by decomposing it.
Someone interested in analysis, for exploring the effects of scaling on the model.
Someone interested in high-performance computing, to help with programming (probably c/c++ with MPI)

From talking to folks in our class, some other benefits of the approach include
* improving running time for very-long-running simulations
* aerospace applications—decomposing the National Air Space into computationally tractable subdomains for modeling or real-world purposes.
* Applying the decomposition technique to other model domains. For example, can a similar technique be used to decompose a social network, especially if a single model has both geographic spatial domains and also network domains?

Other approaches suggested by classmates have included implementation on GPUs (graphics processors used for general purpose computation) and sticking to an SMP implementation (multicore workstations with shared memory--simpler implementation/perhaps not as scalable), versus a distributed-memory cluster. I welcome further ideas that might help kick-start this zany scheme.

[[Steven Lade]] What I'm about to say seems kind of obvious, and I'm not sure it helps you at all, but I can't help but say that if your 'averaged behaviour' converges for very large numbers of agents, you'd in effect be modelling some partial differential equation.

[[Matt McMahon]] Thanks, Steven. Not obvious to me though ... Can you elucidate?

[[Steven Lade]] It seems that as you reach a large number of agents, and your grid becomes small, you'd most likely reach some partial differential equation in the density of agents. Say a diffusion equation. Or a Navier-Stokes (fluid flow) equation. Not sure how easy it would be to derive, but this would be my intuition. It would be easiest for local interactions only (i.e. some radius which you could let approach zero) but non-local interactions might be possible too. It would of course all depend on the agent rules you use. If you're lucky one might even be able to derive some analytical results for special cases. If you want to chat more, find me in person. (Anyone: does this allnmake sense?)

=== Resilience to invaders in social systems ===

A piece of anecdata from my organizing days: the effect of an external organizer coming to help on a local campaign had one of two -- very different -- effects: either further coalescing the local campaign, or fragmenting it.

I'm curious how well social structures tolerate interlopers and what drives their resilience.

Possible metaphors/methods which could be useful:
* An agent-based models of the connectivity of the underlying social structure & reaction to interloper?
* Analogizing to food-web/ecology with the interloper as an invasive species?
* Analogizing the interloper to a crystal defect?

BUT I have no idea 1) how to parameterize this and 2) whether there are data (of any sort -- eg resilience to colonists/prophets/carpetbaggers) to which the model could be compared for sanity-checking.

I know '''nothing''' about sociology & related fields, so maybe this is a well-studied problem. Or an ill-posed problem. Or maybe it's not a problem at all. In any event, I'd be curious to hear other's thoughts.

==Final Projects==

Please place your final project ideas here: details should include clear and objective outlines.

===The Effect of Gossip on Social Networks===
In this project we look at the effects of gossip spread on social network structure. We define gossip as information passed between two individuals A and B about an individual C who is not present, which has the potential to affect the strengths of all three relationships A-B, B-C, and A-C. This work is novel in two respects: first, there is no theoretical work on how network structure changes when information passing through a network has the potential to affect edges not in the direct path, and second while past studies have looked at how network structure affects gossip spread, there is no work done on how gossip spread affects network structure.

Page: [[Modeling gossip networks]]

Members:
* [[Allison Shaw]]
* [[Chang Yu]]
* [[Dave Brooks]]
* [[Milena Tsvetkova]]
* [[Roozbeh Daneshvar ]]

===1,2,3, language!===

In a nutshell:
In this project we will make use of information theoretic measures of similarity between data sets, such as mutual information
or more specifically some global allignment methods coming from evolutionary biology to build up a distance matrix between languages.
The data under study are simply the numbers 1,2,3...,10, for which we have access to a massive dataset that enumerates the spelling of the first ten numbers in more than 4,000 languages. We will finally derive the phylogenetic tree of languages, and compare it with the state of the art.

Members:
* [[Andrew Berdahl]]
* [[Lucas Lacasa]]

===Social mitosis in group conversations: a cooperative phenomenon approach===

In a nutshell:
When you participate in a conversation, you typically expect to (i) actively participate and (ii) be confortable in it. These arguments somewhat put some constraints in the number of persons attending the same conversation. In other words, when people are forced to stay in the same confined space, they tend to undertake a conversation, however if too many people are present, the conversation rapidly splits in two, three... some nucleation phenomenon takes place. In this project we approach this subject from a complex systems point of view and want to understand if the 'conversation mitosis' is a collective phenomenon, much in the vein of a symmetry-breaking phenomenon in statistical physics. We will develop an agent based model that captures the essential mechanisms of conversation dynamics and will characterize such behaviors. Analytical developments will also be addressed. Finally, we will compare our analytical/numerical results with empirical data gathered through e-mail surveys.

Members:
* [[Massimo Mastrangeli]]
* [[Martin Schmidt]]
* [[Lucas Lacasa]]

===Modeling mesoscopic sequential self-assembly===

One of the reasons for the huge success of microelectronics is the ability to produce very large amounts of devices at very small price. Anyway, a large part of the final price of electronic devices is due to assembly and packaging issues. The standard procedure to package microdevices is by robotic or even manual manipulation, which while satisfactory for large sizes becomes inefficient and even practically incontrollable below the millimeter scale. Moreover, when dealing with very large amounts of components the task becomes time-consuming and this expensive.

Self-assembly techniques have the potential to boost electronic assembly by their intrinsic massive parallelism and advantageous scaling properties. Particularly, self-assembly performed in liquid environment has gained momentum by showing interesting performance. Anyway, the analytic modeling of the dynamics of this process is still limited and not capable of capturing the details of the stochastic dynamics of self-assembly. In this project, I want to simulate the dynamics 2D and 3D sequential self-assembly with agent-based models. This framework, never so far applied to this task, may help sheding light on the role of important parameters of the process such as dimensions of the assembly space, redundance of components, viscosity of the fluid carrier.

[[Massimo Mastrangeli]]

===Percolation-like phenomenon in the Google search engine===

In a nutshell:
Type a (short) random string of letters in Google. This mimics the effect of mispelling words, 'typos'. Surprisingly, you will find a non-null amount of results: the probability of finding such a word, even if it's a random string without a semantic meaning, is non-null, since (i) someone could have already 'invented it' (acronym or so), (ii) someone could have mispelled a word (committed a typographic error) in his/her website/blog etc. But repeat the procedure with larger strings, and look how the number of results rapidly drops to zero... Is this a phase transition? Can we characterize such phenomenon? What are the relations between language-like properties and this behavior? What information can we extract? In this project we will endeavor such questions, programming automatic queries to google of randomly-generated strings and relating the system's behavior to some collective phenomena such as Percolation.

Members:
* [[Jacopo Tagliabue]]
* [[Andrew Berdahl]]
* [[Lucas Lacasa]]

===Competitive spatial network growth===
Many large-scale aggregate networks are actually composed of several essentially independent subnetworks, each of which takes into account the other agents' actions. While traditional optimization methods yield insight into the most efficient network structures to satisfy a fixed objective, the presence of several overlapping and evolving networks may change the optimal strategy or create niches for otherwise suboptimal strategies. In this project we develop an agent-based network growth model to simulate competitive airline network growth, studying the effects of the demand distribution, entry time, and number of agents on the success and network structure of the agents.

[[Interacting distribution networks]]

Members:
* [[Brendan Colloran]]
* [[Caroline Farrior]]
* [[Daniel Wuellner]]
* [[Michael Schultz]]

===Spectral clustering of gene expression===

1. Can we differentiate between genes involved in separate biological functions (ie pathways) using spectral clustering?

2. If so, can we use this method to detect the genes activated in cancer?

Members:
* [[Rosemary Braun]]
* [[Corinne Teeter]]
* [[Elliot Martin]]
* [[Eric Kasper]]

===From Topology to Response===
[[From_Topology_to_Response]]

Members:
* [[Marek Kwiatkowski]]
* [[Rosemary Braun]]
* [[Alexander Mikheyev | Sasha Mikheyev]]

===Foraging on the move===
Many animals (e.g. caribou, wildebeest) forage in groups while moving from one location to another. This means individuals have to simultaneously balance several demands: finding the best resources, maintaining the cohesion of the group, and migration in a certain direction. While there is a vast literature on both flocking and optimal foraging, there has been no work done to understand how animals should trade off the decision to flock or forage (since it is difficult to do both simultaneously) during migration. We develop an individual-based model to address this, and implement a genetic algorithm to find the best decision-rule for switching between foraging and flocking, under a variety of conditions.

Page: [[Foraging on the move]]

Members:
* [[Allison Shaw]]
* [[Andrew Berdahl]]
* [[Kathrine Behrman|Kate Behrman]]
* [[Liliana Salvador]]
* [[Steven Lade]]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Members:
* [[Steven Lade]]
* [[Karen Simpson]]
* [[Almut Brunner]]

Update and own page following soon!

===Creative Process===
The project attempts to model the generation of ideas in the subconscious as a random combination of existing concepts (reflected as words) and their selection (reflected as variance). The selection filter determines the quality and quantity of ideas that rise to the conscious. Although the complete model may not be in place by the end of the week, the presentation will display a basic version of the final (and hopefully publishable) paper.

* [[Murad Mithani]]

===A Markov Model of Elite Factionalization===

Authoritarian regimes fracture when elites within the ruling coalition, which buttresses the dictator, defect. Consequently, regime change crucially depends on elite competition and coordination. Previous work has explored this topic through conventional formal models that make exacting informational and cognitive demands on agents. In contrast, this model will attempt to replicate these findings, while exploring additional dynamics and emergent behavior, by embedding boundedly rational agents in a Markovian system. Rather than assume hyper rational actors, capable of solving difficult dynamic programming problems, I assume that elites use relatively simple heuristics to navigate a stochastic environment.

* [[Trevor Johnston]]

===Radicalization Mechanism, Terrorist Networks and Reactive Control Theoretical Approach===
See [[Radicalization]]

Group Members:

[[Alhaji Cherif]]

[[Hirotoshi Yoshioka]]

[[Prasanta Bose]]

[[Wei Ni]]

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-29T01:59:47Z

Acherif: /* Radicalization Mechanism, Terrorist Networks and Reactive Control Theoretical Approach */

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

===Problem solving and mating - are they similar?===

The discussions on this project have been moved to a separate page: [[Problem solving]]

=== Interacting distribution networks ===
Moved to its own page: [[Interacting distribution networks]]

===Modeling Gossip Networks===
Moved to a separate page: [[Modeling gossip networks]]

===Radicalization of Islamic Diasporas and Reactive Control Theoretical Approach===
See [[Radicalization]]

===From Topology to Response===
[[From_Topology_to_Response]]

===[[Spiking Networks on the Cusp of Chaos]]===

Please click the title to be transported to the project page.
===Modeling behaviors between students and teachers===
Update and Details about this project, please click here !
[http://www.santafe.edu/events/workshops/index.php/Modeling_behaviors_student&teacher Modeling behaviors between students and teachers]

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

====Acupuncture/Chinese Alternative Medicine====

Here are some more papers regarding research that has been done on acupuncture. Some network analysis has been done. Very interesting stuff!

[[Media: AcupunctureOverview.pdf|Acupuncture Overview]]: Here is an overview of acupuncture from a journal entitled "Alternative Therapies" in 1998.

[[Media: AcupunctureGraphTheory.pdf| Acupuncture and Graph Theory]]: This paper was written in "Progress in Natural Science" in 2009 which implements the use of graph theory to make a model to understand the effects of acupunture on brain function.

[[Media: AcupunctureFibroblasts.pdf|Body-Wide Cellular Network of Fibroblast Cells]]: A paper relating the study of a body-wide network of fibroblasts to acupuncture. Written in "Histochemistry and Cell Biology" in 2004.

[[Media: AcupunctureNeedleAdmin.pdf|Acupuncture-Psychosocial Context]] And another which studies the effects of the procedure. Written in "Advanced Access Publication" in 2008.

Enjoy! [[Karen Simpson]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

[[Mahyar Malekpour]]: Update June 16, 2009 - Someone asked if there is an application for this. The answer is yes, categorically, any self-organizing system needs synchronization. However, my interest here are visualization and exploration using agent-based tools. I don not intend to develop a solution to this problem, rather build on an existing agent-based model (if there is any) and enhance its capabilities.

[[Massimo Mastrangeli]]: as someone said, there is vast literature on synchronization available; you can for example get a taste in [http://www.amazon.com/SYNC-Emerging-Science-Spontaneous-Order/dp/0786868449 Sync] by Steven Strogatz (also, check out his talk [http://www.ted.com/talks/steven_strogatz_on_sync.html at TED]). I am quite interested in the idea.

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski | Marek]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response | project page]] [[Marek Kwiatkowski | Marek]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

* [[Mauricio Gonzalez-Forero]]: I would like to hear more about this project. Can we meet sometime?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

*[[watson]]: hmmm ... yes. this is exactly what I am talking about. very cool to hear you have some experience with this Massimo. right now i am leaning on going ahead with this project. i think we have serious potential to make impact, elucidate new relationships and phenomena and educate in the process. and i think it could be a ton of fun. here are a couple of links i have found which could be of use:
**[http://fusionanomaly.net/polyrhythms.html this] page talks about history, theory and even mentions chaos.
**[http://web.mit.edu/cjoye/www/music/tabla/ this] is a good source for tabla samples. tabla is one of the simpler devices that has some melodic structure as well as rhythmic structure to it. we could work with others as well... one thought is even just a drum kit of different sounds (rock style).

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and has looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects, too. His papers might be a good starting point.

[[Nathan Hodas]] I'd like to be in on this. I've pondered a good deal about this since reading the book. Maybe we should contact Jared Diamond?

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

[[Margreth Keiler]]: Murad and I had the same idea yesterday, but we thought to make each week a surveys to see how the network change over time and to add also after CSSS surveys. Should we discuss our draft tomorrow at SFI?

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to
‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity
in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to
allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

===Economic Geography in the Lake Titicaca Basin===

Moved to [http://www.santafe.edu/events/workshops/index.php/Economic_Geography_and_State_Emergence Economic Geography and State Emergence]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

[[Image:Theoretical_framework.jpg|480px|thumb|Theoretical_framework]]

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Looking forward to exchanging ideas!

[[Steven Lade]] Hi Almut, As I've said, I think this is well suited to modelling with differential equations. Particularly if, as I assume to be the case, the GIS data comes already in a rectangular grid. Having said that, there are some complementary aspects for which ABM would be well-suited. For instance, following agents as they form streams, or if you were to have a localised thunderstorm. We could possibly do this in parallel and see if they match and/or use each method's particular advantages.

You may be interested in this paper, which I found through the SFI library database: [http://pubs.usgs.gov/sir/2007/5009/pdf/sir_2007-5009.pdf]. I think this one is more complicated though, because they need to consider a three-dimensional water table. More generally, what sort of modelling (if any) do people usually do in these sorts of topics?

[[Karen Simpson]] I am interested in this project! I have studied these concepts in many of my classes. Through past research, I've looked at storm/rain events, and how a large runoff from stormwater causes high contaminant concentrations in streams and rivers. This research was done for urban, forest, and agricultural landuse types.(I will try to find the results of this research soon). Another thing to think about is the time between rain events. A long timespan between rainfall events will cause the soil to become unsaturated, and the next rainfall may have little effect on the stream. I also will not be around much this weekend, so would it be possible to meet sometime tomorrow (Thursday 6/18)?

===Scalable (parallel) Spatial Agent-Based Models===

This project idea is an exploration of what happens to agent-based models “in the large?” For example,
* As the number of interacting agents in a model increases, what happens to the dynamics of the model?
* What happens as the size of the agents’ domain increases (e.g. simulating a neighborhood versus simulating a city or country)
* How do the properties of the model change? Are there scaling laws in effect ?

In order to investigate these issues, we need a scalable simulation, i.e. a parallel implementation of the model that allows us to introduce arbitrarily large numbers of agents. There are many approaches to doing this [lit review needed here!], but for this project, I would like to focus on spatial agent-based models: models where there are N agents who exist in a geographical domain and possess “vision,” where vision can be optical/eye-based, local communications (audible or electromagnetic line of site).
A couple such models which can serve as starting points include the flocking model (aka “boids”) and Epstein’s model of civil violence (or its derivative “Rebellion” model).

The idea is to decompose the spatial domain into independent subdomains, distribute those subdomains to nodes on a compute cluster, amalgamate the results, wash-rinse-repeat. One possible approach is to use an adaptive mesh refinement (AMR) such as those used by engineers for finite element analysis or by physicists in hydrodynamics simulations. One concrete example, using a quad-tree decomposition to keep agent density constant on each processor (and thereby keeping computational load balanced), is as follows:

[[Image:Particle.PNG|thumb|left|An example showing decomposition of a particle system using a quad-tree. Each resulting square has (roughly) the same number of particles in it. Can this approach be used for parallelizing spatial agent-based models ?]]
I have a cluster available for implementation, along with the MPI libraries for parallel programming. Other suggested areas of expertise that would greatly benefit the project include:
Someone interested in evaluating simulation results, who can help ensure that we don’t break the model by decomposing it.
Someone interested in analysis, for exploring the effects of scaling on the model.
Someone interested in high-performance computing, to help with programming (probably c/c++ with MPI)

From talking to folks in our class, some other benefits of the approach include
* improving running time for very-long-running simulations
* aerospace applications—decomposing the National Air Space into computationally tractable subdomains for modeling or real-world purposes.
* Applying the decomposition technique to other model domains. For example, can a similar technique be used to decompose a social network, especially if a single model has both geographic spatial domains and also network domains?

Other approaches suggested by classmates have included implementation on GPUs (graphics processors used for general purpose computation) and sticking to an SMP implementation (multicore workstations with shared memory--simpler implementation/perhaps not as scalable), versus a distributed-memory cluster. I welcome further ideas that might help kick-start this zany scheme.

[[Steven Lade]] What I'm about to say seems kind of obvious, and I'm not sure it helps you at all, but I can't help but say that if your 'averaged behaviour' converges for very large numbers of agents, you'd in effect be modelling some partial differential equation.

[[Matt McMahon]] Thanks, Steven. Not obvious to me though ... Can you elucidate?

[[Steven Lade]] It seems that as you reach a large number of agents, and your grid becomes small, you'd most likely reach some partial differential equation in the density of agents. Say a diffusion equation. Or a Navier-Stokes (fluid flow) equation. Not sure how easy it would be to derive, but this would be my intuition. It would be easiest for local interactions only (i.e. some radius which you could let approach zero) but non-local interactions might be possible too. It would of course all depend on the agent rules you use. If you're lucky one might even be able to derive some analytical results for special cases. If you want to chat more, find me in person. (Anyone: does this allnmake sense?)

=== Resilience to invaders in social systems ===

A piece of anecdata from my organizing days: the effect of an external organizer coming to help on a local campaign had one of two -- very different -- effects: either further coalescing the local campaign, or fragmenting it.

I'm curious how well social structures tolerate interlopers and what drives their resilience.

Possible metaphors/methods which could be useful:
* An agent-based models of the connectivity of the underlying social structure & reaction to interloper?
* Analogizing to food-web/ecology with the interloper as an invasive species?
* Analogizing the interloper to a crystal defect?

BUT I have no idea 1) how to parameterize this and 2) whether there are data (of any sort -- eg resilience to colonists/prophets/carpetbaggers) to which the model could be compared for sanity-checking.

I know '''nothing''' about sociology & related fields, so maybe this is a well-studied problem. Or an ill-posed problem. Or maybe it's not a problem at all. In any event, I'd be curious to hear other's thoughts.

==Final Projects==

Please place your final project ideas here: details should include clear and objective outlines.

===Modeling gossip networks===
In this project we look at the effects of gossip spread on social network structure. We define gossip as information passed between two individuals A and B about an individual C who is not present, which has the potential to affect the strengths of all three relationships A-B, B-C, and A-C. This work is novel in two respects: first, there is no theoretical work on how network structure changes when information passing through a network has the potential to affect edges not in the direct path, and second while past studies have looked at how network structure affects gossip spread, there is no work done on how gossip spread affects network structure.

Page: [[Modeling gossip networks]]

Members:
* [[Allison Shaw]]
* [[Chang Yu]]
* [[Dave Brooks]]
* [[Milena Tsvetkova]]
* [[Roozbeh Daneshvar ]]

===1,2,3, language!===

In a nutshell:
In this project we will make use of information theoretic measures of similarity between data sets, such as mutual information
or more specifically some global allignment methods coming from evolutionary biology to build up a distance matrix between languages.
The data under study are simply the numbers 1,2,3...,10, for which we have access to a massive dataset that enumerates the spelling of the first ten numbers in more than 4,000 languages. We will finally derive the phylogenetic tree of languages, and compare it with the state of the art.

Members:
* [[Andrew Berdahl]]
* [[Lucas Lacasa]]

===Social mitosis in group conversations: a cooperative phenomenon approach===

In a nutshell:
When you participate in a conversation, you typically expect to (i) actively participate and (ii) be confortable in it. These arguments somewhat put some constraints in the number of persons attending the same conversation. In other words, when people are forced to stay in the same confined space, they tend to undertake a conversation, however if too many people are present, the conversation rapidly splits in two, three... some nucleation phenomenon takes place. In this project we approach this subject from a complex systems point of view and want to understand if the 'conversation mitosis' is a collective phenomenon, much in the vein of a symmetry-breaking phenomenon in statistical physics. We will develop an agent based model that captures the essential mechanisms of conversation dynamics and will characterize such behaviors. Analytical developments will also be addressed. Finally, we will compare our analytical/numerical results with empirical data gathered through e-mail surveys.

Members:
* [[Massimo Mastrangeli]]
* [[Martin Schmidt]]
* [[Lucas Lacasa]]

===Modeling mesoscopic sequential self-assembly===

One of the reasons for the huge success of microelectronics is the ability to produce very large amounts of devices at very small price. Anyway, a large part of the final price of electronic devices is due to assembly and packaging issues. The standard procedure to package microdevices is by robotic or even manual manipulation, which while satisfactory for large sizes becomes inefficient and even practically incontrollable below the millimeter scale. Moreover, when dealing with very large amounts of components the task becomes time-consuming and this expensive.

Self-assembly techniques have the potential to boost electronic assembly by their intrinsic massive parallelism and advantageous scaling properties. Particularly, self-assembly performed in liquid environment has gained momentum by showing interesting performance. Anyway, the analytic modeling of the dynamics of this process is still limited and not capable of capturing the details of the stochastic dynamics of self-assembly. In this project, I want to simulate the dynamics 2D and 3D sequential self-assembly with agent-based models. This framework, never so far applied to this task, may help sheding light on the role of important parameters of the process such as dimensions of the assembly space, redundance of components, viscosity of the fluid carrier.

[[Massimo Mastrangeli]]

===Percolation-like phenomenon in the Google search engine===

In a nutshell:
Type a (short) random string of letters in Google. This mimics the effect of mispelling words, 'typos'. Surprisingly, you will find a non-null amount of results: the probability of finding such a word, even if it's a random string without a semantic meaning, is non-null, since (i) someone could have already 'invented it' (acronym or so), (ii) someone could have mispelled a word (committed a typographic error) in his/her website/blog etc. But repeat the procedure with larger strings, and look how the number of results rapidly drops to zero... Is this a phase transition? Can we characterize such phenomenon? What are the relations between language-like properties and this behavior? What information can we extract? In this project we will endeavor such questions, programming automatic queries to google of randomly-generated strings and relating the system's behavior to some collective phenomena such as Percolation.

Members:
* [[Jacopo Tagliabue]]
* [[Andrew Berdahl]]
* [[Lucas Lacasa]]

===Competitive spatial network growth===
Many large-scale aggregate networks are actually composed of several essentially independent subnetworks, each of which takes into account the other agents' actions. While traditional optimization methods yield insight into the most efficient network structures to satisfy a fixed objective, the presence of several overlapping and evolving networks may change the optimal strategy or create niches for otherwise suboptimal strategies. In this project we develop an agent-based network growth model to simulate competitive airline network growth, studying the effects of the demand distribution, entry time, and number of agents on the success and network structure of the agents.

[[Interacting distribution networks]]

Members:
* [[Brendan Colloran]]
* [[Caroline Farrior]]
* [[Daniel Wuellner]]
* [[Michael Schultz]]

===Spectral clustering of gene expression===

1. Can we differentiate between genes involved in separate biological functions (ie pathways) using spectral clustering?

2. If so, can we use this method to detect the genes activated in cancer?

Members:
* [[Rosemary Braun]]
* [[Corinne Teeter]]
* [[Elliot Martin]]
* [[Eric Kasper]]

===From Topology to Response===
[[From_Topology_to_Response]]

Members:
* [[Marek Kwiatkowski]]
* [[Rosemary Braun]]
* [[Alexander Mikheyev | Sasha Mikheyev]]

===Foraging on the move===
Many animals (e.g. caribou, wildebeest) forage in groups while moving from one location to another. This means individuals have to simultaneously balance several demands: finding the best resources, maintaining the cohesion of the group, and migration in a certain direction. While there is a vast literature on both flocking and optimal foraging, there has been no work done to understand how animals should trade off the decision to flock or forage (since it is difficult to do both simultaneously) during migration. We develop an individual-based model to address this, and implement a genetic algorithm to find the best decision-rule for switching between foraging and flocking, under a variety of conditions.

