Complex Systems Summer School 2013-Projects & Working Groups

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Complex Systems Summer School 2013

SFI CSSS X-Prize Idea!

Every summer, PhDs & post-docs descend on SFI and learn by doing through amazing projects, so here's an idea -- maybe more of a challenge -- for future SFI students, faculty & staff (the idea would require some additional SFI fundraising by faculty & staff). What if SFI set up a project competition with a reward, like an X-Prize, for some healthy competition & real reward? The winning group could receive a monetary prize -- perhaps funds to cover conference attendance & travel, for example to present 2013 SFI CSSS work at the Complex Systems Society in Barcelona, Spain:

SFI X-Prize could be fun! - – Kristen Honey

mess-generating meta-project

I just figured I'm extremely good at adding noise into people's projects! If anyone's interested, I'd love to join any group discussion to contribute entropy... -Mengsen XD

Modeling self-organization of "arbortron" by cost-driven growth in spatial networks


Formation and structure of ramified charge transportation networks in an electromechanical system

Emergence of hierarchy in cost-driven growth of spatial networks

-许晏(XU Yan)

I'm in! -张萌森 Mengsen

I am in (listening silently). However, I would love to see a game theoretic explanation for the race condition. -Vishwa


I have had an interest in MOOCs and their potential role in the future of education/ as a means of providing education to non-traditional students. This has been in the back of my mind for some time, but I have no experience dealing with real social data (including how to access it) and was hoping other people might be interested. I did a brief search and found that it may be possible to use twitter data. The questions I am interested in are where MOOCs are popular (globally), how it spread, what kind of topics, etc. Anyone else interested?


An automated cluster analysis of the content of Coursera catalogs would be quite straightforward. It could help get a sense of meaningful clusters of content regarding how courses present themselves, and also what categories are salient for MOOC's. --Manish

Food webs

It seems like there is A LOT of interest in food webs amongst this group. Since there are so many people, maybe it would be worth coming together and dividing into smaller groups based on the questions (there are many!) people are interested in addressing and skills. I think it would good be to have some coordinated effort, for efficiency and productivity. We could divide questions, or parts of bigger questions, or approach the same questions, using different methods and then compare results and try to understand the similarities and differences. Maybe we could meet at 3 pm today (Wed) or at dinner.


A Midsummer Night's Project: Comedy and Tragedy in Shakespeare

Max Kleiman-Weiner and I have a corpus of all the Shakespeare plays and have been talking (with many of you, too!) about building a system to automatically classify Shakespeare plays as either comedy or tragedy. A simple approach would be to just use a bag of words to see if the plays can be classified based on lexical content alone. We have also been discussing building social networks for each play based on which characters interact (i.e., who speaks before and after whom). We suspect that the social network structure of a comedy and tragedy should look different from each other. Do characters in comedies have more connections? Do tragedies and comedies start off the same and then have networks that evolve differently over the course of the play? Or are they different from Act I? This may be an opportunity to look at how social networks change over time in a specific domain. We could also look at the different roles of men and women in the social networks of the plays.

-Kyle M

This sounds interesting and doable. I've done a work on author attribution of opinion articles before using only syntactic (function words like prepositions and articles, punctuations), structural (sentence length, paragraph length) and lexical (other non-specific words) features. It'd be nice to see if the same thing will work for this. I think it will be more fascinating to explore the social network approach and we should definitely give it a shot if there's still time.


Stories are always good. Incidentally, are you familiar with Kurt Vonnegut's commentary on the shapes of stories? If not, it's not hard to find. If so, there may be something to be said for attempting to determine whether a story conforms to more specific tropes (e.g. Cinderella stories and "The Chosen One"). Moreover, if enough distinct patterns do emerge out Shakespeare's plays, it may prove interesting to compare these to other stories (e.g. novels available on Project Gutenberg).


There is now a DropBox with the corpus and some preliminary Python scripts - so come find me if you're interested in getting linked in.


See project page for this here: [1].

Recursive Agents

A common theme that I've seen in complexity is that you can represent many different systems (cells, agents, civilizations, for example) with more or less the same rules. I'm wondering if anyone would be interested in modeling this? Essentially we develop a recursive rule set: agents' behavior are based on a set of their 'inner agents,' which also follow similar rules.


Global Emergent Risk

In the current phase of globalization, networks in logistics, travel, finance, disease