Page: [[Foraging on the move]]

Members:
* [[Allison Shaw]]
* [[Andrew Berdahl]]
* [[Kathrine Behrman|Kate Behrman]]
* [[Liliana Salvador]]
* [[Steven Lade]]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Members:
* [[Steven Lade]]
* [[Karen Simpson]]
* [[Almut Brunner]]

Update and own page following soon!

===Creative Process===
The project attempts to model the generation of ideas in the subconscious as a random combination of existing concepts (reflected as words) and their selection (reflected as variance). The selection filter determines the quality and quantity of ideas that rise to the conscious. Although the complete model may not be in place by the end of the week, the presentation will display a basic version of the final (and hopefully publishable) paper.

* [[Murad Mithani]]

===A Markov Model of Elite Factionalization===

Authoritarian regimes fracture when elites within the ruling coalition, which buttresses the dictator, defect. Consequently, regime change crucially depends on elite competition and coordination. Previous work has explored this topic through conventional formal models that make exacting informational and cognitive demands on agents. In contrast, this model will attempt to replicate these findings, while exploring additional dynamics and emergent behavior, by embedding boundedly rational agents in a Markovian system. Rather than assume hyper rational actors, capable of solving difficult dynamic programming problems, I assume that elites use relatively simple heuristics to navigate a stochastic environment.

* [[Trevor Johnston]]

===Radicalization Mechanism, Terrorist Networks and Reactive Control Theoretical Approach===
See [[Radicalization]]
Group Members:

[[Alhaji Cherif]]

[[Hirotoshi Yoshioka]]

[[Prasanta Bose]]

[[Wei Ni]]

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-29T01:59:20Z

Acherif: /* Final Projects */

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

===Problem solving and mating - are they similar?===

The discussions on this project have been moved to a separate page: [[Problem solving]]

=== Interacting distribution networks ===
Moved to its own page: [[Interacting distribution networks]]

===Modeling Gossip Networks===
Moved to a separate page: [[Modeling gossip networks]]

===Radicalization of Islamic Diasporas and Reactive Control Theoretical Approach===
See [[Radicalization]]

===From Topology to Response===
[[From_Topology_to_Response]]

===[[Spiking Networks on the Cusp of Chaos]]===

Please click the title to be transported to the project page.
===Modeling behaviors between students and teachers===
Update and Details about this project, please click here !
[http://www.santafe.edu/events/workshops/index.php/Modeling_behaviors_student&teacher Modeling behaviors between students and teachers]

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

====Acupuncture/Chinese Alternative Medicine====

Here are some more papers regarding research that has been done on acupuncture. Some network analysis has been done. Very interesting stuff!

[[Media: AcupunctureOverview.pdf|Acupuncture Overview]]: Here is an overview of acupuncture from a journal entitled "Alternative Therapies" in 1998.

[[Media: AcupunctureGraphTheory.pdf| Acupuncture and Graph Theory]]: This paper was written in "Progress in Natural Science" in 2009 which implements the use of graph theory to make a model to understand the effects of acupunture on brain function.

[[Media: AcupunctureFibroblasts.pdf|Body-Wide Cellular Network of Fibroblast Cells]]: A paper relating the study of a body-wide network of fibroblasts to acupuncture. Written in "Histochemistry and Cell Biology" in 2004.

[[Media: AcupunctureNeedleAdmin.pdf|Acupuncture-Psychosocial Context]] And another which studies the effects of the procedure. Written in "Advanced Access Publication" in 2008.

Enjoy! [[Karen Simpson]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

[[Mahyar Malekpour]]: Update June 16, 2009 - Someone asked if there is an application for this. The answer is yes, categorically, any self-organizing system needs synchronization. However, my interest here are visualization and exploration using agent-based tools. I don not intend to develop a solution to this problem, rather build on an existing agent-based model (if there is any) and enhance its capabilities.

[[Massimo Mastrangeli]]: as someone said, there is vast literature on synchronization available; you can for example get a taste in [http://www.amazon.com/SYNC-Emerging-Science-Spontaneous-Order/dp/0786868449 Sync] by Steven Strogatz (also, check out his talk [http://www.ted.com/talks/steven_strogatz_on_sync.html at TED]). I am quite interested in the idea.

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski | Marek]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response | project page]] [[Marek Kwiatkowski | Marek]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

* [[Mauricio Gonzalez-Forero]]: I would like to hear more about this project. Can we meet sometime?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

*[[watson]]: hmmm ... yes. this is exactly what I am talking about. very cool to hear you have some experience with this Massimo. right now i am leaning on going ahead with this project. i think we have serious potential to make impact, elucidate new relationships and phenomena and educate in the process. and i think it could be a ton of fun. here are a couple of links i have found which could be of use:
**[http://fusionanomaly.net/polyrhythms.html this] page talks about history, theory and even mentions chaos.
**[http://web.mit.edu/cjoye/www/music/tabla/ this] is a good source for tabla samples. tabla is one of the simpler devices that has some melodic structure as well as rhythmic structure to it. we could work with others as well... one thought is even just a drum kit of different sounds (rock style).

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and has looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects, too. His papers might be a good starting point.

[[Nathan Hodas]] I'd like to be in on this. I've pondered a good deal about this since reading the book. Maybe we should contact Jared Diamond?

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

[[Margreth Keiler]]: Murad and I had the same idea yesterday, but we thought to make each week a surveys to see how the network change over time and to add also after CSSS surveys. Should we discuss our draft tomorrow at SFI?

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to
‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity
in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to
allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

===Economic Geography in the Lake Titicaca Basin===

Moved to [http://www.santafe.edu/events/workshops/index.php/Economic_Geography_and_State_Emergence Economic Geography and State Emergence]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

[[Image:Theoretical_framework.jpg|480px|thumb|Theoretical_framework]]

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Looking forward to exchanging ideas!

[[Steven Lade]] Hi Almut, As I've said, I think this is well suited to modelling with differential equations. Particularly if, as I assume to be the case, the GIS data comes already in a rectangular grid. Having said that, there are some complementary aspects for which ABM would be well-suited. For instance, following agents as they form streams, or if you were to have a localised thunderstorm. We could possibly do this in parallel and see if they match and/or use each method's particular advantages.

You may be interested in this paper, which I found through the SFI library database: [http://pubs.usgs.gov/sir/2007/5009/pdf/sir_2007-5009.pdf]. I think this one is more complicated though, because they need to consider a three-dimensional water table. More generally, what sort of modelling (if any) do people usually do in these sorts of topics?

[[Karen Simpson]] I am interested in this project! I have studied these concepts in many of my classes. Through past research, I've looked at storm/rain events, and how a large runoff from stormwater causes high contaminant concentrations in streams and rivers. This research was done for urban, forest, and agricultural landuse types.(I will try to find the results of this research soon). Another thing to think about is the time between rain events. A long timespan between rainfall events will cause the soil to become unsaturated, and the next rainfall may have little effect on the stream. I also will not be around much this weekend, so would it be possible to meet sometime tomorrow (Thursday 6/18)?

===Scalable (parallel) Spatial Agent-Based Models===

This project idea is an exploration of what happens to agent-based models “in the large?” For example,
* As the number of interacting agents in a model increases, what happens to the dynamics of the model?
* What happens as the size of the agents’ domain increases (e.g. simulating a neighborhood versus simulating a city or country)
* How do the properties of the model change? Are there scaling laws in effect ?

In order to investigate these issues, we need a scalable simulation, i.e. a parallel implementation of the model that allows us to introduce arbitrarily large numbers of agents. There are many approaches to doing this [lit review needed here!], but for this project, I would like to focus on spatial agent-based models: models where there are N agents who exist in a geographical domain and possess “vision,” where vision can be optical/eye-based, local communications (audible or electromagnetic line of site).
A couple such models which can serve as starting points include the flocking model (aka “boids”) and Epstein’s model of civil violence (or its derivative “Rebellion” model).

The idea is to decompose the spatial domain into independent subdomains, distribute those subdomains to nodes on a compute cluster, amalgamate the results, wash-rinse-repeat. One possible approach is to use an adaptive mesh refinement (AMR) such as those used by engineers for finite element analysis or by physicists in hydrodynamics simulations. One concrete example, using a quad-tree decomposition to keep agent density constant on each processor (and thereby keeping computational load balanced), is as follows:

[[Image:Particle.PNG|thumb|left|An example showing decomposition of a particle system using a quad-tree. Each resulting square has (roughly) the same number of particles in it. Can this approach be used for parallelizing spatial agent-based models ?]]
I have a cluster available for implementation, along with the MPI libraries for parallel programming. Other suggested areas of expertise that would greatly benefit the project include:
Someone interested in evaluating simulation results, who can help ensure that we don’t break the model by decomposing it.
Someone interested in analysis, for exploring the effects of scaling on the model.
Someone interested in high-performance computing, to help with programming (probably c/c++ with MPI)

From talking to folks in our class, some other benefits of the approach include
* improving running time for very-long-running simulations
* aerospace applications—decomposing the National Air Space into computationally tractable subdomains for modeling or real-world purposes.
* Applying the decomposition technique to other model domains. For example, can a similar technique be used to decompose a social network, especially if a single model has both geographic spatial domains and also network domains?

Other approaches suggested by classmates have included implementation on GPUs (graphics processors used for general purpose computation) and sticking to an SMP implementation (multicore workstations with shared memory--simpler implementation/perhaps not as scalable), versus a distributed-memory cluster. I welcome further ideas that might help kick-start this zany scheme.

[[Steven Lade]] What I'm about to say seems kind of obvious, and I'm not sure it helps you at all, but I can't help but say that if your 'averaged behaviour' converges for very large numbers of agents, you'd in effect be modelling some partial differential equation.

[[Matt McMahon]] Thanks, Steven. Not obvious to me though ... Can you elucidate?

[[Steven Lade]] It seems that as you reach a large number of agents, and your grid becomes small, you'd most likely reach some partial differential equation in the density of agents. Say a diffusion equation. Or a Navier-Stokes (fluid flow) equation. Not sure how easy it would be to derive, but this would be my intuition. It would be easiest for local interactions only (i.e. some radius which you could let approach zero) but non-local interactions might be possible too. It would of course all depend on the agent rules you use. If you're lucky one might even be able to derive some analytical results for special cases. If you want to chat more, find me in person. (Anyone: does this allnmake sense?)

=== Resilience to invaders in social systems ===

A piece of anecdata from my organizing days: the effect of an external organizer coming to help on a local campaign had one of two -- very different -- effects: either further coalescing the local campaign, or fragmenting it.

I'm curious how well social structures tolerate interlopers and what drives their resilience.

Possible metaphors/methods which could be useful:
* An agent-based models of the connectivity of the underlying social structure & reaction to interloper?
* Analogizing to food-web/ecology with the interloper as an invasive species?
* Analogizing the interloper to a crystal defect?

BUT I have no idea 1) how to parameterize this and 2) whether there are data (of any sort -- eg resilience to colonists/prophets/carpetbaggers) to which the model could be compared for sanity-checking.

I know '''nothing''' about sociology & related fields, so maybe this is a well-studied problem. Or an ill-posed problem. Or maybe it's not a problem at all. In any event, I'd be curious to hear other's thoughts.

==Final Projects==

Please place your final project ideas here: details should include clear and objective outlines.

===Modeling gossip networks===
In this project we look at the effects of gossip spread on social network structure. We define gossip as information passed between two individuals A and B about an individual C who is not present, which has the potential to affect the strengths of all three relationships A-B, B-C, and A-C. This work is novel in two respects: first, there is no theoretical work on how network structure changes when information passing through a network has the potential to affect edges not in the direct path, and second while past studies have looked at how network structure affects gossip spread, there is no work done on how gossip spread affects network structure.

Page: [[Modeling gossip networks]]

Members:
* [[Allison Shaw]]
* [[Chang Yu]]
* [[Dave Brooks]]
* [[Milena Tsvetkova]]
* [[Roozbeh Daneshvar ]]

===1,2,3, language!===

In a nutshell:
In this project we will make use of information theoretic measures of similarity between data sets, such as mutual information
or more specifically some global allignment methods coming from evolutionary biology to build up a distance matrix between languages.
The data under study are simply the numbers 1,2,3...,10, for which we have access to a massive dataset that enumerates the spelling of the first ten numbers in more than 4,000 languages. We will finally derive the phylogenetic tree of languages, and compare it with the state of the art.

Members:
* [[Andrew Berdahl]]
* [[Lucas Lacasa]]

===Social mitosis in group conversations: a cooperative phenomenon approach===

In a nutshell:
When you participate in a conversation, you typically expect to (i) actively participate and (ii) be confortable in it. These arguments somewhat put some constraints in the number of persons attending the same conversation. In other words, when people are forced to stay in the same confined space, they tend to undertake a conversation, however if too many people are present, the conversation rapidly splits in two, three... some nucleation phenomenon takes place. In this project we approach this subject from a complex systems point of view and want to understand if the 'conversation mitosis' is a collective phenomenon, much in the vein of a symmetry-breaking phenomenon in statistical physics. We will develop an agent based model that captures the essential mechanisms of conversation dynamics and will characterize such behaviors. Analytical developments will also be addressed. Finally, we will compare our analytical/numerical results with empirical data gathered through e-mail surveys.

Members:
* [[Massimo Mastrangeli]]
* [[Martin Schmidt]]
* [[Lucas Lacasa]]

===Modeling mesoscopic sequential self-assembly===

One of the reasons for the huge success of microelectronics is the ability to produce very large amounts of devices at very small price. Anyway, a large part of the final price of electronic devices is due to assembly and packaging issues. The standard procedure to package microdevices is by robotic or even manual manipulation, which while satisfactory for large sizes becomes inefficient and even practically incontrollable below the millimeter scale. Moreover, when dealing with very large amounts of components the task becomes time-consuming and this expensive.

Self-assembly techniques have the potential to boost electronic assembly by their intrinsic massive parallelism and advantageous scaling properties. Particularly, self-assembly performed in liquid environment has gained momentum by showing interesting performance. Anyway, the analytic modeling of the dynamics of this process is still limited and not capable of capturing the details of the stochastic dynamics of self-assembly. In this project, I want to simulate the dynamics 2D and 3D sequential self-assembly with agent-based models. This framework, never so far applied to this task, may help sheding light on the role of important parameters of the process such as dimensions of the assembly space, redundance of components, viscosity of the fluid carrier.

[[Massimo Mastrangeli]]

===Percolation-like phenomenon in the Google search engine===

In a nutshell:
Type a (short) random string of letters in Google. This mimics the effect of mispelling words, 'typos'. Surprisingly, you will find a non-null amount of results: the probability of finding such a word, even if it's a random string without a semantic meaning, is non-null, since (i) someone could have already 'invented it' (acronym or so), (ii) someone could have mispelled a word (committed a typographic error) in his/her website/blog etc. But repeat the procedure with larger strings, and look how the number of results rapidly drops to zero... Is this a phase transition? Can we characterize such phenomenon? What are the relations between language-like properties and this behavior? What information can we extract? In this project we will endeavor such questions, programming automatic queries to google of randomly-generated strings and relating the system's behavior to some collective phenomena such as Percolation.

Members:
* [[Jacopo Tagliabue]]
* [[Andrew Berdahl]]
* [[Lucas Lacasa]]

===Competitive spatial network growth===
Many large-scale aggregate networks are actually composed of several essentially independent subnetworks, each of which takes into account the other agents' actions. While traditional optimization methods yield insight into the most efficient network structures to satisfy a fixed objective, the presence of several overlapping and evolving networks may change the optimal strategy or create niches for otherwise suboptimal strategies. In this project we develop an agent-based network growth model to simulate competitive airline network growth, studying the effects of the demand distribution, entry time, and number of agents on the success and network structure of the agents.

[[Interacting distribution networks]]

Members:
* [[Brendan Colloran]]
* [[Caroline Farrior]]
* [[Daniel Wuellner]]
* [[Michael Schultz]]

===Spectral clustering of gene expression===

1. Can we differentiate between genes involved in separate biological functions (ie pathways) using spectral clustering?

2. If so, can we use this method to detect the genes activated in cancer?

Members:
* [[Rosemary Braun]]
* [[Corinne Teeter]]
* [[Elliot Martin]]
* [[Eric Kasper]]

===From Topology to Response===
[[From_Topology_to_Response]]

Members:
* [[Marek Kwiatkowski]]
* [[Rosemary Braun]]
* [[Alexander Mikheyev | Sasha Mikheyev]]

===Foraging on the move===
Many animals (e.g. caribou, wildebeest) forage in groups while moving from one location to another. This means individuals have to simultaneously balance several demands: finding the best resources, maintaining the cohesion of the group, and migration in a certain direction. While there is a vast literature on both flocking and optimal foraging, there has been no work done to understand how animals should trade off the decision to flock or forage (since it is difficult to do both simultaneously) during migration. We develop an individual-based model to address this, and implement a genetic algorithm to find the best decision-rule for switching between foraging and flocking, under a variety of conditions.

Page: [[Foraging on the move]]

Members:
* [[Allison Shaw]]
* [[Andrew Berdahl]]
* [[Kathrine Behrman|Kate Behrman]]
* [[Liliana Salvador]]
* [[Steven Lade]]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Members:
* [[Steven Lade]]
* [[Karen Simpson]]
* [[Almut Brunner]]

Update and own page following soon!

===Creative Process===
The project attempts to model the generation of ideas in the subconscious as a random combination of existing concepts (reflected as words) and their selection (reflected as variance). The selection filter determines the quality and quantity of ideas that rise to the conscious. Although the complete model may not be in place by the end of the week, the presentation will display a basic version of the final (and hopefully publishable) paper.

* [[Murad Mithani]]

===A Markov Model of Elite Factionalization===

Authoritarian regimes fracture when elites within the ruling coalition, which buttresses the dictator, defect. Consequently, regime change crucially depends on elite competition and coordination. Previous work has explored this topic through conventional formal models that make exacting informational and cognitive demands on agents. In contrast, this model will attempt to replicate these findings, while exploring additional dynamics and emergent behavior, by embedding boundedly rational agents in a Markovian system. Rather than assume hyper rational actors, capable of solving difficult dynamic programming problems, I assume that elites use relatively simple heuristics to navigate a stochastic environment.

* [[Trevor Johnston]]
===Radicalization Mechanism, Terrorist Networks and Reactive Control Theoretical Approach===
See [[Radicalization]]
Group Members:
[[Alhaji Cherif]]

[[Hirotoshi Yoshioka]]

[[Prasanta Bose]]

[[Wei Ni]]

Modeling behaviors student&teacher

2009-06-26T01:37:05Z

Acherif: /* Model Thoughts */

==Introduction==

=== Background ===
In the project, we study the impact of teacher's quality (in term of qualification (proxy by salary), and sets of activities s/he organizes) on minority/marginalized students. Under Chinese elite educational system, there is a large group of marginalized high school students who have been ignored due to the emphasis on exam-oriented results. As a result, teachers have primarily paid more attention to good students since number of good students produced by a teacher is used as a promotion marker which is in turn used as a salary increase index. This practice has unattended consequences on various socio-economic factors, drop-out rates and crime rates.

=== Objectives ===
The study was motivated by previous empirical investigations on two secondary vocational schools in China. From this study, it was observed that teachers’ attitudes and behaviors (proxy for various activities they organized) are significant factors that motivate these students to tuned down their personalities and behaviors (note: these students are troubled students). It was thus recommended that improvement of students’ personality and behaviors should served as the prime index in teacher’s salary. Only in this matter can teachers be motivated to devote ample of time in exploring various educational methods on these students. According to the previous research and data analysis from the questionnaires, we will model the bidirectional interactions between students and teachers in NetLogo to investigate some of the assumptions made in the above statements.

=== Members ===

* [[Chang Yu]]
* [[Alhaji Cherif]]

==Modeling==
=== Model Thoughts ===
* Current situation:

Graduation rate is one of the critical index in teacher’s salary. Teachers don’t concern inferior students. Thus the decreasing numbers of these students’ properties have no link to teacher’s salary.

*Suggestion:

Set students’ normalized personality and behaviors as prime index in teacher’s salary. Thus the properties of these students have strong links to teachers’ salary.

*Initial condition of student:
**Four properties: learningSkill, acceptanceToTeacher, normalizedCharacter, normalizedBehavior.
**Every property is decreasing when the model starts.

*Initial condition of teacher:
**One property : Salary.
**Salary equals 1000.
**actions: Encourage sutents.Organize activities

*Condition: make conection between teacher's salary and students' performance or not.We can use switch.
**if switch-off: Student’s properties decrease continually -------- Teacher dose nothing and his salary keeps steadily.
**if switch-on : Every student property is decreasing in every time step.(This is initial condition).If sum of four student properties < a(We can define 'a' in the model), then teacher’s salary starts to subtract 50 in every time step. And If salary < radom s1, (400 < s1 < 600), teacher will take “actions”.

Teacher takes action1——encourage students.

77.83% of students make progress on two properties: learningSkill and acceptanceToTeacher.These two two properties will increase in every step.

Teacher takes action2 ——organize social activies.

53.2% of students change better on two properties: normalizedCharacter+1 and normalizedBehavior.These two two properties will increase in every step.

Teacher takes action1 and action2——encourage students and organize social activies.
*Feedback loop:

If sum of four student properties > b, then teacher’s salary starts to plus 50 in evert time step.
If salary>random s2, (1500<s2<2000,s2 is the threshold of salary satisfaction), the contact between teacher and students will be more frequent and their links will be thicker.

=== Modeling Working===
[[Image:Student&Teacher Demo.jpg|200px|thumb|right|]]

We are thinking to use the structure of small world and model the behaviors between one teacher and 40-60 students.We put one teacher in the center and students around this teacher. Use sliders to control the weight and pramaters and see the changes of links between teacher and students.

==Original Discussion==
[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !

[[Alhaji Cherif]]:Interesting idea, you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know, I know some of the authors of the two papers.

[[Image:student&teacher-Chang.jpg|200px|thumb|left|]]

[[Chang Yu]]:Thank u Alhaji! I really appreciate if you could tell me how to get the papers. Have you done any research in this area? We should talk about it! Thanks.

[[Chang Yu]]:I think this picture could be a better way to explain this project.
==Original Data==

This research in China includes questionnaire survey for 200 students. The questionnaire has two pages. First page is monomial/ multiple Choices and second page is scale-measure topics. We use Frequency Analysis, Tests of Between-Subjects Effects and Multiple Linear Regression Analysis. I translate these questions and data analysis which are related with teachers.
[[Image:Data statistic1 Chang.jpg|200px|thumb|left|]][[Image:Data statistic2 Chang.jpg|200px|thumb|left|]]
[http://www.santafe.edu/events/workshops/index.php/Image:Questionnare_Chang.pdf Questionnaire and Analysis].

Modeling behaviors student&teacher

2009-06-26T01:35:22Z

Acherif:

==Introduction==

=== Background ===
In the project, we study the impact of teacher's quality (in term of qualification (proxy by salary), and sets of activities s/he organizes) on minority/marginalized students. Under Chinese elite educational system, there is a large group of marginalized high school students who have been ignored due to the emphasis on exam-oriented results. As a result, teachers have primarily paid more attention to good students since number of good students produced by a teacher is used as a promotion marker which is in turn used as a salary increase index. This practice has unattended consequences on various socio-economic factors, drop-out rates and crime rates.

=== Objectives ===
The study was motivated by previous empirical investigations on two secondary vocational schools in China. From this study, it was observed that teachers’ attitudes and behaviors (proxy for various activities they organized) are significant factors that motivate these students to tuned down their personalities and behaviors (note: these students are troubled students). It was thus recommended that improvement of students’ personality and behaviors should served as the prime index in teacher’s salary. Only in this matter can teachers be motivated to devote ample of time in exploring various educational methods on these students. According to the previous research and data analysis from the questionnaires, we will model the bidirectional interactions between students and teachers in NetLogo to investigate some of the assumptions made in the above statements.

=== Members ===

* [[Chang Yu]]
* [[Alhaji Cherif]]

==Modeling==
=== Model Thoughts ===
* Current situation:

Promotion rate to colleges is the critical index in teacher’s salary. Teachers don’t concern inferior students. Thus the decreasing numbers of these students’ properties have no link to teacher’s salary.

*Suggestion:

Set students’ normalized personality and behaviors as prime index in teacher’s salary. Thus the properties of these students have strong links to teachers’ salary.

*Initial condition of student:
**Four properties: learningSkill, acceptanceToTeacher, normalizedCharacter, normalizedBehavior.
**Every property is decreasing when the model starts.

*Initial condition of teacher:
**One property : Salary.
**Salary equals 1000.
**actions: Encourage sutents.Organize activities

*Condition: make conection between teacher's salary and students' performance or not.We can use switch.
**if switch-off: Student’s properties decrease continually -------- Teacher dose nothing and his salary keeps steadily.
**if switch-on : Every student property is decreasing in every time step.(This is initial condition).If sum of four student properties < a(We can define 'a' in the model), then teacher’s salary starts to subtract 50 in every time step. And If salary < radom s1, (400 < s1 < 600), teacher will take “actions”.

Teacher takes action1——encourage students.

77.83% of students make progress on two properties: learningSkill and acceptanceToTeacher.These two two properties will increase in every step.

Teacher takes action2 ——organize social activies.

53.2% of students change better on two properties: normalizedCharacter+1 and normalizedBehavior.These two two properties will increase in every step.

Teacher takes action1 and action2——encourage students and organize social activies.
*Feedback loop:

If sum of four student properties > b, then teacher’s salary starts to plus 50 in evert time step.
If salary>random s2, (1500<s2<2000,s2 is the threshold of salary satisfaction), the contact between teacher and students will be more frequent and their links will be thicker.
=== Modeling Working===
[[Image:Student&Teacher Demo.jpg|200px|thumb|right|]]

We are thinking to use the structure of small world and model the behaviors between one teacher and 40-60 students.We put one teacher in the center and students around this teacher. Use sliders to control the weight and pramaters and see the changes of links between teacher and students.

==Original Discussion==
[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !

[[Alhaji Cherif]]:Interesting idea, you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know, I know some of the authors of the two papers.

[[Image:student&teacher-Chang.jpg|200px|thumb|left|]]

[[Chang Yu]]:Thank u Alhaji! I really appreciate if you could tell me how to get the papers. Have you done any research in this area? We should talk about it! Thanks.

[[Chang Yu]]:I think this picture could be a better way to explain this project.
==Original Data==

This research in China includes questionnaire survey for 200 students. The questionnaire has two pages. First page is monomial/ multiple Choices and second page is scale-measure topics. We use Frequency Analysis, Tests of Between-Subjects Effects and Multiple Linear Regression Analysis. I translate these questions and data analysis which are related with teachers.
[[Image:Data statistic1 Chang.jpg|200px|thumb|left|]][[Image:Data statistic2 Chang.jpg|200px|thumb|left|]]
[http://www.santafe.edu/events/workshops/index.php/Image:Questionnare_Chang.pdf Questionnaire and Analysis].

CSSS 2009 Santa Fe-Readings

2009-06-23T00:25:29Z

Acherif: /* Liz Bradley: Introduction to Nonlinear Dynamics */

{{CSSS 2009 Santa Fe}}

----
===Liz Bradley: Introduction to Nonlinear Dynamics===
* [[Media:CSSS-Bradley-Syllabus.pdf|Syllabus]]
* [[Media:Ode.pdf|Numerical Solution of Differential Equations: Notes for CSCI3656]]
* [[Media:Ida.pdf|Time Series Analysis]]
* [[Media:CSSS-08-L1.pdf|Slides for Lecture 1 (2008)]]
* [[Media:CSSS-08-L2.pdf|Slides for Lecture 2 (2008)]]
* [[Media:CSSS-08-L3.pdf|Slides for Lecture 3 (2008)]]
* [[Media:CSSS-08-L4.pdf|Slides for Lecture 4 (2008)]]
'''CSSS2009'''
* [[Media:CSSS09_syllabus.pdf|Syllabus]]
* [[Media:CSSS09_BradleyLecture1.pdf|Intro to Nonlinear Dynamics: Maps]]
* [[Media:CSSS09_BradleyLecture2.pdf|Intro to Nonlinear Dynamics: Flows]]
* [[Media:BradleyLecture3.pdf|Intro to Nonlinear Dynamics: Methods for Solving]]
* [[Media:BradleyLecture4.pdf|Intro to Nonlinear Dynamics: Application and Examples]]

[http://www.cs.colorado.edu/~lizb/na/ode-notes.pdf Notes 1] 
[http://www.cs.colorado.edu/~lizb/papers/ida-chapter.pdf Notes 2]

FRACTAL Villages
[http://www.youtube.com/watch?v=7n36qV4Lk94]

===Tom Carter===
Here is a link to a page with various background readings -- I'll be talking about some of this material, watch the wiki for days/times
* [http://csustan.csustan.edu/~tom/SFI-CSSS/index.html Summer School readings.]
----
===Owen Densmore and Stephen Guerin===
For the [http://backspaces.net/wiki/NetLogo_Tutorial NetLogo Tutorial], please read the first page and follow the instructions for downloading and installing the software. Then run at least one of the File > Models Library example models .. both for the fun of it and to make sure your software download works!
----

===Peter Dodds: Networks===

Please see Peter's website for readings as well as notes and supplemental material. 
[http://www.uvm.edu/~pdodds/teaching/courses/2009-06SFI-networks/index.html '''Peter's Complex Systems Summer School 2009 Webpage''']

===Olaf Sporns: Neuroscience===
* [[Media:Core_sm.pdf|Mapping The Structural Core of the Human Cerebral Cortex]]
* [[Media:Networks_nrn.pdf|Complex Brain Networks]]
* [[Media:PLOS_TE.pdf|Mapping Information Flow in Sensorimotor Networks]]

* [[Media:SFI2.pdf|Slides for Lecture 2]]
* [[MEdia:SFI3.pdf|Slides for Lecture 3]]
----
===Doug Erwin: The History of Life and the Construction of Biodiversity===

My talks will focus on various aspects of the construction of biodiversity, with the overall theme being how much we do not yet know about this problem, despite its obvious importance. The first lecture will provide an overview of the history of life, the nature of the fossil record, and the various aspects of diversity. In the second talk we will discuss a variety of different conceptual models to understand the growth of diversity, as well as their problems. There will be a number of possibilities for projects here. The third talk will focus on the Cambrian radiation of animals (about 570-510 million years ago) and particularly on the role of changes in developmental gene regulation. Some of the issues that I raise in these lectures will be explored in more detail by others later in the week.

This paper discusses the last common bilaterian ancestor: [[Media:Erwin and Davidson 2002.pdf]]

This paper discusses the nature of change in gene regulatory networks: [[Media:Erwin_and_Davidson_2009.pdf‎]]

This paper provides further background on morphologic disparity: [[Media:Erwin_2007_disparity.pdf‎]]

Here is a powerpoint of the talk: [[Media:Erwin_Powerpoint.pdf]]
----

===Adaptive Modeling in Social Science (Nathan Collins)===

Please have a look at these [http://www.santafe.edu/~nac/CSSS/Adaptive.pdf notes] and the related [http://www.santafe.edu/~nac/CSSS/FigsCSSS.pdf figures].

Papers you should at least have a look at prior to the lectures are marked with *.

====Reinforcement learning and related approaches:====

Colin Camerer and Teck Ho, Experience-weighted attraction (EWA) learning in normal-form games," Econometrica, 67, July 1999, 827-874.

Camerer, Ho, and Chong, [http://www.hss.caltech.edu/~camerer/fewaRES.PDF Function EWA: A one-parameter model of learning in games].*

Nathan Collins, [http://www.santafe.edu/~nac/Papers/GameLearning.pdf Risk Learning].*

Sutton and Barto, [http://www.cs.ualberta.ca/%7Esutton/book/ebook/the-book.html Reinforcement Learning]. An extensive introduction to reinforcement learning methods. I will cover an infinitesimal portion of this material.

====Aspiration-based models:====

Jonathan Bendor, Daniel Diermeier, and Michael Ting, [http://gsbapps.stanford.edu/researchpapers/library/rp1627.pdf A Behavioral Model of Turnout]. (This is an older, working-paper version. The published version is available at jstor.org.)

Nathan Collins, Sunil Kumar, and Jonathan Bendor, The Adaptive Dynamics of Turnout, Journal of Politics 71(2), April 2009, 457-472.
(You will get a hard copy of this on Monday.)*

[http://www.santafe.edu/~nac/CSSS/turnout-supp.pdf Supplementary material for this paper.]

====Categorization-based models (which we may or may not get to):====

Love, Medin, and Gureckis, [http://love.psy.utexas.edu/~love/papers/love_etal_2004.pdf SUSTAIN: a network model of category learning].

Collins, [http://www.santafe.edu/~nac/Papers/SpatialVoting4.pdf A Unified Model of Spatial Voting].*
----
===Scott Pauls: Partition Decoupling for Roll Call Data===

I will be discussing an application of statistical learning methods to roll call votes of the U.S. Congress. Here, I will post slides as well as supporting material.
====Papers====
*[http://www.pnas.org/content/105/52/20589.abstract Application to the equities market]
*[http://www.math.dartmouth.edu/~pauls/RollCallGeometry_final.pdf Application to roll call voting]

====Spatial models====
As a point of comparison, I discuss spatial models of voting, focusing on those of Poole and Rosenthal. You can find information about this method (as well as results and tons of data) at [http://voteview.com Keith Poole's website].

====Slides====
*[http://www.math.dartmouth.edu/~pauls/rollcall-SFI.pptx Powerpoint version]
*[http://www.math.dartmouth.edu/~pauls/rollcall-SFI.pdf PDF version]
*[http://www.math.dartmouth.edu/~pauls/rollcall-SFI.zip Recording of the lecture (flash)]

====Matlab Code====
In the talk, I reference a bunch of matlab code and provide copies of some of the routines. The entire PDM matlab package is [http://www.math.dartmouth.edu/~pauls/PDMToolkit.zip here]. Note: this link was broken but I've fixed it now.

----

===Willemien Kets: Social Systems: Complexity, Reasoning and Beliefs===

Many social systems are complex: they consist of a large number of interacting agents who adapt to their environment, and feature both positive and negative feedbacks. This suggests that social systems can be analyzed with similar techniques as complex systems in e.g. physics. Indeed, some models based on models for physical systems have been successful at explaining some features of social systems, thus providing new insights. In particular, these models are well-suited to investigate how behavior at the micro-scale leads to aggregate patterns of behavior. However, social systems also differ from other complex systems in important ways. A key feature of social systems that its constituents--individuals--can reason and form beliefs about
the system and each other. This means that individuals can interact strategically, i.e., they behave in a way that they believe will benefit them most, given others' behavior. It also means that individuals---even the fully rational individual assumed in game theory and economics---can be wrong in their beliefs. This may give rise to types of behavior that are not
present in physical and other complex systems. In the first talk, I discuss a model to analyze a simple game called the minority game that builds on insights from physics and illustrate how such models can shed light on different types of macro-scale behavior and how they may even teach us something about the emergence of heuristics. In the second talk, I take a closer look at the relevant types of micro-behavior in social systems. Only if we have a thorough understanding of the behavior of individuals at micro-scale can we investigate how this microbehavior influences aggregate performance of social systems. I introduce a formal framework to describe individuals' reasoning processes and beliefs. This allows us to explore the conditions under which reasoning processes and belief formation have a large impact on behavior. It also allows us to analyze the implications of bounded rationality in a systematic manner. Ultimately, the goal is to use this detailed understanding of micro-behavior to understand the aggregate behavior of social systems.

====Papers====

* Kets, W. (2007), [http://www.santafe.edu/~willemien.kets/SMG.pdf The minority game: An economics perspective]

* Brandenburger, A. (1999), [http://www.santafe.edu/~willemien.kets/Brandenburger_PoP.pdf The power of paradox: Some recent developments in interactive epistemology], International Journal of Game Theory

* [http://www.santafe.edu/~willemien.kets/CSSS_2009-0620.pdf Slides]

----

===Jessica Flack: Regulation===

Please review the following papers before lectures on Monday and Tuesday.

* [[Media:AyFlackKrakauer2007.pdf|Robustness and Complexity in Animal Communication]]
* [[Media:ErrorAttack.pdf|Error and Attack Tolerance of Complex Networks]]
* [[Media:Flackniche.pdf|Policing and Niche Construction]]
* [[Media:Flacknichesupplementary.pdf|Policing and Niche Construction Supplementary]]
* [[Media:Ihmel.pdf|Backup without Redundency]]

===Jennifer Dunne: Ecological Network Structures===
* [[Media:09-CSSS_Structure.pdf‎ | Slides]]

Radicalization

2009-06-21T05:30:08Z

Acherif: /* Members */

==Members==
[[Alhaji Cherif]]

[[Hirotoshi Yoshioka ]]

[[Prasanta Bose]]

[[Wei Ni]]

This wiki only has the summary of our intention, interested individual should talk to any of the group members

==Background==
Current US counter-terrorism efforts have either scattered, killed or captured Al-Qaeda's core leadership, reducing the threat from its central core operatives, foot-soldiers and leaders. However, the Jihad-Salafism continues to spread at an exponential rate across various locales, as a result creating subcultures within vulnerable Islamic Diaspora communities. The threat from radicalized Salafist-Jihadists has evolved and has become diasporic (e.g.: Madrid 2004, Amsterdam Hofstad group, London 2005, Toronto 18 Case and Australia's Operation Pendennis) in nature.

The aim of this project is two-fold:

[1] Radicalization process: Recent modeling efforts have focus on strategic measures of controlling terrorism, few have focus primarily on the horizontal process of fanaticism. However, these models fail to incorporate various dynamics (oblique and vertical process of fanaticism). In our project, we model the process of radicalization that includes ideological transmission with differential recruitments.

[2] Control Mechanism: In this project, we hope to develop new kind of control mechanism we have called reactive control. Usually, application of control theory requires one to know the equations representing the system of interest. However, most real world problems, at least the interesting one, do not have any concrete equations. In order to circumvent this problem, we hope to develop a coarse-grained control theory that adaptively adjusts to the mechanism of interests.

The methods we hope to develop and apply are general and can be applied to various disciplines and applications (fads, contagion, control of disease, implementation of robust policy, etc....)

Radicalization

2009-06-21T05:29:19Z

Acherif: /* Members */

==Members==
[[Alhaji Cherif]]

[[Hirotoshi Yoshioka ]]

[[Prasanta Bose]]

[[Wei Ni]]

We only summarize our intention in this wiki, interested individual should talk to any of the group members

==Background==
Current US counter-terrorism efforts have either scattered, killed or captured Al-Qaeda's core leadership, reducing the threat from its central core operatives, foot-soldiers and leaders. However, the Jihad-Salafism continues to spread at an exponential rate across various locales, as a result creating subcultures within vulnerable Islamic Diaspora communities. The threat from radicalized Salafist-Jihadists has evolved and has become diasporic (e.g.: Madrid 2004, Amsterdam Hofstad group, London 2005, Toronto 18 Case and Australia's Operation Pendennis) in nature.

The aim of this project is two-fold:

[1] Radicalization process: Recent modeling efforts have focus on strategic measures of controlling terrorism, few have focus primarily on the horizontal process of fanaticism. However, these models fail to incorporate various dynamics (oblique and vertical process of fanaticism). In our project, we model the process of radicalization that includes ideological transmission with differential recruitments.

[2] Control Mechanism: In this project, we hope to develop new kind of control mechanism we have called reactive control. Usually, application of control theory requires one to know the equations representing the system of interest. However, most real world problems, at least the interesting one, do not have any concrete equations. In order to circumvent this problem, we hope to develop a coarse-grained control theory that adaptively adjusts to the mechanism of interests.

The methods we hope to develop and apply are general and can be applied to various disciplines and applications (fads, contagion, control of disease, implementation of robust policy, etc....)

Radicalization

2009-06-21T04:38:01Z

Acherif: /* Background */

==Members==
[[Alhaji Cherif]]

[[Hirotoshi Yoshioka ]]

[[Prasanta Bose]]

[[Ni Wei]]

We only summarize our intention in this wiki, interested individual should talk to any of the group members
==Background==
Current US counter-terrorism efforts have either scattered, killed or captured Al-Qaeda's core leadership, reducing the threat from its central core operatives, foot-soldiers and leaders. However, the Jihad-Salafism continues to spread at an exponential rate across various locales, as a result creating subcultures within vulnerable Islamic Diaspora communities. The threat from radicalized Salafist-Jihadists has evolved and has become diasporic (e.g.: Madrid 2004, Amsterdam Hofstad group, London 2005, Toronto 18 Case and Australia's Operation Pendennis) in nature.

The aim of this project is two-fold:

[1] Radicalization process: Recent modeling efforts have focus on strategic measures of controlling terrorism, few have focus primarily on the horizontal process of fanaticism. However, these models fail to incorporate various dynamics (oblique and vertical process of fanaticism). In our project, we model the process of radicalization that includes ideological transmission with differential recruitments.

[2] Control Mechanism: In this project, we hope to develop new kind of control mechanism we have called reactive control. Usually, application of control theory requires one to know the equations representing the system of interest. However, most real world problems, at least the interesting one, do not have any concrete equations. In order to circumvent this problem, we hope to develop a coarse-grained control theory that adaptively adjusts to the mechanism of interests.

The methods we hope to develop and apply are general and can be applied to various disciplines and applications (fads, contagion, control of disease, implementation of robust policy, etc....)

Radicalization

2009-06-21T04:35:01Z

Acherif: /* Background */

==Members==
[[Alhaji Cherif]]

[[Hirotoshi Yoshioka ]]

[[Prasanta Bose]]

[[Ni Wei]]

We only summarize our intention in this wiki, interested individual should talk to any of the group members
==Background==
Current US counter-terrorism efforts have either scattered, killed or captured Al-Qaeda's core leadership, reducing the threat from its central core operatives, foot-soldiers and leaders. However, the Jihad-Salafism continues to spread at an exponential rate across various locales, as a result creating subcultures within vulnerable Islamic Diaspora communities. The threat from radicalized Salafist-Jihadists has evolved and has become diasporic (e.g.: Madrid 2004, Amsterdam Hofstad group, London 2005, Toronto 18 Case and Australia's Operation Pendennis) in nature.

The aim of this project is two-fold:

[1] Radicalization process: Recent modeling efforts have focus on strategic measures of controlling terrorism, few have focus primarily on the horizontal process of fanaticism. However, these models fail to incorporate various dynamics (oblique and vertical process of fanaticism). In our project, we model the process of radicalization that includes ideological transmission with differential recruitments.

[2] Control Mechanism: In this project, we hope to develop new kind of control mechanism we have called reactive control. Usually, application control theory requires one to know the equations representing the system of interest. However, most real world problems, at least the interesting one, do not have any concrete equations. In order to circumvent this problem, we hope to develop a coarse-grained control theory that adaptively adjusts to the mechanism of interests.

The methods we hope to develop and apply are general and can be applied to various disciplines and applications (fads, contagion, control of disease, implementation of robust policy, etc....)

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-21T04:31:48Z

Acherif: /* Radicalization of Islamic Diasporas and Reactive Control Theoretical Approach */

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

===Problem solving and mating - are they similar?===

The discussions on this project have been moved to a separate page: [[Problem solving]]

=== Interacting distribution networks ===
Moved to its own page: [[Interacting distribution networks]]

===Modeling Gossip Networks===
Moved to a separate page: [[Modeling gossip networks]]

===Radicalization of Islamic Diasporas and Reactive Control Theoretical Approach===
See [[Radicalization]]

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

====Acupuncture/Chinese Alternative Medicine====

Here are some more papers regarding research that has been done on acupuncture. Some network analysis has been done. Very interesting stuff!

[[Media: AcupunctureOverview.pdf|Acupuncture Overview]]: Here is an overview of acupuncture from a journal entitled "Alternative Therapies" in 1998.

[[Media: AcupunctureGraphTheory.pdf| Acupuncture and Graph Theory]]: This paper was written in "Progress in Natural Science" in 2009 which implements the use of graph theory to make a model to understand the effects of acupunture on brain function.

[[Media: AcupunctureFibroblasts.pdf|Body-Wide Cellular Network of Fibroblast Cells]]: A paper relating the study of a body-wide network of fibroblasts to acupuncture. Written in "Histochemistry and Cell Biology" in 2004.

[[Media: AcupunctureNeedleAdmin.pdf|Acupuncture-Psychosocial Context]] And another which studies the effects of the procedure. Written in "Advanced Access Publication" in 2008.

Enjoy! [[Karen Simpson]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

[[Mahyar Malekpour]]: Update June 16, 2009 - Someone asked if there is an application for this. The answer is yes, categorically, any self-organizing system needs synchronization. However, my interest here are visualization and exploration using agent-based tools. I don not intend to develop a solution to this problem, rather build on an existing agent-based model (if there is any) and enhance its capabilities.

[[Massimo Mastrangeli]]: as someone said, there is vast literature on synchronization available; you can for example get a taste in [http://www.amazon.com/SYNC-Emerging-Science-Spontaneous-Order/dp/0786868449 Sync] by Steven Strogatz (also, check out his talk [http://www.ted.com/talks/steven_strogatz_on_sync.html at TED]). I am quite interested in the idea.

===The Global Spread of Cricket===
No I'm not actually intending to study this particular topic. But there is one interesting article published in 2005 (Kaufman and Patterson, American Sociological Review) that examined why cricket continues to be popular in many British-influenced societies while it is not in the U.S. and Canada. This is interesting given the fact that cricket was very popular in the two countries and that the first official international cricket match took place between the two countries in the mid-19th century. So, not only how cultures, ideas, technologies, etc. diffuse across nations, populations, and so on, but also mechanisms that influence the retention after the initial adoption merit serious attention I think. One possible topic include is modern contraceptive use in developing countries. I guess modeling such mechanisms would require taking into account the models presented by Nathan Collins and Peter Dodds, in addition to signed networks (Doreian). One difficulty of modeling this kind of mechanism is that both structural and individual factors should be considered [[Hirotoshi Yoshioka]].

There is a nice article by H. Peyton Young ('Social Dynamics: Theory and Applications', Handbook of Computational Economics, Vol. II; you can download it at http://www.econ.jhu.edu/people/young/Publications.html) which investigates the evolution of norms or conventions in an agent-based modelling / evolutionary game theoretic setting (in our small library there is also a whole book about that by Peyton Young). It might be interesting to analyze the diverging popularity of cricket in those countries (which can be interpreted as a kind of convention) in this framework. [[Mareen Hofmann]]

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response]] project page. '''We still need a biologist.''' [[Marek Kwiatkowski]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

* [[Mauricio Gonzalez-Forero]]: I would like to hear more about this project. Can we meet sometime?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Modeling behaviors between students and teachers===

[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !

Interesting idea, you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know, I know some of the authors of the two papers. Alhaji Cherif

[[Image:student&teacher-Chang.jpg|200px|thumb|left|]]

[[Chang Yu]]:Thank u Alhaji! I really appreciate if you could tell me how to get the papers. Have you done any research in this area? We should talk about it! Thanks.

[[Chang Yu]]:I think this picture could be a better way to explain this project.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

*[[watson]]: hmmm ... yes. this is exactly what I am talking about. very cool to hear you have some experience with this Massimo. right now i am leaning on going ahead with this project. i think we have serious potential to make impact, elucidate new relationships and phenomena and educate in the process. and i think it could be a ton of fun. here are a couple of links i have found which could be of use:
**[http://fusionanomaly.net/polyrhythms.html this] page talks about history, theory and even mentions chaos.
**[http://web.mit.edu/cjoye/www/music/tabla/ this] is a good source for tabla samples. tabla is one of the simpler devices that has some melodic structure as well as rhythmic structure to it. we could work with others as well... one thought is even just a drum kit of different sounds (rock style).

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and has looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects, too. His papers might be a good starting point.

[[Nathan Hodas]] I'd like to be in on this. I've pondered a good deal about this since reading the book. Maybe we should contact Jared Diamond?

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

[[Margreth Keiler]]: Murad and I had the same idea yesterday, but we thought to make each week a surveys to see how the network change over time and to add also after CSSS surveys. Should we discuss our draft tomorrow at SFI?

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to
‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity
in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to
allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

===Economic Geography in the Lake Titicaca Basin===

Moved to [http://www.santafe.edu/events/workshops/index.php/Economic_Geography_and_State_Emergence Economic Geography and State Emergence]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

[[Image:Theoretical_framework.jpg|480px|thumb|Theoretical_framework]]

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Looking forward to exchanging ideas!

[[Steven Lade]] Hi Almut, As I've said, I think this is well suited to modelling with differential equations. Particularly if, as I assume to be the case, the GIS data comes already in a rectangular grid. Having said that, there are some complementary aspects for which ABM would be well-suited. For instance, following agents as they form streams, or if you were to have a localised thunderstorm. We could possibly do this in parallel and see if they match and/or use each method's particular advantages.

You may be interested in this paper, which I found through the SFI library database: [http://pubs.usgs.gov/sir/2007/5009/pdf/sir_2007-5009.pdf]. I think this one is more complicated though, because they need to consider a three-dimensional water table. More generally, what sort of modelling (if any) do people usually do in these sorts of topics?

[[Karen Simpson]] I am interested in this project! I have studied these concepts in many of my classes. Through past research, I've looked at storm/rain events, and how a large runoff from stormwater causes high contaminant concentrations in streams and rivers. This research was done for urban, forest, and agricultural landuse types.(I will try to find the results of this research soon). Another thing to think about is the time between rain events. A long timespan between rainfall events will cause the soil to become unsaturated, and the next rainfall may have little effect on the stream. I also will not be around much this weekend, so would it be possible to meet sometime tomorrow (Thursday 6/18)?

===Scalable (parallel) Spatial Agent-Based Models===

This project idea is an exploration of what happens to agent-based models “in the large?” For example,
* As the number of interacting agents in a model increases, what happens to the dynamics of the model?
* What happens as the size of the agents’ domain increases (e.g. simulating a neighborhood versus simulating a city or country)
* How do the properties of the model change? Are there scaling laws in effect ?

In order to investigate these issues, we need a scalable simulation, i.e. a parallel implementation of the model that allows us to introduce arbitrarily large numbers of agents. There are many approaches to doing this [lit review needed here!], but for this project, I would like to focus on spatial agent-based models: models where there are N agents who exist in a geographical domain and possess “vision,” where vision can be optical/eye-based, local communications (audible or electromagnetic line of site).
A couple such models which can serve as starting points include the flocking model (aka “boids”) and Epstein’s model of civil violence (or its derivative “Rebellion” model).

The idea is to decompose the spatial domain into independent subdomains, distribute those subdomains to nodes on a compute cluster, amalgamate the results, wash-rinse-repeat. One possible approach is to use an adaptive mesh refinement (AMR) such as those used by engineers for finite element analysis or by physicists in hydrodynamics simulations. One concrete example, using a quad-tree decomposition to keep agent density constant on each processor (and thereby keeping computational load balanced), is as follows:

[[Image:Particle.PNG|thumb|left|An example showing decomposition of a particle system using a quad-tree. Each resulting square has (roughly) the same number of particles in it. Can this approach be used for parallelizing spatial agent-based models ?]]
I have a cluster available for implementation, along with the MPI libraries for parallel programming. Other suggested areas of expertise that would greatly benefit the project include:
Someone interested in evaluating simulation results, who can help ensure that we don’t break the model by decomposing it.
Someone interested in analysis, for exploring the effects of scaling on the model.
Someone interested in high-performance computing, to help with programming (probably c/c++ with MPI)

From talking to folks in our class, some other benefits of the approach include
* improving running time for very-long-running simulations
* aerospace applications—decomposing the National Air Space into computationally tractable subdomains for modeling or real-world purposes.
* Applying the decomposition technique to other model domains. For example, can a similar technique be used to decompose a social network, especially if a single model has both geographic spatial domains and also network domains?

Other approaches suggested by classmates have included implementation on GPUs (graphics processors used for general purpose computation) and sticking to an SMP implementation (multicore workstations with shared memory--simpler implementation/perhaps not as scalable), versus a distributed-memory cluster. I welcome further ideas that might help kick-start this zany scheme.

[[Steven Lade]] What I'm about to say seems kind of obvious, and I'm not sure it helps you at all, but I can't help but say that if your 'averaged behaviour' converges for very large numbers of agents, you'd in effect be modelling some partial differential equation.

[[Matt McMahon]] Thanks, Steven. Not obvious to me though ... Can you elucidate?

==Final Projects==

Please place your final project ideas here: details should include clear and objective outlines.

Radicalization

2009-06-21T04:26:30Z

Acherif: /* Background */

==Members==
[[Alhaji Cherif]]

[[Hirotoshi Yoshioka ]]

[[Prasanta Bose]]

[[Ni Wei]]

We only summarize our intention in this wiki, interested individual should talk to any of the group members
==Background==
Current US counter-terrorism efforts have either scattered, killed or captured Al-Qaeda's core leadership, reducing the threat from its central core operatives, foot-soldiers and leaders. However, the Jihad-Salafism continues to spread at an exponential rate across various locales, as a result creating subcultures within vulnerable Islamic Diaspora communities. The threat from radicalized Salafist-Jihadists has evolved and has become diasporic (e.g.: Madrid 2004, Amsterdam Hofstad group, London 2005, Toronto 18 Case and Australia's Operation Pendennis) in nature.

The aim of this project is two-fold:

[1] Radicalization process: Recent modeling efforts have focus on strategic measures of controlling terrorism, few have focus primarily on the horizontal process of fanaticism. However, these models fail to incorporate various dynamics (oblique and vertical process of fanaticism). In our project, we model the process of radicalization that includes ideological transmission with differential recruitments.
[2] Control Mechanism: In this project, we hope to develop new kind of control mechanism we have called reactive control. Usually, application control theory requires one to know the equations representing the system of interest. However, most real world problems, at least the interesting one, do not have any concrete equations. In order to circumvent this problem, we hope to develop a coarse-grained control theory that adaptively adjusts to the mechanism of interests.

The methods we hope to develop and apply are general and can be applied to various disciplines and applications (fads, contagion, control of disease, implementation of robust policy, etc....)

Radicalization

2009-06-21T04:20:35Z

Acherif: /* Background */

==Members==
[[Alhaji Cherif]]

[[Hirotoshi Yoshioka ]]

[[Prasanta Bose]]

[[Ni Wei]]

We only summarize our intention in this wiki, interested individual should talk to any of the group members
==Background==
Current US counter-terrorism efforts have either scattered, killed or captured Al-Qaeda's core leadership, reducing the threat from its central core operatives, foot-soldiers and leaders. However, the Jihad-Salafism continues to spread at an exponential rate across various locales, as a result creating subcultures within vulnerable Islamic Diaspora communities. The aim of this project is two-fold:
[1] Radicalization process: Recent modeling efforts have focus on strategic measures of controlling terrorism, few have focus primarily on the horizontal process of fanaticism. However, these models fail to incorporate various dynamics (oblique and vertical process of fanaticism). In our project, we model the process of radicalization that includes ideological transmission with differential recruitments.
[2] Control Mechanism: In this project, we hope to develop new kind of control mechanism we have called reactive control. Usually, application control theory requires one to know the equations representing the system of interest. However, most real world problems, at least the interesting one, do not have any concrete equations. In order to circumvent this problem, we hope to develop a coarse-grained control theory that adaptively adjusts to the mechanism of interest.

Radicalization

2009-06-21T04:13:55Z

Acherif: New page: ==Members== Alhaji Cherif Hirotoshi Yoshioka Prasanta Bose Ni Wei ==Background== Current US counter-terrorism efforts have either scattered, killed or captured Al-Qaed...

==Members==
[[Alhaji Cherif]]

[[Hirotoshi Yoshioka ]]

[[Prasanta Bose]]

[[Ni Wei]]

==Background==
Current US counter-terrorism efforts have either scattered, killed or captured Al-Qaeda's core leadership, reducing the threat from its central core operatives, foot-soldiers and leaders. However, the Jihad-Salafism continues to spread at an exponential rate across various locales, as a result creating subcultures within vulnerable Islamic Diaspora communities. The aim of this project is two-fold:
[1] Radicalization process: Recent modeling efforts have focus on strategic measures of controlling terrorism, few have focus primarily on the horizontal process of fanaticism. However, these models fail to incorporate various dynamics (oblique and vertical process of fanaticism). In our project

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-21T04:00:50Z

Acherif:

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

===Problem solving and mating - are they similar?===

The discussions on this project have been moved to a separate page: [[Problem solving]]

=== Interacting distribution networks ===
Moved to its own page: [[Interacting distribution networks]]

===Modeling Gossip Networks===
Moved to a separate page: [[Modeling gossip networks]]

===Radicalization of Islamic Diasporas and Reactive Control Theoretical Approach===
Coming
==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

====Acupuncture/Chinese Alternative Medicine====

Here are some more papers regarding research that has been done on acupuncture. Some network analysis has been done. Very interesting stuff!

[[Media: AcupunctureOverview.pdf|Acupuncture Overview]]: Here is an overview of acupuncture from a journal entitled "Alternative Therapies" in 1998.

[[Media: AcupunctureGraphTheory.pdf| Acupuncture and Graph Theory]]: This paper was written in "Progress in Natural Science" in 2009 which implements the use of graph theory to make a model to understand the effects of acupunture on brain function.

[[Media: AcupunctureFibroblasts.pdf|Body-Wide Cellular Network of Fibroblast Cells]]: A paper relating the study of a body-wide network of fibroblasts to acupuncture. Written in "Histochemistry and Cell Biology" in 2004.

[[Media: AcupunctureNeedleAdmin.pdf|Acupuncture-Psychosocial Context]] And another which studies the effects of the procedure. Written in "Advanced Access Publication" in 2008.

Enjoy! [[Karen Simpson]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

[[Mahyar Malekpour]]: Update June 16, 2009 - Someone asked if there is an application for this. The answer is yes, categorically, any self-organizing system needs synchronization. However, my interest here are visualization and exploration using agent-based tools. I don not intend to develop a solution to this problem, rather build on an existing agent-based model (if there is any) and enhance its capabilities.

[[Massimo Mastrangeli]]: as someone said, there is vast literature on synchronization available; you can for example get a taste in [http://www.amazon.com/SYNC-Emerging-Science-Spontaneous-Order/dp/0786868449 Sync] by Steven Strogatz (also, check out his talk [http://www.ted.com/talks/steven_strogatz_on_sync.html at TED]). I am quite interested in the idea.

===The Global Spread of Cricket===
No I'm not actually intending to study this particular topic. But there is one interesting article published in 2005 (Kaufman and Patterson, American Sociological Review) that examined why cricket continues to be popular in many British-influenced societies while it is not in the U.S. and Canada. This is interesting given the fact that cricket was very popular in the two countries and that the first official international cricket match took place between the two countries in the mid-19th century. So, not only how cultures, ideas, technologies, etc. diffuse across nations, populations, and so on, but also mechanisms that influence the retention after the initial adoption merit serious attention I think. One possible topic include is modern contraceptive use in developing countries. I guess modeling such mechanisms would require taking into account the models presented by Nathan Collins and Peter Dodds, in addition to signed networks (Doreian). One difficulty of modeling this kind of mechanism is that both structural and individual factors should be considered [[Hirotoshi Yoshioka]].

There is a nice article by H. Peyton Young ('Social Dynamics: Theory and Applications', Handbook of Computational Economics, Vol. II; you can download it at http://www.econ.jhu.edu/people/young/Publications.html) which investigates the evolution of norms or conventions in an agent-based modelling / evolutionary game theoretic setting (in our small library there is also a whole book about that by Peyton Young). It might be interesting to analyze the diverging popularity of cricket in those countries (which can be interpreted as a kind of convention) in this framework. [[Mareen Hofmann]]

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response]] project page. '''We still need a biologist.''' [[Marek Kwiatkowski]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

* [[Mauricio Gonzalez-Forero]]: I would like to hear more about this project. Can we meet sometime?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Modeling behaviors between students and teachers===

[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !

Interesting idea, you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know, I know some of the authors of the two papers. Alhaji Cherif

[[Image:student&teacher-Chang.jpg|200px|thumb|left|]]

[[Chang Yu]]:Thank u Alhaji! I really appreciate if you could tell me how to get the papers. Have you done any research in this area? We should talk about it! Thanks.

[[Chang Yu]]:I think this picture could be a better way to explain this project.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

*[[watson]]: hmmm ... yes. this is exactly what I am talking about. very cool to hear you have some experience with this Massimo. right now i am leaning on going ahead with this project. i think we have serious potential to make impact, elucidate new relationships and phenomena and educate in the process. and i think it could be a ton of fun. here are a couple of links i have found which could be of use:
**[http://fusionanomaly.net/polyrhythms.html this] page talks about history, theory and even mentions chaos.
**[http://web.mit.edu/cjoye/www/music/tabla/ this] is a good source for tabla samples. tabla is one of the simpler devices that has some melodic structure as well as rhythmic structure to it. we could work with others as well... one thought is even just a drum kit of different sounds (rock style).

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and has looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects, too. His papers might be a good starting point.

[[Nathan Hodas]] I'd like to be in on this. I've pondered a good deal about this since reading the book. Maybe we should contact Jared Diamond?

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

[[Margreth Keiler]]: Murad and I had the same idea yesterday, but we thought to make each week a surveys to see how the network change over time and to add also after CSSS surveys. Should we discuss our draft tomorrow at SFI?

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to
‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity
in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to
allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

===Economic Geography in the Lake Titicaca Basin===

Moved to [http://www.santafe.edu/events/workshops/index.php/Economic_Geography_and_State_Emergence Economic Geography and State Emergence]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

[[Image:Theoretical_framework.jpg|480px|thumb|Theoretical_framework]]

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Looking forward to exchanging ideas!

[[Steven Lade]] Hi Almut, As I've said, I think this is well suited to modelling with differential equations. Particularly if, as I assume to be the case, the GIS data comes already in a rectangular grid. Having said that, there are some complementary aspects for which ABM would be well-suited. For instance, following agents as they form streams, or if you were to have a localised thunderstorm. We could possibly do this in parallel and see if they match and/or use each method's particular advantages.

You may be interested in this paper, which I found through the SFI library database: [http://pubs.usgs.gov/sir/2007/5009/pdf/sir_2007-5009.pdf]. I think this one is more complicated though, because they need to consider a three-dimensional water table. More generally, what sort of modelling (if any) do people usually do in these sorts of topics?

[[Karen Simpson]] I am interested in this project! I have studied these concepts in many of my classes. Through past research, I've looked at storm/rain events, and how a large runoff from stormwater causes high contaminant concentrations in streams and rivers. This research was done for urban, forest, and agricultural landuse types.(I will try to find the results of this research soon). Another thing to think about is the time between rain events. A long timespan between rainfall events will cause the soil to become unsaturated, and the next rainfall may have little effect on the stream. I also will not be around much this weekend, so would it be possible to meet sometime tomorrow (Thursday 6/18)?

===Scalable (parallel) Spatial Agent-Based Models===

This project idea is an exploration of what happens to agent-based models “in the large?” For example,
* As the number of interacting agents in a model increases, what happens to the dynamics of the model?
* What happens as the size of the agents’ domain increases (e.g. simulating a neighborhood versus simulating a city or country)
* How do the properties of the model change? Are there scaling laws in effect ?

In order to investigate these issues, we need a scalable simulation, i.e. a parallel implementation of the model that allows us to introduce arbitrarily large numbers of agents. There are many approaches to doing this [lit review needed here!], but for this project, I would like to focus on spatial agent-based models: models where there are N agents who exist in a geographical domain and possess “vision,” where vision can be optical/eye-based, local communications (audible or electromagnetic line of site).
A couple such models which can serve as starting points include the flocking model (aka “boids”) and Epstein’s model of civil violence (or its derivative “Rebellion” model).

The idea is to decompose the spatial domain into independent subdomains, distribute those subdomains to nodes on a compute cluster, amalgamate the results, wash-rinse-repeat. One possible approach is to use an adaptive mesh refinement (AMR) such as those used by engineers for finite element analysis or by physicists in hydrodynamics simulations. One concrete example, using a quad-tree decomposition to keep agent density constant on each processor (and thereby keeping computational load balanced), is as follows:

[[Image:Particle.PNG|thumb|left|An example showing decomposition of a particle system using a quad-tree. Each resulting square has (roughly) the same number of particles in it. Can this approach be used for parallelizing spatial agent-based models ?]]
I have a cluster available for implementation, along with the MPI libraries for parallel programming. Other suggested areas of expertise that would greatly benefit the project include:
Someone interested in evaluating simulation results, who can help ensure that we don’t break the model by decomposing it.
Someone interested in analysis, for exploring the effects of scaling on the model.
Someone interested in high-performance computing, to help with programming (probably c/c++ with MPI)

From talking to folks in our class, some other benefits of the approach include
* improving running time for very-long-running simulations
* aerospace applications—decomposing the National Air Space into computationally tractable subdomains for modeling or real-world purposes.
* Applying the decomposition technique to other model domains. For example, can a similar technique be used to decompose a social network, especially if a single model has both geographic spatial domains and also network domains?

Other approaches suggested by classmates have included implementation on GPUs (graphics processors used for general purpose computation) and sticking to an SMP implementation (multicore workstations with shared memory--simpler implementation/perhaps not as scalable), versus a distributed-memory cluster. I welcome further ideas that might help kick-start this zany scheme.

[[Steven Lade]] What I'm about to say seems kind of obvious, and I'm not sure it helps you at all, but I can't help but say that if your 'averaged behaviour' converges for very large numbers of agents, you'd in effect be modelling some partial differential equation.

[[Matt McMahon]] Thanks, Steven. Not obvious to me though ... Can you elucidate?

==Final Projects==

Please place your final project ideas here: details should include clear and objective outlines.

Alfred Hubler's Nonlinear Dynamics Lab

2009-06-19T07:06:23Z

Acherif: /* Friday June 19 7:00 p.m. */

{{CSSS 2009 Santa Fe}}

Alfred will be hosting a lab where students get to play around with a few concepts from nonlinearity and complexity science.

Class size is limited to 15 students.

===Tuesday June 16, 7:00 p.m.===
1 [[Erin Taylor]] 
2 [[Roozbeh Daneshvar]] 
3 [[Allison Shaw]] 
4 [[Matt_McMahon]] 
5 [[Nathan Hodas]] 
6 [[Steven Lade]] 
7 [[Daniel Wuellner]] 
8 [[Karen Simpson]] 
9 [[Mauricio Gonzalez-Forero]] 
10 [[Margreth Keiler]] 
11 [[Chang Yu]] 
12 [[Hirotoshi Yoshioka]] 
13 [[Joslyn Barnhart]] 
14 [[Massimo Mastrangeli]] 
15 [[Varsha Kulkarni]] 

===Wednesday June 17, 8:30 a.m.===
1 [[Rosemary Braun]] 
2 [[Liliana Salvador]] 
3 [[Michael Schultz]] 
4 [[Dave Brooks]] 
5 [[Jacopo Tagliabue]] 
6 [[Lucas Lacasa]] 
7 [[Mareen Hofmann]] 
8 [[Guimei Zhu]] 
9 [[Andrew Noble]] 
10[[Wendy Ham]] 
11 [[Wei Ni]] 
12 [[Alexander Mikheyev]] 
13 [[Barbara Bauer]] 
14 [[Brian Hollar]] 
15 [[watson]] 
16 [[bose]] 

===Thursday June 18 7:00 p.m.===
1 Caroline Farrior 
2 Kate Behrman 
3 Martin Schmidt 
4 [[Almut Brunner]] 
5 [[Milena Tsvetkova]] 
6 [[Jennifer Terpstra]] 
7 
8 
9 [[Elliot Martin]] 
10 
11[[Mahyar Malekpour]] 
12 [[Andrew Berdahl]] 
13 [[Damian Winters]] 
14 [[Corinne Teeter]] 
15 [[Trevor Johnston]] 

===Friday June 19 7:00 p.m.===
1 [[Murad Mithani]] 
2 [[Lara Danilova-Burdess]] 
3 Brendan Colloran 
4 [[David Jones]] 
5 [[Eric Kasper]] 
6 [[Jeremy Barofsky]] 
7 [[Angela Onslow]] 
8 [[Casey Helgeson]] 
9 [[Sean Brocklebank]] 
10 [[Marek Kwiatkowski]] 
11 [[Gustavo Lacerda]] 
12 [[Almut Brunner]] 
13 [[Alhaji Cherif]] 
14 
15

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-17T03:35:36Z

Acherif: /* Modeling behaviors between students and teachers */

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

[[Mahyar Malekpour]]: Update June 16, 2009 - Someone asked if there is an application for this. The answer is yes, categorically, any self-organizing system needs synchronization. However, my interest here are visualization and exploration using agent-based tools. I don not intend to develop a solution to this problem, rather build on an existing agent-based model (if there is any) and enhance its capabilities.

===The Global Spread of Cricket===
No I'm not actually intending to study this particular topic. But there is one interesting article published in 2005 (Kaufman and Patterson, American Sociological Review) that examined why cricket continues to be popular in many British-influenced societies while it is not in the U.S. and Canada. This is interesting given the fact that cricket was very popular in the two countries and that the first official international cricket match took place between the two countries in the mid-19th century. So, not only how cultures, ideas, technologies, etc. diffuse across nations, populations, and so on, but also mechanisms that influence the retention after the initial adoption merit serious attention I think. One possible topic include is modern contraceptive use in developing countries. I guess modeling such mechanisms would require taking into account the models presented by Nathan Collins and Peter Dodds, in addition to signed networks (Doreian). One difficulty of modeling this kind of mechanism is that both structural and individual factors should be considered [[Hirotoshi Yoshioka]].

There is a nice article by H. Peyton Young ('Social Dynamics: Theory and Applications', Handbook of Computational Economics, Vol. II; you can download it at http://www.econ.jhu.edu/people/young/Publications.html) which investigates the evolution of norms or conventions in an agent-based modelling / evolutionary game theoretic setting (in our small library there is also a whole book about that by Peyton Young). It might be interesting to analyze the diverging popularity of cricket in those countries (which can be interpreted as a kind of convention) in this framework. [[Mareen Hofmann]]

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response]] project page. '''We still need a biologist.''' [[Marek Kwiatkowski]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Problem solving and mating - are they similar?===
I was intrigued by Tom's model of mating and began to wonder whether we can think of problem solving in a similar way. If we were to model problem solving, how would we do it? I'd like to think that problems and solutions are components that combine to generate an emergent property. (After a problem meets a solution--or a solution meets a problem--something new is allowed to emerge. While one instance of problem solving does not exactly create a complex system, many instances may.) That said, there are several questions/considerations to think about before/while we create a proper model of problem solving:

* Given a population of information/knowledge, how can we identify what are problems and what are solutions?
* Actually, which comes first: knowledge, information, problems, or solutions?
* What are some important dimensions of problems and solutions? (These dimensions should inform some kind of a matching probability for problems and solutions.)
* What is the difference between problems and solutions anyway?
* What makes certain kinds of problems and solutions "hang out" in a cluster or neighboring clusters? Is this primarily due to path-dependence?
* When there is a difficult problem (tentatively defined as a problem for which there is no nearby solutions), how can we tell which clusters have the greatest probability of containing the solution(s)? (Can some of the network stuff we learned be of help here?)
* It is of course important to remember that a problem can have many solutions, and a solution can solve many problems, but that they may have different degrees of affinity (just like a ligand-receptor interaction in molecular biology). Also, occasionally a problem needs a combination of several solutions ("AND" as opposed to "OR").

I would love to hear your thoughts and comments, and I'm hoping that someone may actually share some of my interests in figuring out the answers to the questions above! [[Wendy Ham]]

[[Murad Mithani]]: We can look at problem solving as a special case of idea generation. See if you find any parallels between what you have in mind to what is written in the creative process.

[[David Brooks]]: This matching of past solutions or components to new problems leads to several interesting topics of discussion: (1) Shouldn't the process of developing a solution path be treated as a potentially complex system, (2) How do we describe the process without providing a falsely formulaic structure (3) When is the problem, the set of goals, and the process considered to be identified and what elements of the description may hint to the fragility of understanding? I have quite a bit of experience researching and addressing these issues and can help if this becomes a project.

[[Image:Bjh_singles_map.png|250px|right]]'''[[Brian Hollar]]:''' I've been doing some research for my dissertation on '''the effects of gender-imbalances on marriage markets''' and think this would be a fun project to try to model in NetLogo and something that would tie in nicely with Wendy's idea. The basic concept is to try to model the effects of "marriage markets" with more men in them than women or vice-versa, with possible extension to see if this same concept could be expanded to problem-solution matching. Examples of social groups which experience a gender imbalances in marriage markets include: most religious groups, college campuses, some large cities (such as New York and Washington, DC), the African-American community, and some nations (notably China). I am interested in how these gender imbalances affect social norms, marriage and divorce rates, and dating/matching behavior in each of these various groups. Other problem-solution matchings might include: employer-employee, entrepreneur-investor, buyer-seller, etc. If we make the model robust enough, we might be able to extend it to these and other contexts as well.

Some thoughts I have of what to incorporate into the model include:
* The effects of social capital.
* Vision (limited ability to see other agents).
* Open vs. closed groups. (Adjusting rate of entry/exit of agents.)
* Slider-switch for adjusting sex-ratios.
* "Tainting effects" for failure.
* Heterogeneous "attraction" characteristics of each agent.

I'd love to hear ideas anyone might have for this.

[[Wendy Ham]]: [[Jacopo Tagliabue]] shared some interesting thoughts on how to define problems and solutions --> 1) The first one is to define a problem as a lack of knowledge (where knowledge may be theoretical, knowing that, or applied, knowing how) and then use a doxastic logic approach to clarify the notion. The idea is that there is a set of possible states of the world, so-called possible worlds in formal semantic, and our world is one of them: the more you know about the world, the more worlds you can rule out (in the end, with perfect knowledge you will find out which is our world among the infinite set of possibility). You may represent a world as a long description: the set of possible worlds is thus the set pf possible descriptions. Just one of them happens to be THE TRUE description of our world: our tricky task is to find out which one is. For example, since we know that gravity is inversely proportional to distance, we know that all the description saying that gravity is not inversely proportional to distance are false, and cannot be the description of our world. The idea that increasing knowledge means reducing possibilities is analogous to the idea that acquiring information decrease the uncertainties. A problem can be modeled by a set of possible worlds, where each world in the set may actually be the world we live in. A solution is a function from this set to a sub-set of the set (or something similar, I haven't think in depth about this). 2) A second approach may be incorporating some notion from formal learning theorem, where the scientific enterprise is modeled using result from recursion theory (look at this: http://www.princeton.edu/~osherson/papers/hist25.pdf).

[[Wendy Ham]]: My thought originally was to use ABM to model a population of problems and solutions by: 1) determining what counts as problems and as solutions, 2) assigning dimensions to problems and solutions, which determine how they subsequently form a cluster in someone's head, and 3) determining how these heads subsequently form a larger cluster of disciplines, 4) demonstrating that compatible problems and solutions can occasionally end up in faraway clusters (such that they need to be brought back together to generate innovation - possibly using random shortcuts a la those found in small world networks). Jacopo's ideas are making me reevaluate these thoughts...

[[Wendy Ham]]: (Credit to [[Nathan Hodas]]) To be a bit more empirical, it would be interesting to examine a major innovative problem solving event in history that involve the cross-pollination of ideas from several disciplines, e.g., the discovery of the double helix structure, and ask: what kind of structure or system could we have put in place to make such event occur sooner? In other words, what can be done - structurally speaking - to expedite the 'mating' of problems and solutions from traditionally separate fields?

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===Modeling Gossip Networks===

It could be neat to develop a model of gossip networks. If you define gossip as information passed between 2 individuals (call them A and B) about a third party (C), then the act of gossiping has the potential to change the status/connection strength of all parties involved (e.g. maybe strength A-B, and weaken A-C and B-C bonds). Essentially passing information along a path in the network changes the value of BOTH edges in the direct pathway as well as other edges in the network. These are just preliminary ideas, but perhaps we could model how gossip tendency/frequency influences the structure of a network. Also, is it possible for individuals to influence their location in a network (e.g. increase centrality) by changing their gossiping frequency? (Although this is potentially a complicated rather than complex model idea...) Let me know what you guys think! [[Allison Shaw]]
* [[Milena Tsvetkova]]: This is a very interesting idea from sociological point of view. The effect of networks on the spread of gossip is well understood: some of the social dynamics at play include biases in the selection of trusted third parties (one draws a sample of information consistent with one’s predisposition), the reinforcement of opinions in dyads due to an etiquette mechanism, the exaggeration of information in triads due to echo effects. However, I am not aware of any studies that investigate how the spread of gossip affects network structure. My work is on the coevolution of behavior and social networks so we should talk!

* XOXO [[Chang Yu]]:Interesting! Gossip is not always bad. If we can model its spreading mechanism, it could help especially when you want to spread information unofficially. I get some inspirations from Tom’s last lecture on Friday. In the gossip network, what kind of properties of these agents can speed up or reduce information spread, the range of social circle, poverty, wealth, the information itself, or even the locations of houses in a community? I think we may model the different spreading results under different properties.

[[David Brooks]]: I agree that this concept of Gossip Networks is a generic for the analysis of several potential problems. I would like to talk to you about your intended direction and methods.

[[Gustavo Lacerda]]: sounds like some interesting dynamics, but how are you going to get data?

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Interacting distribution networks ===

I'm interested in thinking about evolving, interacting (re)distribution networks. Many large-scale aggregate networks are actually composed of several essentially independent subnetworks (e.g. individual airline carriers, local utility distribution companies), each of which takes into account the other agents' actions. While there may be interesting structure in the aggregate view, we know that the system followed an evolutionary path affected by interactions and should expect evidence of that process in the network structure. In other words: let's think of an agent-based model where each agent is a subnetwork maximizing some objective in a shared environment with constrained resources. I know there is some work on creating networks using games, but the agents are typically single nodes - see [http://portal.acm.org/citation.cfm?id=872035.872088 On a network creation game]

There may be some reasonable biological applications (for example, competing fungal hyphae networks; there was a recent work which modeled individual fungal growth - see [http://rspb.royalsocietypublishing.org/content/274/1623/2307.abstract Biological solutions to transport network design], possibly root structures, functional neural modules?) or social applications (competing idea networks). At the moment I'd love to think about anything other than airline networks.

There are many directions to take this depending on the system in question. Off the top of my head:

* Under what conditions (i.e. which games) can competing entities coexist? In this case, do they all form similar network structures, or do different structures allow them to occupy noncompeting niches?

* How does the game structure affect equilibrium network structure?

* Apparently certain environments support different size networks (small-scale regional carriers, large-scale national/international carriers) - is this realizable with an identical objective function for all agents?

I know basically nothing about game theory, and I'd love to take this in a biological direction. I'm also happy to go off in another direction if this inspires a tangential idea. [[Daniel Wuellner]]

[[Caroline Farrior]] This sounds pretty cool. I don't know much about networks, or airlines, but I do know about evolutionary game theory.

[[Randy Haas]] I'd like to talk some more with ya'll about this. It sounds relevant to a problem I'm interested in, which involves the emergence of settlement hierarchies in 'prisitine' state societies. I'm playing with the idea that such hierarchies are a redistribution solution to optimal resource allocation. Do you see a connection with Christaller's [http://en.wikipedia.org/wiki/Central_Place_Theory Central Place Theory]? It's an oldy, but it seems relevant that a cited critique of the theory is its inability to capture dynamic process.

[[David Brooks]] I have some experience in the analysis and specification of multi-modal and multi-step transportation systems and would like to discuss your intended direction and methods.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Modeling behaviors between students and teachers===

[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !

Interesting idea, you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know, I know some of the authors of the two papers. Alhaji Cherif

[[Chang Yu]]:Thank u Alhaji! I really appreciate if you could tell me how to get the papers. Have you done any research in this area? We should talk about it! Thanks.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and has looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects, too. His papers might be a good starting point.

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to ‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-17T02:28:26Z

Acherif: /* Guns, Germs and Steel: Modeling the fates of human societies */

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

[[Mahyar Malekpour]]: Update June 16, 2009 - Someone asked if there is an application for this. The answer is yes, categorically, any self-organizing system needs synchronization. However, my interest here are visualization and exploration using agent-based tools. I don not intend to develop a solution to this problem, rather build on an existing agent-based model (if there is any) and enhance its capabilities.

===The Global Spread of Cricket===
No I'm not actually intending to study this particular topic. But there is one interesting article published in 2005 (Kaufman and Patterson, American Sociological Review) that examined why cricket continues to be popular in many British-influenced societies while it is not in the U.S. and Canada. This is interesting given the fact that cricket was very popular in the two countries and that the first official international cricket match took place between the two countries in the mid-19th century. So, not only how cultures, ideas, technologies, etc. diffuse across nations, populations, and so on, but also mechanisms that influence the retention after the initial adoption merit serious attention I think. One possible topic include is modern contraceptive use in developing countries. I guess modeling such mechanisms would require taking into account the models presented by Nathan Collins and Peter Dodds, in addition to signed networks (Doreian). One difficulty of modeling this kind of mechanism is that both structural and individual factors should be considered [[Hirotoshi Yoshioka]].

There is a nice article by H. Peyton Young ('Social Dynamics: Theory and Applications', Handbook of Computational Economics, Vol. II; you can download it at http://www.econ.jhu.edu/people/young/Publications.html) which investigates the evolution of norms or conventions in an agent-based modelling / evolutionary game theoretic setting (in our small library there is also a whole book about that by Peyton Young). It might be interesting to analyze the diverging popularity of cricket in those countries (which can be interpreted as a kind of convention) in this framework. [[Mareen Hofmann]]

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response]] project page. '''We still need a biologist.''' [[Marek Kwiatkowski]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Problem solving and mating - are they similar?===
I was intrigued by Tom's model of mating and began to wonder whether we can think of problem solving in a similar way. If we were to model problem solving, how would we do it? I'd like to think that problems and solutions are components that combine to generate an emergent property. (After a problem meets a solution--or a solution meets a problem--something new is allowed to emerge. While one instance of problem solving does not exactly create a complex system, many instances may.) That said, there are several questions/considerations to think about before/while we create a proper model of problem solving:

* Given a population of information/knowledge, how can we identify what are problems and what are solutions?
* Actually, which comes first: knowledge, information, problems, or solutions?
* What are some important dimensions of problems and solutions? (These dimensions should inform some kind of a matching probability for problems and solutions.)
* What is the difference between problems and solutions anyway?
* What makes certain kinds of problems and solutions "hang out" in a cluster or neighboring clusters? Is this primarily due to path-dependence?
* When there is a difficult problem (tentatively defined as a problem for which there is no nearby solutions), how can we tell which clusters have the greatest probability of containing the solution(s)? (Can some of the network stuff we learned be of help here?)
* It is of course important to remember that a problem can have many solutions, and a solution can solve many problems, but that they may have different degrees of affinity (just like a ligand-receptor interaction in molecular biology). Also, occasionally a problem needs a combination of several solutions ("AND" as opposed to "OR").

I would love to hear your thoughts and comments, and I'm hoping that someone may actually share some of my interests in figuring out the answers to the questions above! [[Wendy Ham]]

[[Murad Mithani]]: We can look at problem solving as a special case of idea generation. See if you find any parallels between what you have in mind to what is written in the creative process.

[[David Brooks]]: This matching of past solutions or components to new problems leads to several interesting topics of discussion: (1) Shouldn't the process of developing a solution path be treated as a potentially complex system, (2) How do we describe the process without providing a falsely formulaic structure (3) When is the problem, the set of goals, and the process considered to be identified and what elements of the description may hint to the fragility of understanding? I have quite a bit of experience researching and addressing these issues and can help if this becomes a project.

[[Image:Bjh_singles_map.png|250px|right]]'''[[Brian Hollar]]:''' I've been doing some research for my dissertation on '''the effects of gender-imbalances on marriage markets''' and think this would be a fun project to try to model in NetLogo and something that would tie in nicely with Wendy's idea. The basic concept is to try to model the effects of "marriage markets" with more men in them than women or vice-versa, with possible extension to see if this same concept could be expanded to problem-solution matching. Examples of social groups which experience a gender imbalances in marriage markets include: most religious groups, college campuses, some large cities (such as New York and Washington, DC), the African-American community, and some nations (notably China). I am interested in how these gender imbalances affect social norms, marriage and divorce rates, and dating/matching behavior in each of these various groups. Other problem-solution matchings might include: employer-employee, entrepreneur-investor, buyer-seller, etc. If we make the model robust enough, we might be able to extend it to these and other contexts as well.

Some thoughts I have of what to incorporate into the model include:
* The effects of social capital.
* Vision (limited ability to see other agents).
* Open vs. closed groups. (Adjusting rate of entry/exit of agents.)
* Slider-switch for adjusting sex-ratios.
* "Tainting effects" for failure.
* Heterogeneous "attraction" characteristics of each agent.

I'd love to hear ideas anyone might have for this.

[[Wendy Ham]]: [[Jacopo Tagliabue]] shared some interesting thoughts on how to define problems and solutions --> 1) The first one is to define a problem as a lack of knowledge (where knowledge may be theoretical, knowing that, or applied, knowing how) and then use a doxastic logic approach to clarify the notion. The idea is that there is a set of possible states of the world, so-called possible worlds in formal semantic, and our world is one of them: the more you know about the world, the more worlds you can rule out (in the end, with perfect knowledge you will find out which is our world among the infinite set of possibility). You may represent a world as a long description: the set of possible worlds is thus the set pf possible descriptions. Just one of them happens to be THE TRUE description of our world: our tricky task is to find out which one is. For example, since we know that gravity is inversely proportional to distance, we know that all the description saying that gravity is not inversely proportional to distance are false, and cannot be the description of our world. The idea that increasing knowledge means reducing possibilities is analogous to the idea that acquiring information decrease the uncertainties. A problem can be modeled by a set of possible worlds, where each world in the set may actually be the world we live in. A solution is a function from this set to a sub-set of the set (or something similar, I haven't think in depth about this). 2) A second approach may be incorporating some notion from formal learning theorem, where the scientific enterprise is modeled using result from recursion theory (look at this: http://www.princeton.edu/~osherson/papers/hist25.pdf).

[[Wendy Ham]]: My thought originally was to use ABM to model a population of problems and solutions by: 1) determining what counts as problems and as solutions, 2) assigning dimensions to problems and solutions, which determine how they subsequently form a cluster in someone's head, and 3) determining how these heads subsequently form a larger cluster of disciplines, 4) demonstrating that compatible problems and solutions can occasionally end up in faraway clusters (such that they need to be brought back together to generate innovation - possibly using random shortcuts a la those found in small world networks). Jacopo's ideas are making me reevaluate these thoughts...

[[Wendy Ham]]: (Credit to [[Nathan Hodas]]) To be a bit more empirical, it would be interesting to examine a major innovative problem solving event in history that involve the cross-pollination of ideas from several disciplines, e.g., the discovery of the double helix structure, and ask: what kind of structure or system could we have put in place to make such event occur sooner? In other words, what can be done - structurally speaking - to expedite the 'mating' of problems and solutions from traditionally separate fields?

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===Modeling Gossip Networks===

It could be neat to develop a model of gossip networks. If you define gossip as information passed between 2 individuals (call them A and B) about a third party (C), then the act of gossiping has the potential to change the status/connection strength of all parties involved (e.g. maybe strength A-B, and weaken A-C and B-C bonds). Essentially passing information along a path in the network changes the value of BOTH edges in the direct pathway as well as other edges in the network. These are just preliminary ideas, but perhaps we could model how gossip tendency/frequency influences the structure of a network. Also, is it possible for individuals to influence their location in a network (e.g. increase centrality) by changing their gossiping frequency? (Although this is potentially a complicated rather than complex model idea...) Let me know what you guys think! [[Allison Shaw]]
* [[Milena Tsvetkova]]: This is a very interesting idea from sociological point of view. The effect of networks on the spread of gossip is well understood: some of the social dynamics at play include biases in the selection of trusted third parties (one draws a sample of information consistent with one’s predisposition), the reinforcement of opinions in dyads due to an etiquette mechanism, the exaggeration of information in triads due to echo effects. However, I am not aware of any studies that investigate how the spread of gossip affects network structure. My work is on the coevolution of behavior and social networks so we should talk!

* XOXO [[Chang Yu]]:Interesting! Gossip is not always bad. If we can model its spreading mechanism, it could help especially when you want to spread information unofficially. I get some inspirations from Tom’s last lecture on Friday. In the gossip network, what kind of properties of these agents can speed up or reduce information spread, the range of social circle, poverty, wealth, the information itself, or even the locations of houses in a community? I think we may model the different spreading results under different properties.

[[David Brooks]]: I agree that this concept of Gossip Networks is a generic for the analysis of several potential problems. I would like to talk to you about your intended direction and methods.

[[Gustavo Lacerda]]: sounds like some interesting dynamics, but how are you going to get data?

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Interacting distribution networks ===

I'm interested in thinking about evolving, interacting (re)distribution networks. Many large-scale aggregate networks are actually composed of several essentially independent subnetworks (e.g. individual airline carriers, local utility distribution companies), each of which takes into account the other agents' actions. While there may be interesting structure in the aggregate view, we know that the system followed an evolutionary path affected by interactions and should expect evidence of that process in the network structure. In other words: let's think of an agent-based model where each agent is a subnetwork maximizing some objective in a shared environment with constrained resources. I know there is some work on creating networks using games, but the agents are typically single nodes - see [http://portal.acm.org/citation.cfm?id=872035.872088 On a network creation game]

There may be some reasonable biological applications (for example, competing fungal hyphae networks; there was a recent work which modeled individual fungal growth - see [http://rspb.royalsocietypublishing.org/content/274/1623/2307.abstract Biological solutions to transport network design], possibly root structures, functional neural modules?) or social applications (competing idea networks). At the moment I'd love to think about anything other than airline networks.

There are many directions to take this depending on the system in question. Off the top of my head:

* Under what conditions (i.e. which games) can competing entities coexist? In this case, do they all form similar network structures, or do different structures allow them to occupy noncompeting niches?

* How does the game structure affect equilibrium network structure?

* Apparently certain environments support different size networks (small-scale regional carriers, large-scale national/international carriers) - is this realizable with an identical objective function for all agents?

I know basically nothing about game theory, and I'd love to take this in a biological direction. I'm also happy to go off in another direction if this inspires a tangential idea. [[Daniel Wuellner]]

[[Caroline Farrior]] This sounds pretty cool. I don't know much about networks, or airlines, but I do know about evolutionary game theory.

[[Randy Haas]] I'd like to talk some more with ya'll about this. It sounds relevant to a problem I'm interested in, which involves the emergence of settlement hierarchies in 'prisitine' state societies. I'm playing with the idea that such hierarchies are a redistribution solution to optimal resource allocation. Do you see a connection with Christaller's [http://en.wikipedia.org/wiki/Central_Place_Theory Central Place Theory]? It's an oldy, but it seems relevant that a cited critique of the theory is its inability to capture dynamic process.

[[David Brooks]] I have some experience in the analysis and specification of multi-modal and multi-step transportation systems and would like to discuss your intended direction and methods.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Modeling behaviors between students and teachers===

[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !

Interesting idea, you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know some of the authors of the two papers. Alhaji Cherif

[[Chang Yu]]:Thank u Alhaji! I really appreciate if you could tell me how to get the papers. Have you done any research in this area? We should talk about it! Thanks.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and has looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects, too. His papers might be a good starting point.

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to ‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-17T00:20:54Z

Acherif: /* Guns, Germs and Steel: Modeling the fates of human societies */

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

===The Global Spread of Cricket===
No I'm not actually intending to study this particular topic. But there is one interesting article published in 2005 (Kaufman and Patterson, American Sociological Review) that examined why cricket continues to be popular in many British-influenced societies while it is not in the U.S. and Canada. This is interesting given the fact that cricket was very popular in the two countries and that the first official international cricket match took place between the two countries in the mid-19th century. So, not only how cultures, ideas, technologies, etc. diffuse across nations, populations, and so on, but also mechanisms that influence the retention after the initial adoption merit serious attention I think. One possible topic include is modern contraceptive use in developing countries. I guess modeling such mechanisms would require taking into account the models presented by Nathan Collins and Peter Dodds, in addition to signed networks (Doreian). One difficulty of modeling this kind of mechanism is that both structural and individual factors should be considered [[Hirotoshi Yoshioka]].

There is a nice article by H. Peyton Young ('Social Dynamics: Theory and Applications', Handbook of Computational Economics, Vol. II; you can download it at http://www.econ.jhu.edu/people/young/Publications.html) which investigates the evolution of norms or conventions in an agent-based modelling / evolutionary game theoretic setting (in our small library there is also a whole book about that by Peyton Young). It might be interesting to analyze the diverging popularity of cricket in those countries (which can be interpreted as a kind of convention) in this framework. [[Mareen Hofmann]]

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response]] project page. '''We still need a biologist.''' [[Marek Kwiatkowski]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Problem solving and mating - are they similar?===
I was intrigued by Tom's model of mating and began to wonder whether we can think of problem solving in a similar way. If we were to model problem solving, how would we do it? I'd like to think that problems and solutions are components that combine to generate an emergent property. (After a problem meets a solution--or a solution meets a problem--something new is allowed to emerge. While one instance of problem solving does not exactly create a complex system, many instances may.) That said, there are several questions/considerations to think about before/while we create a proper model of problem solving:

* Given a population of information/knowledge, how can we identify what are problems and what are solutions?
* Actually, which comes first: knowledge, information, problems, or solutions?
* What are some important dimensions of problems and solutions? (These dimensions should inform some kind of a matching probability for problems and solutions.)
* What is the difference between problems and solutions anyway?
* What makes certain kinds of problems and solutions "hang out" in a cluster or neighboring clusters? Is this primarily due to path-dependence?
* When there is a difficult problem (tentatively defined as a problem for which there is no nearby solutions), how can we tell which clusters have the greatest probability of containing the solution(s)? (Can some of the network stuff we learned be of help here?)
* It is of course important to remember that a problem can have many solutions, and a solution can solve many problems, but that they may have different degrees of affinity (just like a ligand-receptor interaction in molecular biology). Also, occasionally a problem needs a combination of several solutions ("AND" as opposed to "OR").

I would love to hear your thoughts and comments, and I'm hoping that someone may actually share some of my interests in figuring out the answers to the questions above! [[Wendy Ham]]

[[Murad Mithani]]: We can look at problem solving as a special case of idea generation. See if you find any parallels between what you have in mind to what is written in the creative process.

[[David Brooks]]: This matching of past solutions or components to new problems leads to several interesting topics of discussion: (1) Shouldn't the process of developing a solution path be treated as a potentially complex system, (2) How do we describe the process without providing a falsely formulaic structure (3) When is the problem, the set of goals, and the process considered to be identified and what elements of the description may hint to the fragility of understanding? I have quite a bit of experience researching and addressing these issues and can help if this becomes a project.

[[Image:Bjh_singles_map.png|250px|right]]'''[[Brian Hollar]]:''' I've been doing some research for my dissertation on '''the effects of gender-imbalances on marriage markets''' and think this would be a fun project to try to model in NetLogo and something that would tie in nicely with Wendy's idea. The basic concept is to try to model the effects of "marriage markets" with more men in them than women or vice-versa, with possible extension to see if this same concept could be expanded to problem-solution matching. Examples of social groups which experience a gender imbalances in marriage markets include: most religious groups, college campuses, some large cities (such as New York and Washington, DC), the African-American community, and some nations (notably China). I am interested in how these gender imbalances affect social norms, marriage and divorce rates, and dating/matching behavior in each of these various groups. Other problem-solution matchings might include: employer-employee, entrepreneur-investor, buyer-seller, etc. If we make the model robust enough, we might be able to extend it to these and other contexts as well.

Some thoughts I have of what to incorporate into the model include:
* The effects of social capital.
* Vision (limited ability to see other agents).
* Open vs. closed groups. (Adjusting rate of entry/exit of agents.)
* Slider-switch for adjusting sex-ratios.
* "Tainting effects" for failure.
* Heterogeneous "attraction" characteristics of each agent.

I'd love to hear ideas anyone might have for this.

[[Wendy Ham]]: [[Jacopo Tagliabue]] shared some interesting thoughts on how to define problems and solutions --> 1) The first one is to define a problem as a lack of knowledge (where knowledge may be theoretical, knowing that, or applied, knowing how) and then use a doxastic logic approach to clarify the notion. The idea is that there is a set of possible states of the world, so-called possible worlds in formal semantic, and our world is one of them: the more you know about the world, the more worlds you can rule out (in the end, with perfect knowledge you will find out which is our world among the infinite set of possibility). You may represent a world as a long description: the set of possible worlds is thus the set pf possible descriptions. Just one of them happens to be THE TRUE description of our world: our tricky task is to find out which one is. For example, since we know that gravity is inversely proportional to distance, we know that all the description saying that gravity is not inversely proportional to distance are false, and cannot be the description of our world. The idea that increasing knowledge means reducing possibilities is analogous to the idea that acquiring information decrease the uncertainties. A problem can be modeled by a set of possible worlds, where each world in the set may actually be the world we live in. A solution is a function from this set to a sub-set of the set (or something similar, I haven't think in depth about this). 2) A second approach may be incorporating some notion from formal learning theorem, where the scientific enterprise is modeled using result from recursion theory (look at this: http://www.princeton.edu/~osherson/papers/hist25.pdf).

[[Wendy Ham]]: My thought originally was to use ABM to model a population of problems and solutions by: 1) determining what counts as problems and as solutions, 2) assigning dimensions to problems and solutions, which determine how they subsequently form a cluster in someone's head, and 3) determining how these heads subsequently form a larger cluster of disciplines, 4) demonstrating that compatible problems and solutions can occasionally end up in faraway clusters (such that they need to be brought back together to generate innovation - possibly using random shortcuts a la those found in small world networks). Jacopo's ideas are making me reevaluate these thoughts...

[[Wendy Ham]]: (Credit to [[Nathan Hodas]]) To be a bit more empirical, it would be interesting to examine a major innovative problem solving event in history that involve the cross-pollination of ideas from several disciplines, e.g., the discovery of the double helix structure, and ask: what kind of structure or system could we have put in place to make such event occur sooner? In other words, what can be done - structurally speaking - to expedite the 'mating' of problems and solutions from traditionally separate fields?

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===Modeling Gossip Networks===

It could be neat to develop a model of gossip networks. If you define gossip as information passed between 2 individuals (call them A and B) about a third party (C), then the act of gossiping has the potential to change the status/connection strength of all parties involved (e.g. maybe strength A-B, and weaken A-C and B-C bonds). Essentially passing information along a path in the network changes the value of BOTH edges in the direct pathway as well as other edges in the network. These are just preliminary ideas, but perhaps we could model how gossip tendency/frequency influences the structure of a network. Also, is it possible for individuals to influence their location in a network (e.g. increase centrality) by changing their gossiping frequency? (Although this is potentially a complicated rather than complex model idea...) Let me know what you guys think! [[Allison Shaw]]
* [[Milena Tsvetkova]]: This is a very interesting idea from sociological point of view. The effect of networks on the spread of gossip is well understood: some of the social dynamics at play include biases in the selection of trusted third parties (one draws a sample of information consistent with one’s predisposition), the reinforcement of opinions in dyads due to an etiquette mechanism, the exaggeration of information in triads due to echo effects. However, I am not aware of any studies that investigate how the spread of gossip affects network structure. My work is on the coevolution of behavior and social networks so we should talk!

* XOXO [[Chang Yu]]:Interesting! Gossip is not always bad. If we can model its spreading mechanism, it could help especially when you want to spread information unofficially. I get some inspirations from Tom’s last lecture on Friday. In the gossip network, what kind of properties of these agents can speed up or reduce information spread, the range of social circle, poverty, wealth, the information itself, or even the locations of houses in a community? I think we may model the different spreading results under different properties.

[[David Brooks]]: I agree that this concept of Gossip Networks is a generic for the analysis of several potential problems. I would like to talk to you about your intended direction and methods.

[[Gustavo Lacerda]]: sounds like some interesting dynamics, but how are you going to get data?

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Interacting distribution networks ===

I'm interested in thinking about evolving, interacting (re)distribution networks. Many large-scale aggregate networks are actually composed of several essentially independent subnetworks (e.g. individual airline carriers, local utility distribution companies), each of which takes into account the other agents' actions. While there may be interesting structure in the aggregate view, we know that the system followed an evolutionary path affected by interactions and should expect evidence of that process in the network structure. In other words: let's think of an agent-based model where each agent is a subnetwork maximizing some objective in a shared environment with constrained resources. I know there is some work on creating networks using games, but the agents are typically single nodes - see [http://portal.acm.org/citation.cfm?id=872035.872088 On a network creation game]

There may be some reasonable biological applications (for example, competing fungal hyphae networks; there was a recent work which modeled individual fungal growth - see [http://rspb.royalsocietypublishing.org/content/274/1623/2307.abstract Biological solutions to transport network design], possibly root structures, functional neural modules?) or social applications (competing idea networks). At the moment I'd love to think about anything other than airline networks.

There are many directions to take this depending on the system in question. Off the top of my head:

* Under what conditions (i.e. which games) can competing entities coexist? In this case, do they all form similar network structures, or do different structures allow them to occupy noncompeting niches?

* How does the game structure affect equilibrium network structure?

* Apparently certain environments support different size networks (small-scale regional carriers, large-scale national/international carriers) - is this realizable with an identical objective function for all agents?

I know basically nothing about game theory, and I'd love to take this in a biological direction. I'm also happy to go off in another direction if this inspires a tangential idea. [[Daniel Wuellner]]

[[Caroline Farrior]] This sounds pretty cool. I don't know much about networks, or airlines, but I do know about evolutionary game theory.

[[Randy Haas]] I'd like to talk some more with ya'll about this. It sounds relevant to a problem I'm interested in, which involves the emergence of settlement hierarchies in 'prisitine' state societies. I'm playing with the idea that such hierarchies are a redistribution solution to optimal resource allocation. Do you see a connection with Christaller's [http://en.wikipedia.org/wiki/Central_Place_Theory Central Place Theory]? It's an oldy, but it seems relevant that a cited critique of the theory is its inability to capture dynamic process.

[[David Brooks]] I have some experience in the analysis and specification of multi-modal and multi-step transportation systems and would like to discuss your intended direction and methods.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Modeling behaviors between students and teachers===

[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !

Interesting idea, you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know some of the authors of the two papers. Alhaji Cherif

[[Chang Yu]]:Thank u Alhaji! I really appreciate if you could tell me how to get the papers. Have you done any research in this area? We should talk about it! Thanks.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and have looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects. His papers might be a good starting point.

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to ‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-17T00:20:27Z

Acherif: /* Guns, Germs and Steel: Modeling the fates of human societies */

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

===The Global Spread of Cricket===
No I'm not actually intending to study this particular topic. But there is one interesting article published in 2005 (Kaufman and Patterson, American Sociological Review) that examined why cricket continues to be popular in many British-influenced societies while it is not in the U.S. and Canada. This is interesting given the fact that cricket was very popular in the two countries and that the first official international cricket match took place between the two countries in the mid-19th century. So, not only how cultures, ideas, technologies, etc. diffuse across nations, populations, and so on, but also mechanisms that influence the retention after the initial adoption merit serious attention I think. One possible topic include is modern contraceptive use in developing countries. I guess modeling such mechanisms would require taking into account the models presented by Nathan Collins and Peter Dodds, in addition to signed networks (Doreian). One difficulty of modeling this kind of mechanism is that both structural and individual factors should be considered [[Hirotoshi Yoshioka]].

There is a nice article by H. Peyton Young ('Social Dynamics: Theory and Applications', Handbook of Computational Economics, Vol. II; you can download it at http://www.econ.jhu.edu/people/young/Publications.html) which investigates the evolution of norms or conventions in an agent-based modelling / evolutionary game theoretic setting (in our small library there is also a whole book about that by Peyton Young). It might be interesting to analyze the diverging popularity of cricket in those countries (which can be interpreted as a kind of convention) in this framework. [[Mareen Hofmann]]

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response]] project page. '''We still need a biologist.''' [[Marek Kwiatkowski]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Problem solving and mating - are they similar?===
I was intrigued by Tom's model of mating and began to wonder whether we can think of problem solving in a similar way. If we were to model problem solving, how would we do it? I'd like to think that problems and solutions are components that combine to generate an emergent property. (After a problem meets a solution--or a solution meets a problem--something new is allowed to emerge. While one instance of problem solving does not exactly create a complex system, many instances may.) That said, there are several questions/considerations to think about before/while we create a proper model of problem solving:

* Given a population of information/knowledge, how can we identify what are problems and what are solutions?
* Actually, which comes first: knowledge, information, problems, or solutions?
* What are some important dimensions of problems and solutions? (These dimensions should inform some kind of a matching probability for problems and solutions.)
* What is the difference between problems and solutions anyway?
* What makes certain kinds of problems and solutions "hang out" in a cluster or neighboring clusters? Is this primarily due to path-dependence?
* When there is a difficult problem (tentatively defined as a problem for which there is no nearby solutions), how can we tell which clusters have the greatest probability of containing the solution(s)? (Can some of the network stuff we learned be of help here?)
* It is of course important to remember that a problem can have many solutions, and a solution can solve many problems, but that they may have different degrees of affinity (just like a ligand-receptor interaction in molecular biology). Also, occasionally a problem needs a combination of several solutions ("AND" as opposed to "OR").

I would love to hear your thoughts and comments, and I'm hoping that someone may actually share some of my interests in figuring out the answers to the questions above! [[Wendy Ham]]

[[Murad Mithani]]: We can look at problem solving as a special case of idea generation. See if you find any parallels between what you have in mind to what is written in the creative process.

[[David Brooks]]: This matching of past solutions or components to new problems leads to several interesting topics of discussion: (1) Shouldn't the process of developing a solution path be treated as a potentially complex system, (2) How do we describe the process without providing a falsely formulaic structure (3) When is the problem, the set of goals, and the process considered to be identified and what elements of the description may hint to the fragility of understanding? I have quite a bit of experience researching and addressing these issues and can help if this becomes a project.

[[Image:Bjh_singles_map.png|250px|right]]'''[[Brian Hollar]]:''' I've been doing some research for my dissertation on '''the effects of gender-imbalances on marriage markets''' and think this would be a fun project to try to model in NetLogo and something that would tie in nicely with Wendy's idea. The basic concept is to try to model the effects of "marriage markets" with more men in them than women or vice-versa, with possible extension to see if this same concept could be expanded to problem-solution matching. Examples of social groups which experience a gender imbalances in marriage markets include: most religious groups, college campuses, some large cities (such as New York and Washington, DC), the African-American community, and some nations (notably China). I am interested in how these gender imbalances affect social norms, marriage and divorce rates, and dating/matching behavior in each of these various groups. Other problem-solution matchings might include: employer-employee, entrepreneur-investor, buyer-seller, etc. If we make the model robust enough, we might be able to extend it to these and other contexts as well.

Some thoughts I have of what to incorporate into the model include:
* The effects of social capital.
* Vision (limited ability to see other agents).
* Open vs. closed groups. (Adjusting rate of entry/exit of agents.)
* Slider-switch for adjusting sex-ratios.
* "Tainting effects" for failure.
* Heterogeneous "attraction" characteristics of each agent.

I'd love to hear ideas anyone might have for this.

[[Wendy Ham]]: [[Jacopo Tagliabue]] shared some interesting thoughts on how to define problems and solutions --> 1) The first one is to define a problem as a lack of knowledge (where knowledge may be theoretical, knowing that, or applied, knowing how) and then use a doxastic logic approach to clarify the notion. The idea is that there is a set of possible states of the world, so-called possible worlds in formal semantic, and our world is one of them: the more you know about the world, the more worlds you can rule out (in the end, with perfect knowledge you will find out which is our world among the infinite set of possibility). You may represent a world as a long description: the set of possible worlds is thus the set pf possible descriptions. Just one of them happens to be THE TRUE description of our world: our tricky task is to find out which one is. For example, since we know that gravity is inversely proportional to distance, we know that all the description saying that gravity is not inversely proportional to distance are false, and cannot be the description of our world. The idea that increasing knowledge means reducing possibilities is analogous to the idea that acquiring information decrease the uncertainties. A problem can be modeled by a set of possible worlds, where each world in the set may actually be the world we live in. A solution is a function from this set to a sub-set of the set (or something similar, I haven't think in depth about this). 2) A second approach may be incorporating some notion from formal learning theorem, where the scientific enterprise is modeled using result from recursion theory (look at this: http://www.princeton.edu/~osherson/papers/hist25.pdf).

[[Wendy Ham]]: My thought originally was to use ABM to model a population of problems and solutions by: 1) determining what counts as problems and as solutions, 2) assigning dimensions to problems and solutions, which determine how they subsequently form a cluster in someone's head, and 3) determining how these heads subsequently form a larger cluster of disciplines, 4) demonstrating that compatible problems and solutions can occasionally end up in faraway clusters (such that they need to be brought back together to generate innovation - possibly using random shortcuts a la those found in small world networks). Jacopo's ideas are making me reevaluate these thoughts...

[[Wendy Ham]]: (Credit to [[Nathan Hodas]]) To be a bit more empirical, it would be interesting to examine a major innovative problem solving event in history that involve the cross-pollination of ideas from several disciplines, e.g., the discovery of the double helix structure, and ask: what kind of structure or system could we have put in place to make such event occur sooner? In other words, what can be done - structurally speaking - to expedite the 'mating' of problems and solutions from traditionally separate fields?

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===Modeling Gossip Networks===

It could be neat to develop a model of gossip networks. If you define gossip as information passed between 2 individuals (call them A and B) about a third party (C), then the act of gossiping has the potential to change the status/connection strength of all parties involved (e.g. maybe strength A-B, and weaken A-C and B-C bonds). Essentially passing information along a path in the network changes the value of BOTH edges in the direct pathway as well as other edges in the network. These are just preliminary ideas, but perhaps we could model how gossip tendency/frequency influences the structure of a network. Also, is it possible for individuals to influence their location in a network (e.g. increase centrality) by changing their gossiping frequency? (Although this is potentially a complicated rather than complex model idea...) Let me know what you guys think! [[Allison Shaw]]
* [[Milena Tsvetkova]]: This is a very interesting idea from sociological point of view. The effect of networks on the spread of gossip is well understood: some of the social dynamics at play include biases in the selection of trusted third parties (one draws a sample of information consistent with one’s predisposition), the reinforcement of opinions in dyads due to an etiquette mechanism, the exaggeration of information in triads due to echo effects. However, I am not aware of any studies that investigate how the spread of gossip affects network structure. My work is on the coevolution of behavior and social networks so we should talk!

* XOXO [[Chang Yu]]:Interesting! Gossip is not always bad. If we can model its spreading mechanism, it could help especially when you want to spread information unofficially. I get some inspirations from Tom’s last lecture on Friday. In the gossip network, what kind of properties of these agents can speed up or reduce information spread, the range of social circle, poverty, wealth, the information itself, or even the locations of houses in a community? I think we may model the different spreading results under different properties.

[[David Brooks]]: I agree that this concept of Gossip Networks is a generic for the analysis of several potential problems. I would like to talk to you about your intended direction and methods.

[[Gustavo Lacerda]]: sounds like some interesting dynamics, but how are you going to get data?

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Interacting distribution networks ===

I'm interested in thinking about evolving, interacting (re)distribution networks. Many large-scale aggregate networks are actually composed of several essentially independent subnetworks (e.g. individual airline carriers, local utility distribution companies), each of which takes into account the other agents' actions. While there may be interesting structure in the aggregate view, we know that the system followed an evolutionary path affected by interactions and should expect evidence of that process in the network structure. In other words: let's think of an agent-based model where each agent is a subnetwork maximizing some objective in a shared environment with constrained resources. I know there is some work on creating networks using games, but the agents are typically single nodes - see [http://portal.acm.org/citation.cfm?id=872035.872088 On a network creation game]

There may be some reasonable biological applications (for example, competing fungal hyphae networks; there was a recent work which modeled individual fungal growth - see [http://rspb.royalsocietypublishing.org/content/274/1623/2307.abstract Biological solutions to transport network design], possibly root structures, functional neural modules?) or social applications (competing idea networks). At the moment I'd love to think about anything other than airline networks.

There are many directions to take this depending on the system in question. Off the top of my head:

* Under what conditions (i.e. which games) can competing entities coexist? In this case, do they all form similar network structures, or do different structures allow them to occupy noncompeting niches?

* How does the game structure affect equilibrium network structure?

* Apparently certain environments support different size networks (small-scale regional carriers, large-scale national/international carriers) - is this realizable with an identical objective function for all agents?

I know basically nothing about game theory, and I'd love to take this in a biological direction. I'm also happy to go off in another direction if this inspires a tangential idea. [[Daniel Wuellner]]

[[Caroline Farrior]] This sounds pretty cool. I don't know much about networks, or airlines, but I do know about evolutionary game theory.

[[Randy Haas]] I'd like to talk some more with ya'll about this. It sounds relevant to a problem I'm interested in, which involves the emergence of settlement hierarchies in 'prisitine' state societies. I'm playing with the idea that such hierarchies are a redistribution solution to optimal resource allocation. Do you see a connection with Christaller's [http://en.wikipedia.org/wiki/Central_Place_Theory Central Place Theory]? It's an oldy, but it seems relevant that a cited critique of the theory is its inability to capture dynamic process.

[[David Brooks]] I have some experience in the analysis and specification of multi-modal and multi-step transportation systems and would like to discuss your intended direction and methods.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Modeling behaviors between students and teachers===

[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !

Interesting idea, you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know some of the authors of the two papers. Alhaji Cherif

[[Chang Yu]]:Thank u Alhaji! I really appreciate if you could tell me how to get the papers. Have you done any research in this area? We should talk about it! Thanks.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.
[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and have looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects. His papers might be a good starting point.

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to ‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-15T03:47:33Z

Acherif: /* Modeling behaviors between students and teachers */

{{CSSS 2009 Santa Fe}}
==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

===The Global Spread of Cricket===
No I'm not actually intending to study this particular topic. But there is one interesting article published in 2005 (Kaufman and Patterson, American Sociological Review) that examined why cricket continues to be popular in many British-influenced societies while it is not in the U.S. and Canada. This is interesting given the fact that cricket was very popular in the two countries and that the first official international cricket match took place between the two countries in the mid-19th century. So, not only how cultures, ideas, technologies, etc. diffuse across nations, populations, and so on, but also mechanisms that influence the retention after the initial adoption merit serious attention I think. One possible topic include is modern contraceptive use in developing countries. I guess modeling such mechanisms would require taking into account the models presented by Nathan Collins and Peter Dodds, in addition to signed networks (Doreian). One difficulty of modeling this kind of mechanism is that both structural and individual factors should be considered [[Hirotoshi Yoshioka]].

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

Introducing Agent-Based Modeling:
Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs.

Let me know your thoughts. [[Karen Simpson]]

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response]] project page. '''We still need a biologist.''' [[Marek Kwiatkowski]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Problem solving and mating - are they similar?===
I was intrigued by Tom's model of mating and began to wonder whether we can think of problem solving in a similar way. If we were to model problem solving, how would we do it? I'd like to think that problems and solutions are components that combine to generate an emergent property. (After a problem meets a solution--or a solution meets a problem--something new is allowed to emerge. While one instance of problem solving does not exactly create a complex system, many instances may.) That said, there are several questions/considerations to think about before/while we create a proper model of problem solving:

* What is the difference between problems and solutions anyway?
* What makes certain kinds of problems and solutions "hang out" in a cluster or neighboring clusters? Is this primarily due to path-dependence?
* When there is a difficult problem (tentatively defined as a problem for which there is no nearby solutions), how can we tell which clusters have the greatest probability of containing the solution(s)? (Can some of the network stuff we learned be of help here?)
* It is of course important to remember that a problem can have many solutions, and a solution can solve many problems, but that they may have different degrees of affinity (just like a ligand-receptor interaction in molecular biology). Also, occasionally a problem needs a combination of several solutions ("AND" as opposed to "OR").

I would love to hear your thoughts and comments, and I'm hoping that someone may actually share some of my interests in figuring out the answers to the questions above! [[Wendy Ham]]

[[Murad Mithani]]: We can look at problem solving as a special case of idea generation. See if you find any parallels between what you have in mind to what is written in the creative process.

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

*I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

===Evolving nanomachines===
Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

===Foraging on the move===
[[Image:Caribou.jpg|250px|thumb|left|Snapshot of caribou migration.]]
[[Allison Shaw]]: Many animals forage in groups while moving from one location to another. This means individuals have to simultaneously balance several demands: finding the best resources, maintaining the cohesion of the group, and in some cases moving in a certain direction. Can we develop an agent-based model with a simple set of individual movement rules that would allow for all these demands to be met?

This was inspired by a piece of Planet Earth footage on caribou: go to http://dsc.discovery.com/convergence/planet-earth/video-player/video-player.html, scroll down in the video clips to "Planet Earth: Plains: Following the Caribou" and watch the dynamics at about 1:30-2:00. (If anyone has a hard copy of this segment or knows how to get one, please let me know!). In this case each individual caribou pauses to eat along the way but the group never fragments and in fact it seems to almost 'flow' through an area. My guess is that one of the physicists could provide some interesting insight on how to model this.

[[Daniel Wuellner]]: Cool idea. Most importantly: I actually brought the Planet Earth DVDs with me which I'll happily lend; maybe we can organize a viewing w/ a projector somewhere.

I think there's some swarm literature out there for ideas on rules you could extend to incorporate foraging (or any other caribouish behavior). The one I know is [http://portal.acm.org/citation.cfm?id=37401.37406&type=series Flocks, herds and schools: A distributed behavioral model] (this actually might be the 'original' swarm paper).

[[Kate Behrman]]: I also interested in this. One possible extension could be to consider how the structure of the landscape between the two locations affects the movement of the group.

[[Steven Lade]]: I like the sound of this too.

[[Murad Mithani]]: It sounds similar to what happens when the cognitive processes are focused on a particular problem to come up with ideas. The initiation of problem solving is a conscious mechanism that flourishes when that initial push is taken away. If you guys are planning to model this in some way, count me in.

===Modeling Gossip Networks===

It could be neat to develop a model of gossip networks. If you define gossip as information passed between 2 individuals (call them A and B) about a third party (C), then the act of gossiping has the potential to change the status/connection strength of all parties involved (e.g. maybe strength A-B, and weaken A-C and B-C bonds). Essentially passing information along a path in the network changes the value of BOTH edges in the direct pathway as well as other edges in the network. These are just preliminary ideas, but perhaps we could model how gossip tendency/frequency influences the structure of a network. Also, is it possible for individuals to influence their location in a network (e.g. increase centrality) by changing their gossiping frequency? (Although this is potentially a complicated rather than complex model idea...) Let me know what you guys think! [[Allison Shaw]]
* [[Milena Tsvetkova]]: This is a very interesting idea from sociological point of view. The effect of networks on the spread of gossip is well understood: some of the social dynamics at play include biases in the selection of trusted third parties (one draws a sample of information consistent with one’s predisposition), the reinforcement of opinions in dyads due to an etiquette mechanism, the exaggeration of information in triads due to echo effects. However, I am not aware of any studies that investigate how the spread of gossip affects network structure. My work is on the coevolution of behavior and social networks so we should talk!

* XOXO [[Chang Yu]]:Interesting! Gossip is not always bad. If we can model its spreading mechanism, it could help especially when you want to spread information unofficially. I get some inspirations from Tom’s last lecture on Friday. In the gossip network, what kind of properties of these agents can speed up or reduce information spread, the range of social circle, poverty, wealth, the information itself, or even the locations of houses in a community? I think we may model the different spreading results under different properties.

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Interacting distribution networks ===

I'm interested in thinking about evolving, interacting (re)distribution networks. Many large-scale aggregate networks are actually composed of several essentially independent subnetworks (e.g. individual airline carriers, local utility distribution companies), each of which takes into account the other agents' actions. While there may be interesting structure in the aggregate view, we know that the system followed an evolutionary path affected by interactions and should expect evidence of that process in the network structure. In other words: let's think of an agent-based model where each agent is a subnetwork maximizing some objective in a shared environment with constrained resources. I know there is some work on creating networks using games, but the agents are typically single nodes - see [http://portal.acm.org/citation.cfm?id=872035.872088 On a network creation game]

There may be some reasonable biological applications (for example, competing fungal hyphae networks; there was a recent work which modeled individual fungal growth - see [http://rspb.royalsocietypublishing.org/content/274/1623/2307.abstract Biological solutions to transport network design], possibly root structures, functional neural modules?) or social applications (competing idea networks). At the moment I'd love to think about anything other than airline networks.

There are many directions to take this depending on the system in question. Off the top of my head:

* Under what conditions (i.e. which games) can competing entities coexist? In this case, do they all form similar network structures, or do different structures allow them to occupy noncompeting niches?

* How does the game structure affect equilibrium network structure?

* Apparently certain environments support different size networks (small-scale regional carriers, large-scale national/international carriers) - is this realizable with an identical objective function for all agents?

I know basically nothing about game theory, and I'd love to take this in a biological direction. I'm also happy to go off in another direction if this inspires a tangential idea. [[Daniel Wuellner]]

[[Caroline Farrior]] This sounds pretty cool. I don't know much about networks, or airlines, but I do know about evolutionary game theory.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods presented by Scott Paul at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO-PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Paul about this already, and he says that his method should be useful to help to resolve the issue.

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, feel free to chat with me.

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

===Modeling behaviors between students and teachers===

[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !

Interesting idea, you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know some of the authors of the two papers. Alhaji Cherif

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-15T03:46:33Z

Acherif: /* Modeling behaviors between students and teachers */

{{CSSS 2009 Santa Fe}}
==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

===The Global Spread of Cricket===
No I'm not actually intending to study this particular topic. But there is one interesting article published in 2005 (Kaufman and Patterson, American Sociological Review) that examined why cricket continues to be popular in many British-influenced societies while it is not in the U.S. and Canada. This is interesting given the fact that cricket was very popular in the two countries and that the first official international cricket match took place between the two countries in the mid-19th century. So, not only how cultures, ideas, technologies, etc. diffuse across nations, populations, and so on, but also mechanisms that influence the retention after the initial adoption merit serious attention I think. One possible topic include is modern contraceptive use in developing countries. I guess modeling such mechanisms would require taking into account the models presented by Nathan Collins and Peter Dodds, in addition to signed networks (Doreian). One difficulty of modeling this kind of mechanism is that both structural and individual factors should be considered [[Hirotoshi Yoshioka]].

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

Introducing Agent-Based Modeling:
Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs.

Let me know your thoughts. [[Karen Simpson]]

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response]] project page. '''We still need a biologist.''' [[Marek Kwiatkowski]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Problem solving and mating - are they similar?===
I was intrigued by Tom's model of mating and began to wonder whether we can think of problem solving in a similar way. If we were to model problem solving, how would we do it? I'd like to think that problems and solutions are components that combine to generate an emergent property. (After a problem meets a solution--or a solution meets a problem--something new is allowed to emerge. While one instance of problem solving does not exactly create a complex system, many instances may.) That said, there are several questions/considerations to think about before/while we create a proper model of problem solving:

* What is the difference between problems and solutions anyway?
* What makes certain kinds of problems and solutions "hang out" in a cluster or neighboring clusters? Is this primarily due to path-dependence?
* When there is a difficult problem (tentatively defined as a problem for which there is no nearby solutions), how can we tell which clusters have the greatest probability of containing the solution(s)? (Can some of the network stuff we learned be of help here?)
* It is of course important to remember that a problem can have many solutions, and a solution can solve many problems, but that they may have different degrees of affinity (just like a ligand-receptor interaction in molecular biology). Also, occasionally a problem needs a combination of several solutions ("AND" as opposed to "OR").

I would love to hear your thoughts and comments, and I'm hoping that someone may actually share some of my interests in figuring out the answers to the questions above! [[Wendy Ham]]

[[Murad Mithani]]: We can look at problem solving as a special case of idea generation. See if you find any parallels between what you have in mind to what is written in the creative process.

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

*I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

===Evolving nanomachines===
Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

===Foraging on the move===
[[Image:Caribou.jpg|250px|thumb|left|Snapshot of caribou migration.]]
[[Allison Shaw]]: Many animals forage in groups while moving from one location to another. This means individuals have to simultaneously balance several demands: finding the best resources, maintaining the cohesion of the group, and in some cases moving in a certain direction. Can we develop an agent-based model with a simple set of individual movement rules that would allow for all these demands to be met?

This was inspired by a piece of Planet Earth footage on caribou: go to http://dsc.discovery.com/convergence/planet-earth/video-player/video-player.html, scroll down in the video clips to "Planet Earth: Plains: Following the Caribou" and watch the dynamics at about 1:30-2:00. (If anyone has a hard copy of this segment or knows how to get one, please let me know!). In this case each individual caribou pauses to eat along the way but the group never fragments and in fact it seems to almost 'flow' through an area. My guess is that one of the physicists could provide some interesting insight on how to model this.

[[Daniel Wuellner]]: Cool idea. Most importantly: I actually brought the Planet Earth DVDs with me which I'll happily lend; maybe we can organize a viewing w/ a projector somewhere.

I think there's some swarm literature out there for ideas on rules you could extend to incorporate foraging (or any other caribouish behavior). The one I know is [http://portal.acm.org/citation.cfm?id=37401.37406&type=series Flocks, herds and schools: A distributed behavioral model] (this actually might be the 'original' swarm paper).

[[Kate Behrman]]: I also interested in this. One possible extension could be to consider how the structure of the landscape between the two locations affects the movement of the group.

[[Steven Lade]]: I like the sound of this too.

[[Murad Mithani]]: It sounds similar to what happens when the cognitive processes are focused on a particular problem to come up with ideas. The initiation of problem solving is a conscious mechanism that flourishes when that initial push is taken away. If you guys are planning to model this in some way, count me in.

===Modeling Gossip Networks===

It could be neat to develop a model of gossip networks. If you define gossip as information passed between 2 individuals (call them A and B) about a third party (C), then the act of gossiping has the potential to change the status/connection strength of all parties involved (e.g. maybe strength A-B, and weaken A-C and B-C bonds). Essentially passing information along a path in the network changes the value of BOTH edges in the direct pathway as well as other edges in the network. These are just preliminary ideas, but perhaps we could model how gossip tendency/frequency influences the structure of a network. Also, is it possible for individuals to influence their location in a network (e.g. increase centrality) by changing their gossiping frequency? (Although this is potentially a complicated rather than complex model idea...) Let me know what you guys think! [[Allison Shaw]]
* [[Milena Tsvetkova]]: This is a very interesting idea from sociological point of view. The effect of networks on the spread of gossip is well understood: some of the social dynamics at play include biases in the selection of trusted third parties (one draws a sample of information consistent with one’s predisposition), the reinforcement of opinions in dyads due to an etiquette mechanism, the exaggeration of information in triads due to echo effects. However, I am not aware of any studies that investigate how the spread of gossip affects network structure. My work is on the coevolution of behavior and social networks so we should talk!

* XOXO [[Chang Yu]]:Interesting! Gossip is not always bad. If we can model its spreading mechanism, it could help especially when you want to spread information unofficially. I get some inspirations from Tom’s last lecture on Friday. In the gossip network, what kind of properties of these agents can speed up or reduce information spread, the range of social circle, poverty, wealth, the information itself, or even the locations of houses in a community? I think we may model the different spreading results under different properties.

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Interacting distribution networks ===

I'm interested in thinking about evolving, interacting (re)distribution networks. Many large-scale aggregate networks are actually composed of several essentially independent subnetworks (e.g. individual airline carriers, local utility distribution companies), each of which takes into account the other agents' actions. While there may be interesting structure in the aggregate view, we know that the system followed an evolutionary path affected by interactions and should expect evidence of that process in the network structure. In other words: let's think of an agent-based model where each agent is a subnetwork maximizing some objective in a shared environment with constrained resources. I know there is some work on creating networks using games, but the agents are typically single nodes - see [http://portal.acm.org/citation.cfm?id=872035.872088 On a network creation game]

There may be some reasonable biological applications (for example, competing fungal hyphae networks; there was a recent work which modeled individual fungal growth - see [http://rspb.royalsocietypublishing.org/content/274/1623/2307.abstract Biological solutions to transport network design], possibly root structures, functional neural modules?) or social applications (competing idea networks). At the moment I'd love to think about anything other than airline networks.

There are many directions to take this depending on the system in question. Off the top of my head:

* Under what conditions (i.e. which games) can competing entities coexist? In this case, do they all form similar network structures, or do different structures allow them to occupy noncompeting niches?

* How does the game structure affect equilibrium network structure?

* Apparently certain environments support different size networks (small-scale regional carriers, large-scale national/international carriers) - is this realizable with an identical objective function for all agents?

I know basically nothing about game theory, and I'd love to take this in a biological direction. I'm also happy to go off in another direction if this inspires a tangential idea. [[Daniel Wuellner]]

[[Caroline Farrior]] This sounds pretty cool. I don't know much about networks, or airlines, but I do know about evolutionary game theory.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods presented by Scott Paul at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO-PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Paul about this already, and he says that his method should be useful to help to resolve the issue.

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, feel free to chat with me.

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

===Modeling behaviors between students and teachers===

[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !

Interesting paper you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know some of the authors of the two papers. Alhaji Cherif

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-15T02:55:16Z

Acherif: /* Modeling behaviors between students and teachers */

{{CSSS 2009 Santa Fe}}
==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

===The Global Spread of Cricket===
No I'm not actually intending to study this particular topic. But there is one interesting article published in 2005 (Kaufman and Patterson, American Sociological Review) that examined why cricket continues to be popular in many British-influenced societies while it is not in the U.S. and Canada. This is interesting given the fact that cricket was very popular in the two countries and that the first official international cricket match took place between the two countries in the mid-19th century. So, not only how cultures, ideas, technologies, etc. diffuse across nations, populations, and so on, but also mechanisms that influence the retention after the initial adoption merit serious attention I think. One possible topic include is modern contraceptive use in developing countries. I guess modeling such mechanisms would require taking into account the models presented by Nathan Collins and Peter Dodds, in addition to signed networks (Doreian). One difficulty of modeling this kind of mechanism is that both structural and individual factors should be considered [[Hirotoshi Yoshioka]].

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

Introducing Agent-Based Modeling:
Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs.

Let me know your thoughts. [[Karen Simpson]]

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response]] project page. '''We still need a biologist.''' [[Marek Kwiatkowski]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Problem solving and mating - are they similar?===
I was intrigued by Tom's model of mating and began to wonder whether we can think of problem solving in a similar way. If we were to model problem solving, how would we do it? I'd like to think that problems and solutions are components that combine to generate an emergent property. (After a problem meets a solution--or a solution meets a problem--something new is allowed to emerge. While one instance of problem solving does not exactly create a complex system, many instances may.) That said, there are several questions/considerations to think about before/while we create a proper model of problem solving:

* What is the difference between problems and solutions anyway?
* What makes certain kinds of problems and solutions "hang out" in a cluster or neighboring clusters? Is this primarily due to path-dependence?
* When there is a difficult problem (tentatively defined as a problem for which there is no nearby solutions), how can we tell which clusters have the greatest probability of containing the solution(s)? (Can some of the network stuff we learned be of help here?)
* It is of course important to remember that a problem can have many solutions, and a solution can solve many problems, but that they may have different degrees of affinity (just like a ligand-receptor interaction in molecular biology). Also, occasionally a problem needs a combination of several solutions ("AND" as opposed to "OR").

I would love to hear your thoughts and comments, and I'm hoping that someone may actually share some of my interests in figuring out the answers to the questions above! [[Wendy Ham]]

[[Murad Mithani]]: We can look at problem solving as a special case of idea generation. See if you find any parallels between what you have in mind to what is written in the creative process.

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

*I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

===Evolving nanomachines===
Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

===Foraging on the move===
[[Image:Caribou.jpg|250px|thumb|left|Snapshot of caribou migration.]]
[[Allison Shaw]]: Many animals forage in groups while moving from one location to another. This means individuals have to simultaneously balance several demands: finding the best resources, maintaining the cohesion of the group, and in some cases moving in a certain direction. Can we develop an agent-based model with a simple set of individual movement rules that would allow for all these demands to be met?

This was inspired by a piece of Planet Earth footage on caribou: go to http://dsc.discovery.com/convergence/planet-earth/video-player/video-player.html, scroll down in the video clips to "Planet Earth: Plains: Following the Caribou" and watch the dynamics at about 1:30-2:00. (If anyone has a hard copy of this segment or knows how to get one, please let me know!). In this case each individual caribou pauses to eat along the way but the group never fragments and in fact it seems to almost 'flow' through an area. My guess is that one of the physicists could provide some interesting insight on how to model this.

[[Daniel Wuellner]]: Cool idea. Most importantly: I actually brought the Planet Earth DVDs with me which I'll happily lend; maybe we can organize a viewing w/ a projector somewhere.

I think there's some swarm literature out there for ideas on rules you could extend to incorporate foraging (or any other caribouish behavior). The one I know is [http://portal.acm.org/citation.cfm?id=37401.37406&type=series Flocks, herds and schools: A distributed behavioral model] (this actually might be the 'original' swarm paper).

[[Kate Behrman]]: I also interested in this. One possible extension could be to consider how the structure of the landscape between the two locations affects the movement of the group.

[[Steven Lade]]: I like the sound of this too.

[[Murad Mithani]]: It sounds similar to what happens when the cognitive processes are focused on a particular problem to come up with ideas. The initiation of problem solving is a conscious mechanism that flourishes when that initial push is taken away. If you guys are planning to model this in some way, count me in.

===Modeling Gossip Networks===

It could be neat to develop a model of gossip networks. If you define gossip as information passed between 2 individuals (call them A and B) about a third party (C), then the act of gossiping has the potential to change the status/connection strength of all parties involved (e.g. maybe strength A-B, and weaken A-C and B-C bonds). Essentially passing information along a path in the network changes the value of BOTH edges in the direct pathway as well as other edges in the network. These are just preliminary ideas, but perhaps we could model how gossip tendency/frequency influences the structure of a network. Also, is it possible for individuals to influence their location in a network (e.g. increase centrality) by changing their gossiping frequency? (Although this is potentially a complicated rather than complex model idea...) Let me know what you guys think! [[Allison Shaw]]
* [[Milena Tsvetkova]]: This is a very interesting idea from sociological point of view. The effect of networks on the spread of gossip is well understood: some of the social dynamics at play include biases in the selection of trusted third parties (one draws a sample of information consistent with one’s predisposition), the reinforcement of opinions in dyads due to an etiquette mechanism, the exaggeration of information in triads due to echo effects. However, I am not aware of any studies that investigate how the spread of gossip affects network structure. My work is on the coevolution of behavior and social networks so we should talk!

* XOXO [[Chang Yu]]:Interesting! Gossip is not always bad. If we can model its spreading mechanism, it could help especially when you want to spread information unofficially. I get some inspirations from Tom’s last lecture on Friday. In the gossip network, what kind of properties of these agents can speed up or reduce information spread, the range of social circle, poverty, wealth, the information itself, or even the locations of houses in a community? I think we may model the different spreading results under different properties.

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Interacting distribution networks ===

I'm interested in thinking about evolving, interacting (re)distribution networks. Many large-scale aggregate networks are actually composed of several essentially independent subnetworks (e.g. individual airline carriers, local utility distribution companies), each of which takes into account the other agents' actions. While there may be interesting structure in the aggregate view, we know that the system followed an evolutionary path affected by interactions and should expect evidence of that process in the network structure. In other words: let's think of an agent-based model where each agent is a subnetwork maximizing some objective in a shared environment with constrained resources. I know there is some work on creating networks using games, but the agents are typically single nodes - see [http://portal.acm.org/citation.cfm?id=872035.872088 On a network creation game]

There may be some reasonable biological applications (for example, competing fungal hyphae networks; there was a recent work which modeled individual fungal growth - see [http://rspb.royalsocietypublishing.org/content/274/1623/2307.abstract Biological solutions to transport network design], possibly root structures, functional neural modules?) or social applications (competing idea networks). At the moment I'd love to think about anything other than airline networks.

There are many directions to take this depending on the system in question. Off the top of my head:

* Under what conditions (i.e. which games) can competing entities coexist? In this case, do they all form similar network structures, or do different structures allow them to occupy noncompeting niches?

* How does the game structure affect equilibrium network structure?

* Apparently certain environments support different size networks (small-scale regional carriers, large-scale national/international carriers) - is this realizable with an identical objective function for all agents?

I know basically nothing about game theory, and I'd love to take this in a biological direction. I'm also happy to go off in another direction if this inspires a tangential idea. [[Daniel Wuellner]]

[[Caroline Farrior]] This sounds pretty cool. I don't know much about networks, or airlines, but I do know about evolutionary game theory.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods presented by Scott Paul at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO-PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Paul about this already, and he says that his method should be useful to help to resolve the issue.

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, feel free to chat with me.

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

===Modeling behaviors between students and teachers===

[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !
Interesting paper you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know some of the authors of the two papers. Alhaji Cherif

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-13T05:23:27Z

Acherif: /* Disease ecology of media hype */

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-11T07:20:25Z

Acherif: /* Brainstorming */

{{CSSS 2009 Santa Fe}}
==Brainstorming==
* '''Disease ecology of media hype.''' How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

[[Alhaji Cherif]] you could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.

*[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
'''Housing prices.''' The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 
*'''Movie Turnouts''' Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

Alhaji Cherif

2009-05-31T03:04:25Z

Acherif: /* 4. Do you have any possible projects in mind for the CSSS? */

=Brief Bio=
Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus (under Castillo-Chavez) at Arizona State University. I hold a BS in engineering from Cornell University and was trained in Theoretical and Applied Mechanics (under Strogatz), and Electrical Engineering (udner Belina) with minors/certification in Applied Mathematics (Rand), African Politics, African American Studies (Assie-Lumumba), Mechanical Engineering-unofficial (Phoenix). I spent most of my undergraduate years doing research in the above areas and in nanobiotechnology (NEMS/MEMS, microfluidcs, biosensors), composite materials, reliability theory. I have worked on a variety of research problems, including the development of micro-fluidic systems also known as lab-on-chips (biosensors and micropump, NEMS/MEMS); theoretical modeling of electrophoretic deposition of thin-films; mechanics of fibrous composites; and dynamic model of the flight of butterfly, socio-political dynamics of instability, political reliability theory. I have a short interest horizon and broad interests in interdisciplinary applied mathematics, specially when applied to physical, engineering, natural, biological and social sciences. As an engineer and a scientist who has straddle various scientific fields, I believe in the importance of real world problems as an inspiration for the development of mathematical theory, not for its own sake, but as a means to solving important practical problems and providing practical decision solutions. I hope to add Complexity Theory to my intellectual tool box and possibly use them in my research in a near future. I look forward to meeting you all in June and do not hesitate to email me at alhaji_dot_cherif_at_asu_dot_edu.

=Questions:=

==1. What are your main interests? Feel free to include a "pie in the sky" big idea!==
I am generally interested in the application of dynamical system theory (deterministic, stochastic and crypto-deterministic) to interesting problems in a variety of fields (Engineering, social and biological sciences). In the area of mathematical epidemiology, I am interested in incorporating socio-behavorial dynamics into models. I am also interested in sociopolitical dynamics, political violence and aggression (civil wars, terrorism).

==2. What sorts of expertise can you bring to the group?==
As an engineer, mathematician and Africanist who has been exposed to various fields, I can bring some of the transferable skills I have learned over the years. I can contribute to the knowledge of dynamical systems, bifurcation theory, asymptotic and perturbation methods in Deterministic and Stochastic Differential Equations, Dynamical Programming and Control Theory, Mechanics and other engineering and applied mathematics skills, and most importantly modeling skills.

==3. What do you hope to get out of the CSSS?==
I hope to learn more about complex adaptive dynamical systems and to establish possible future collaborations with other students and SFI scholars.

==4. Do you have any possible projects in mind for the CSSS?==
I will like to continue on some of the projects I have recently been working on. Possible projects are:
[1] dynamics of radicalization (terrorism) in prison, of Islamic diaspora;
[2] coevolution of colonial and traditional institutions in Africa and its impact on current African political conundrums;
[3] polity distributions;
[4] mathematical modeling of Gangsterism or;
[5] generalization of the 3 theories I developed in my undergraduate thesis: sociopolitical bundle and reliability, and socio-econo-demographic theories and their applications in economics, politics and state collapse;
[6] and I am, of course, open to other ideas and/or modification of then above and other previous research questions I was interested in but never got around doing them.

Alhaji Cherif

2009-05-31T02:58:13Z

Acherif: /* Questions: */

=Brief Bio=
Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus (under Castillo-Chavez) at Arizona State University. I hold a BS in engineering from Cornell University and was trained in Theoretical and Applied Mechanics (under Strogatz), and Electrical Engineering (udner Belina) with minors/certification in Applied Mathematics (Rand), African Politics, African American Studies (Assie-Lumumba), Mechanical Engineering-unofficial (Phoenix). I spent most of my undergraduate years doing research in the above areas and in nanobiotechnology (NEMS/MEMS, microfluidcs, biosensors), composite materials, reliability theory. I have worked on a variety of research problems, including the development of micro-fluidic systems also known as lab-on-chips (biosensors and micropump, NEMS/MEMS); theoretical modeling of electrophoretic deposition of thin-films; mechanics of fibrous composites; and dynamic model of the flight of butterfly, socio-political dynamics of instability, political reliability theory. I have a short interest horizon and broad interests in interdisciplinary applied mathematics, specially when applied to physical, engineering, natural, biological and social sciences. As an engineer and a scientist who has straddle various scientific fields, I believe in the importance of real world problems as an inspiration for the development of mathematical theory, not for its own sake, but as a means to solving important practical problems and providing practical decision solutions. I hope to add Complexity Theory to my intellectual tool box and possibly use them in my research in a near future. I look forward to meeting you all in June and do not hesitate to email me at alhaji_dot_cherif_at_asu_dot_edu.

=Questions:=

==1. What are your main interests? Feel free to include a "pie in the sky" big idea!==
I am generally interested in the application of dynamical system theory (deterministic, stochastic and crypto-deterministic) to interesting problems in a variety of fields (Engineering, social and biological sciences). In the area of mathematical epidemiology, I am interested in incorporating socio-behavorial dynamics into models. I am also interested in sociopolitical dynamics, political violence and aggression (civil wars, terrorism).

==2. What sorts of expertise can you bring to the group?==
As an engineer, mathematician and Africanist who has been exposed to various fields, I can bring some of the transferable skills I have learned over the years. I can contribute to the knowledge of dynamical systems, bifurcation theory, asymptotic and perturbation methods in Deterministic and Stochastic Differential Equations, Dynamical Programming and Control Theory, Mechanics and other engineering and applied mathematics skills, and most importantly modeling skills.

==3. What do you hope to get out of the CSSS?==
I hope to learn more about complex adaptive dynamical systems and to establish possible future collaborations with other students and SFI scholars.

=4. Do you have any possible projects in mind for the CSSS?=
I will like to continue on some of the projects I have recently been working on. Possible projects are:
[a] dynamics of radicalization (terrorism) in prison, of Islamic diaspora
[b] coevolution of colonial and traditional institutions in Africa and its impact on current African political conundrums.
[c] polity distributions
[d] mathematical modeling of Gangsterism or
[e] generalization of the 3 theories I developed in my undergraduate thesis: sociopolitical bundle and reliability, and socio-econo-demographic theories and their applications in economics, politics and state collapse.
[f] and I am open to other ideas and/or modification of then above and other previous research questions I was interested in but never got around doing them.

Alhaji Cherif

2009-05-31T02:58:00Z

Acherif: /* Brief Bio */

=Brief Bio=
Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus (under Castillo-Chavez) at Arizona State University. I hold a BS in engineering from Cornell University and was trained in Theoretical and Applied Mechanics (under Strogatz), and Electrical Engineering (udner Belina) with minors/certification in Applied Mathematics (Rand), African Politics, African American Studies (Assie-Lumumba), Mechanical Engineering-unofficial (Phoenix). I spent most of my undergraduate years doing research in the above areas and in nanobiotechnology (NEMS/MEMS, microfluidcs, biosensors), composite materials, reliability theory. I have worked on a variety of research problems, including the development of micro-fluidic systems also known as lab-on-chips (biosensors and micropump, NEMS/MEMS); theoretical modeling of electrophoretic deposition of thin-films; mechanics of fibrous composites; and dynamic model of the flight of butterfly, socio-political dynamics of instability, political reliability theory. I have a short interest horizon and broad interests in interdisciplinary applied mathematics, specially when applied to physical, engineering, natural, biological and social sciences. As an engineer and a scientist who has straddle various scientific fields, I believe in the importance of real world problems as an inspiration for the development of mathematical theory, not for its own sake, but as a means to solving important practical problems and providing practical decision solutions. I hope to add Complexity Theory to my intellectual tool box and possibly use them in my research in a near future. I look forward to meeting you all in June and do not hesitate to email me at alhaji_dot_cherif_at_asu_dot_edu.

==Questions:==

==1. What are your main interests? Feel free to include a "pie in the sky" big idea!==
I am generally interested in the application of dynamical system theory (deterministic, stochastic and crypto-deterministic) to interesting problems in a variety of fields (Engineering, social and biological sciences). In the area of mathematical epidemiology, I am interested in incorporating socio-behavorial dynamics into models. I am also interested in sociopolitical dynamics, political violence and aggression (civil wars, terrorism).

==2. What sorts of expertise can you bring to the group?==
As an engineer, mathematician and Africanist who has been exposed to various fields, I can bring some of the transferable skills I have learned over the years. I can contribute to the knowledge of dynamical systems, bifurcation theory, asymptotic and perturbation methods in Deterministic and Stochastic Differential Equations, Dynamical Programming and Control Theory, Mechanics and other engineering and applied mathematics skills, and most importantly modeling skills.

==3. What do you hope to get out of the CSSS?==
I hope to learn more about complex adaptive dynamical systems and to establish possible future collaborations with other students and SFI scholars.

=4. Do you have any possible projects in mind for the CSSS?=
I will like to continue on some of the projects I have recently been working on. Possible projects are:
[a] dynamics of radicalization (terrorism) in prison, of Islamic diaspora
[b] coevolution of colonial and traditional institutions in Africa and its impact on current African political conundrums.
[c] polity distributions
[d] mathematical modeling of Gangsterism or
[e] generalization of the 3 theories I developed in my undergraduate thesis: sociopolitical bundle and reliability, and socio-econo-demographic theories and their applications in economics, politics and state collapse.
[f] and I am open to other ideas and/or modification of then above and other previous research questions I was interested in but never got around doing them.

Alhaji Cherif

2009-05-31T02:57:50Z

Acherif: /* 3. What do you hope to get out of the CSSS? */

==Brief Bio==
Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus (under Castillo-Chavez) at Arizona State University. I hold a BS in engineering from Cornell University and was trained in Theoretical and Applied Mechanics (under Strogatz), and Electrical Engineering (udner Belina) with minors/certification in Applied Mathematics (Rand), African Politics, African American Studies (Assie-Lumumba), Mechanical Engineering-unofficial (Phoenix). I spent most of my undergraduate years doing research in the above areas and in nanobiotechnology (NEMS/MEMS, microfluidcs, biosensors), composite materials, reliability theory. I have worked on a variety of research problems, including the development of micro-fluidic systems also known as lab-on-chips (biosensors and micropump, NEMS/MEMS); theoretical modeling of electrophoretic deposition of thin-films; mechanics of fibrous composites; and dynamic model of the flight of butterfly, socio-political dynamics of instability, political reliability theory. I have a short interest horizon and broad interests in interdisciplinary applied mathematics, specially when applied to physical, engineering, natural, biological and social sciences. As an engineer and a scientist who has straddle various scientific fields, I believe in the importance of real world problems as an inspiration for the development of mathematical theory, not for its own sake, but as a means to solving important practical problems and providing practical decision solutions. I hope to add Complexity Theory to my intellectual tool box and possibly use them in my research in a near future. I look forward to meeting you all in June and do not hesitate to email me at alhaji_dot_cherif_at_asu_dot_edu.

==Questions:==

==1. What are your main interests? Feel free to include a "pie in the sky" big idea!==
I am generally interested in the application of dynamical system theory (deterministic, stochastic and crypto-deterministic) to interesting problems in a variety of fields (Engineering, social and biological sciences). In the area of mathematical epidemiology, I am interested in incorporating socio-behavorial dynamics into models. I am also interested in sociopolitical dynamics, political violence and aggression (civil wars, terrorism).

==2. What sorts of expertise can you bring to the group?==
As an engineer, mathematician and Africanist who has been exposed to various fields, I can bring some of the transferable skills I have learned over the years. I can contribute to the knowledge of dynamical systems, bifurcation theory, asymptotic and perturbation methods in Deterministic and Stochastic Differential Equations, Dynamical Programming and Control Theory, Mechanics and other engineering and applied mathematics skills, and most importantly modeling skills.

==3. What do you hope to get out of the CSSS?==
I hope to learn more about complex adaptive dynamical systems and to establish possible future collaborations with other students and SFI scholars.

=4. Do you have any possible projects in mind for the CSSS?=
I will like to continue on some of the projects I have recently been working on. Possible projects are:
[a] dynamics of radicalization (terrorism) in prison, of Islamic diaspora
[b] coevolution of colonial and traditional institutions in Africa and its impact on current African political conundrums.
[c] polity distributions
[d] mathematical modeling of Gangsterism or
[e] generalization of the 3 theories I developed in my undergraduate thesis: sociopolitical bundle and reliability, and socio-econo-demographic theories and their applications in economics, politics and state collapse.
[f] and I am open to other ideas and/or modification of then above and other previous research questions I was interested in but never got around doing them.

Alhaji Cherif

2009-05-31T02:57:41Z

Acherif: /* 2. What sorts of expertise can you bring to the group? */

==Brief Bio==
Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus (under Castillo-Chavez) at Arizona State University. I hold a BS in engineering from Cornell University and was trained in Theoretical and Applied Mechanics (under Strogatz), and Electrical Engineering (udner Belina) with minors/certification in Applied Mathematics (Rand), African Politics, African American Studies (Assie-Lumumba), Mechanical Engineering-unofficial (Phoenix). I spent most of my undergraduate years doing research in the above areas and in nanobiotechnology (NEMS/MEMS, microfluidcs, biosensors), composite materials, reliability theory. I have worked on a variety of research problems, including the development of micro-fluidic systems also known as lab-on-chips (biosensors and micropump, NEMS/MEMS); theoretical modeling of electrophoretic deposition of thin-films; mechanics of fibrous composites; and dynamic model of the flight of butterfly, socio-political dynamics of instability, political reliability theory. I have a short interest horizon and broad interests in interdisciplinary applied mathematics, specially when applied to physical, engineering, natural, biological and social sciences. As an engineer and a scientist who has straddle various scientific fields, I believe in the importance of real world problems as an inspiration for the development of mathematical theory, not for its own sake, but as a means to solving important practical problems and providing practical decision solutions. I hope to add Complexity Theory to my intellectual tool box and possibly use them in my research in a near future. I look forward to meeting you all in June and do not hesitate to email me at alhaji_dot_cherif_at_asu_dot_edu.

==Questions:==

==1. What are your main interests? Feel free to include a "pie in the sky" big idea!==
I am generally interested in the application of dynamical system theory (deterministic, stochastic and crypto-deterministic) to interesting problems in a variety of fields (Engineering, social and biological sciences). In the area of mathematical epidemiology, I am interested in incorporating socio-behavorial dynamics into models. I am also interested in sociopolitical dynamics, political violence and aggression (civil wars, terrorism).

==2. What sorts of expertise can you bring to the group?==
As an engineer, mathematician and Africanist who has been exposed to various fields, I can bring some of the transferable skills I have learned over the years. I can contribute to the knowledge of dynamical systems, bifurcation theory, asymptotic and perturbation methods in Deterministic and Stochastic Differential Equations, Dynamical Programming and Control Theory, Mechanics and other engineering and applied mathematics skills, and most importantly modeling skills.

=3. What do you hope to get out of the CSSS?=
I hope to learn more about complex adaptive dynamical systems and to establish possible future collaborations with other students and SFI scholars.

=4. Do you have any possible projects in mind for the CSSS?=
I will like to continue on some of the projects I have recently been working on. Possible projects are:
[a] dynamics of radicalization (terrorism) in prison, of Islamic diaspora
[b] coevolution of colonial and traditional institutions in Africa and its impact on current African political conundrums.
[c] polity distributions
[d] mathematical modeling of Gangsterism or
[e] generalization of the 3 theories I developed in my undergraduate thesis: sociopolitical bundle and reliability, and socio-econo-demographic theories and their applications in economics, politics and state collapse.
[f] and I am open to other ideas and/or modification of then above and other previous research questions I was interested in but never got around doing them.

Alhaji Cherif

2009-05-31T02:57:18Z

Acherif: /* Questions: */

==Brief Bio==
Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus (under Castillo-Chavez) at Arizona State University. I hold a BS in engineering from Cornell University and was trained in Theoretical and Applied Mechanics (under Strogatz), and Electrical Engineering (udner Belina) with minors/certification in Applied Mathematics (Rand), African Politics, African American Studies (Assie-Lumumba), Mechanical Engineering-unofficial (Phoenix). I spent most of my undergraduate years doing research in the above areas and in nanobiotechnology (NEMS/MEMS, microfluidcs, biosensors), composite materials, reliability theory. I have worked on a variety of research problems, including the development of micro-fluidic systems also known as lab-on-chips (biosensors and micropump, NEMS/MEMS); theoretical modeling of electrophoretic deposition of thin-films; mechanics of fibrous composites; and dynamic model of the flight of butterfly, socio-political dynamics of instability, political reliability theory. I have a short interest horizon and broad interests in interdisciplinary applied mathematics, specially when applied to physical, engineering, natural, biological and social sciences. As an engineer and a scientist who has straddle various scientific fields, I believe in the importance of real world problems as an inspiration for the development of mathematical theory, not for its own sake, but as a means to solving important practical problems and providing practical decision solutions. I hope to add Complexity Theory to my intellectual tool box and possibly use them in my research in a near future. I look forward to meeting you all in June and do not hesitate to email me at alhaji_dot_cherif_at_asu_dot_edu.

==Questions:==

==1. What are your main interests? Feel free to include a "pie in the sky" big idea!==
I am generally interested in the application of dynamical system theory (deterministic, stochastic and crypto-deterministic) to interesting problems in a variety of fields (Engineering, social and biological sciences). In the area of mathematical epidemiology, I am interested in incorporating socio-behavorial dynamics into models. I am also interested in sociopolitical dynamics, political violence and aggression (civil wars, terrorism).

=2. What sorts of expertise can you bring to the group?=
As an engineer, mathematician and Africanist who has been exposed to various fields, I can bring some of the transferable skills I have learned over the years. I can contribute to the knowledge of dynamical systems, bifurcation theory, asymptotic and perturbation methods in Deterministic and Stochastic Differential Equations, Dynamical Programming and Control Theory, Mechanics and other engineering and applied mathematics skills, and most importantly modeling skills.

=3. What do you hope to get out of the CSSS?=
I hope to learn more about complex adaptive dynamical systems and to establish possible future collaborations with other students and SFI scholars.

=4. Do you have any possible projects in mind for the CSSS?=
I will like to continue on some of the projects I have recently been working on. Possible projects are:
[a] dynamics of radicalization (terrorism) in prison, of Islamic diaspora
[b] coevolution of colonial and traditional institutions in Africa and its impact on current African political conundrums.
[c] polity distributions
[d] mathematical modeling of Gangsterism or
[e] generalization of the 3 theories I developed in my undergraduate thesis: sociopolitical bundle and reliability, and socio-econo-demographic theories and their applications in economics, politics and state collapse.
[f] and I am open to other ideas and/or modification of then above and other previous research questions I was interested in but never got around doing them.

Alhaji Cherif

2009-05-31T02:56:43Z

Acherif: /* 1. What are your main interests? Feel free to include a "pie in the sky" big idea! */

==Brief Bio==
Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus (under Castillo-Chavez) at Arizona State University. I hold a BS in engineering from Cornell University and was trained in Theoretical and Applied Mechanics (under Strogatz), and Electrical Engineering (udner Belina) with minors/certification in Applied Mathematics (Rand), African Politics, African American Studies (Assie-Lumumba), Mechanical Engineering-unofficial (Phoenix). I spent most of my undergraduate years doing research in the above areas and in nanobiotechnology (NEMS/MEMS, microfluidcs, biosensors), composite materials, reliability theory. I have worked on a variety of research problems, including the development of micro-fluidic systems also known as lab-on-chips (biosensors and micropump, NEMS/MEMS); theoretical modeling of electrophoretic deposition of thin-films; mechanics of fibrous composites; and dynamic model of the flight of butterfly, socio-political dynamics of instability, political reliability theory. I have a short interest horizon and broad interests in interdisciplinary applied mathematics, specially when applied to physical, engineering, natural, biological and social sciences. As an engineer and a scientist who has straddle various scientific fields, I believe in the importance of real world problems as an inspiration for the development of mathematical theory, not for its own sake, but as a means to solving important practical problems and providing practical decision solutions. I hope to add Complexity Theory to my intellectual tool box and possibly use them in my research in a near future. I look forward to meeting you all in June and do not hesitate to email me at alhaji_dot_cherif_at_asu_dot_edu.

==Questions:==

=+1. What are your main interests? Feel free to include a "pie in the sky" big idea!=+
I am generally interested in the application of dynamical system theory (deterministic, stochastic and crypto-deterministic) to interesting problems in a variety of fields (Engineering, social and biological sciences). In the area of mathematical epidemiology, I am interested in incorporating socio-behavorial dynamics into models. I am also interested in sociopolitical dynamics, political violence and aggression (civil wars, terrorism).

=2. What sorts of expertise can you bring to the group?=
As an engineer, mathematician and Africanist who has been exposed to various fields, I can bring some of the transferable skills I have learned over the years. I can contribute to the knowledge of dynamical systems, bifurcation theory, asymptotic and perturbation methods in Deterministic and Stochastic Differential Equations, Dynamical Programming and Control Theory, Mechanics and other engineering and applied mathematics skills, and most importantly modeling skills.

=3. What do you hope to get out of the CSSS?=
I hope to learn more about complex adaptive dynamical systems and to establish possible future collaborations with other students and SFI scholars.

=4. Do you have any possible projects in mind for the CSSS?=
I will like to continue on some of the projects I have recently been working on. Possible projects are:
[a] dynamics of radicalization (terrorism) in prison, of Islamic diaspora
[b] coevolution of colonial and traditional institutions in Africa and its impact on current African political conundrums.
[c] polity distributions
[d] mathematical modeling of Gangsterism or
[e] generalization of the 3 theories I developed in my undergraduate thesis: sociopolitical bundle and reliability, and socio-econo-demographic theories and their applications in economics, politics and state collapse.
[f] and I am open to other ideas and/or modification of then above and other previous research questions I was interested in but never got around doing them.

Alhaji Cherif

2009-05-31T02:56:28Z

Acherif: /* Questions: */

==Brief Bio==
Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus (under Castillo-Chavez) at Arizona State University. I hold a BS in engineering from Cornell University and was trained in Theoretical and Applied Mechanics (under Strogatz), and Electrical Engineering (udner Belina) with minors/certification in Applied Mathematics (Rand), African Politics, African American Studies (Assie-Lumumba), Mechanical Engineering-unofficial (Phoenix). I spent most of my undergraduate years doing research in the above areas and in nanobiotechnology (NEMS/MEMS, microfluidcs, biosensors), composite materials, reliability theory. I have worked on a variety of research problems, including the development of micro-fluidic systems also known as lab-on-chips (biosensors and micropump, NEMS/MEMS); theoretical modeling of electrophoretic deposition of thin-films; mechanics of fibrous composites; and dynamic model of the flight of butterfly, socio-political dynamics of instability, political reliability theory. I have a short interest horizon and broad interests in interdisciplinary applied mathematics, specially when applied to physical, engineering, natural, biological and social sciences. As an engineer and a scientist who has straddle various scientific fields, I believe in the importance of real world problems as an inspiration for the development of mathematical theory, not for its own sake, but as a means to solving important practical problems and providing practical decision solutions. I hope to add Complexity Theory to my intellectual tool box and possibly use them in my research in a near future. I look forward to meeting you all in June and do not hesitate to email me at alhaji_dot_cherif_at_asu_dot_edu.

==Questions:==

=1. What are your main interests? Feel free to include a "pie in the sky" big idea!=
I am generally interested in the application of dynamical system theory (deterministic, stochastic and crypto-deterministic) to interesting problems in a variety of fields (Engineering, social and biological sciences). In the area of mathematical epidemiology, I am interested in incorporating socio-behavorial dynamics into models. I am also interested in sociopolitical dynamics, political violence and aggression (civil wars, terrorism).

=2. What sorts of expertise can you bring to the group?=
As an engineer, mathematician and Africanist who has been exposed to various fields, I can bring some of the transferable skills I have learned over the years. I can contribute to the knowledge of dynamical systems, bifurcation theory, asymptotic and perturbation methods in Deterministic and Stochastic Differential Equations, Dynamical Programming and Control Theory, Mechanics and other engineering and applied mathematics skills, and most importantly modeling skills.

=3. What do you hope to get out of the CSSS?=
I hope to learn more about complex adaptive dynamical systems and to establish possible future collaborations with other students and SFI scholars.

=4. Do you have any possible projects in mind for the CSSS?=
I will like to continue on some of the projects I have recently been working on. Possible projects are:
[a] dynamics of radicalization (terrorism) in prison, of Islamic diaspora
[b] coevolution of colonial and traditional institutions in Africa and its impact on current African political conundrums.
[c] polity distributions
[d] mathematical modeling of Gangsterism or
[e] generalization of the 3 theories I developed in my undergraduate thesis: sociopolitical bundle and reliability, and socio-econo-demographic theories and their applications in economics, politics and state collapse.
[f] and I am open to other ideas and/or modification of then above and other previous research questions I was interested in but never got around doing them.

Alhaji Cherif

2009-05-31T02:56:10Z

Acherif:

==Brief Bio==
Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus (under Castillo-Chavez) at Arizona State University. I hold a BS in engineering from Cornell University and was trained in Theoretical and Applied Mechanics (under Strogatz), and Electrical Engineering (udner Belina) with minors/certification in Applied Mathematics (Rand), African Politics, African American Studies (Assie-Lumumba), Mechanical Engineering-unofficial (Phoenix). I spent most of my undergraduate years doing research in the above areas and in nanobiotechnology (NEMS/MEMS, microfluidcs, biosensors), composite materials, reliability theory. I have worked on a variety of research problems, including the development of micro-fluidic systems also known as lab-on-chips (biosensors and micropump, NEMS/MEMS); theoretical modeling of electrophoretic deposition of thin-films; mechanics of fibrous composites; and dynamic model of the flight of butterfly, socio-political dynamics of instability, political reliability theory. I have a short interest horizon and broad interests in interdisciplinary applied mathematics, specially when applied to physical, engineering, natural, biological and social sciences. As an engineer and a scientist who has straddle various scientific fields, I believe in the importance of real world problems as an inspiration for the development of mathematical theory, not for its own sake, but as a means to solving important practical problems and providing practical decision solutions. I hope to add Complexity Theory to my intellectual tool box and possibly use them in my research in a near future. I look forward to meeting you all in June and do not hesitate to email me at alhaji_dot_cherif_at_asu_dot_edu.

==Questions:==
=1. What are your main interests? Feel free to include a "pie in the sky" big idea!=
I am generally interested in the application of dynamical system theory (deterministic, stochastic and crypto-deterministic) to interesting problems in a variety of fields (Engineering, social and biological sciences). In the area of mathematical epidemiology, I am interested in incorporating socio-behavorial dynamics into models. I am also interested in sociopolitical dynamics, political violence and aggression (civil wars, terrorism).

=2. What sorts of expertise can you bring to the group?=
As an engineer, mathematician and Africanist who has been exposed to various fields, I can bring some of the transferable skills I have learned over the years. I can contribute to the knowledge of dynamical systems, bifurcation theory, asymptotic and perturbation methods in Deterministic and Stochastic Differential Equations, Dynamical Programming and Control Theory, Mechanics and other engineering and applied mathematics skills, and most importantly modeling skills.

=3. What do you hope to get out of the CSSS?=
I hope to learn more about complex adaptive dynamical systems and to establish possible future collaborations with other students and SFI scholars.

=4. Do you have any possible projects in mind for the CSSS?=
I will like to continue on some of the projects I have recently been working on. Possible projects are:
[a] dynamics of radicalization (terrorism) in prison, of Islamic diaspora
[b] coevolution of colonial and traditional institutions in Africa and its impact on current African political conundrums.
[c] polity distributions
[d] mathematical modeling of Gangsterism or
[e] generalization of the 3 theories I developed in my undergraduate thesis: sociopolitical bundle and reliability, and socio-econo-demographic theories and their applications in economics, politics and state collapse.
[f] and I am open to other ideas and/or modification of then above and other previous research questions I was interested in but never got around doing them.

Alhaji Cherif

2009-05-31T02:52:15Z

Acherif:

Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus (under Castillo-Chavez) at Arizona State University. I hold a BS in engineering from Cornell University and was trained in Theoretical and Applied Mechanics (under Strogatz), and Electrical Engineering (udner Belina) with minors/certification in Applied Mathematics (Rand), African Politics, African American Studies (Assie-Lumumba), Mechanical Engineering-unofficial (Phoenix). I spent most of my undergraduate years doing research in the above areas and in nanobiotechnology (NEMS/MEMS, microfluidcs, biosensors), composite materials, reliability theory. I have worked on a variety of research problems, including the development of micro-fluidic systems also known as lab-on-chips (biosensors and micropump, NEMS/MEMS); theoretical modeling of electrophoretic deposition of thin-films; mechanics of fibrous composites; and dynamic model of the flight of butterfly, socio-political dynamics of instability, political reliability theory. I have a short interest horizon and broad interests in interdisciplinary applied mathematics, specially when applied to physical, engineering, natural, biological and social sciences. As an engineer and a scientist who has straddle various scientific fields, I believe in the importance of real world problems as an inspiration for the development of mathematical theory, not for its own sake, but as a means to solving important practical problems and providing practical decision solutions. I hope to add Complexity Theory to my intellectual tool box and possibly use them in my research in a near future. I look forward to meeting you all in June and do not hesitate to email me at alhaji_dot_cherif_at_asu_dot_edu.

==Questions:==
1. What are your main interests? Feel free to include a "pie in the sky" big idea!
I am generally interested in the application of dynamical system theory (deterministic, stochastic and crypto-deterministic) to interesting problems in a variety of fields (Engineering, social and biological sciences). In the area of mathematical epidemiology, I am interested in incorporating socio-behavorial dynamics into models. I am also interested in sociopolitical dynamics, political violence and aggression (civil wars, terrorism).

2. What sorts of expertise can you bring to the group?
As an engineer, mathematician and Africanist who has been exposed to various fields, I can bring some of the transferable skills I have learned over the years. I can contribute to the knowledge of dynamical systems, bifurcation theory, asymptotic and perturbation methods in Deterministic and Stochastic Differential Equations, Dynamical Programming and Control Theory, Mechanics and other engineering and applied mathematics skills, and most importantly modeling skills.

3. What do you hope to get out of the CSSS?
I hope to learn more about complex adaptive dynamical systems and to establish possible future collaborations with other students and SFI scholars.

4. Do you have any possible projects in mind for the CSSS?
I will like to continue on some of the projects I have recently been working on. Possible projects are:
[a] dynamics of radicalization (terrorism) in prison, of Islamic diaspora
[b] coevolution of colonial and traditional institutions in Africa and its impact on current African political conundrums.
[c] polity distributions
[d] mathematical modeling of Gangsterism or
[e] generalization of the 3 theories I developed in my undergraduate thesis: sociopolitical bundle and reliability, and socio-econo-demographic theories and their applications in economics, politics and state collapse.
[f] and I am open to other ideas and/or modification of then above and other previous research questions I was interested in but never got around doing them.

Alhaji Cherif

2009-05-07T00:58:30Z

Acherif:

Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus at Arizona State University. I hold a BS in engineering from Cornell University and was trained in Theoretical and Applied Mechanics, and Electrical Engineering with minors/certification in Applied Mathematics, African Politics, African American Studies, Mechanical Engineering (unofficial). I spent most of my undergraduate years doing research in the above areas and in nanobiotechnology (NEMS/MEMS, microfluidcs, biosensors), composite materials, reliability theory. I have worked on a variety of research problems, including the development of micro-fluidic systems also known as lab-on-chips (biosensors and micropump, NEMS/MEMS); theoretical modeling of electrophoretic deposition of thin-films; mechanics of fibrous composites; and dynamic model of the flight of butterfly, socio-political dynamics of instability, political reliability theory. I have a short interest horizon and broad interests in interdisciplinary applied mathematics, specially when applied to physical, engineering, natural, biological and social sciences. As an engineer and a scientist who has straddle various scientific fields, I believe in the importance of real world problems as an inspiration for the development of mathematical theory, not for its own sake, but as a means to solving important practical problems and providing practical decision solutions. I hope to add Complexity Theory to my intellectual tool box and possibly use them in my research in a near future. I look forward to meeting you all in June and do not hesitate to email me at alhaji_dot_cherif_at_asu_dot_edu.

Alhaji Cherif

2009-05-07T00:55:23Z

Acherif: New page: Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus at Arizona State University. I hold a BS in engineering from Cornell University and ...

Hi everyone, I am a first year doctoral student in Applied Mathematics with Mathematical Biology focus at Arizona State University. I hold a BS in engineering from Cornell University and was trained in Theoretical and Applied Mechanics, and Electrical Engineering with minors/certification in Applied Mathematics, African Politics, African American Studies, Mechanical Engineering (unofficial). I spent most of my undergraduate years doing research in the above areas and in nanobiotechnology (NEMS/MEMS, microfluidcs, biosensors), composite materials, reliability theory. I have worked on a variety of research problems, including the development of micro-fluidic systems also known as lab-on-chips (biosensors and micropump, NEMS/MEMS); theoretical modeling of electrophoretic deposition of thin-films; mechanics of fibrous composites; and dynamic model of the flight of butterfly, socio-political dynamics of instability, political reliability theory. I have a short interest horizon and broad interests in interdisciplinary applied mathematics, specially when applied to physical, engineering, natural, biological and social sciences. As an engineer and a scientist who has straddle various scientific fields, I believe in the importance of real world problems as an inspiration for the development of mathematical theory, not for its own sake, but as a means to solving important practical problems and providing practical decision solutions. I look forward to meeting you all in June and do not hesitate to email me at alhaji_dot_cherif_at_asu_dot_edu.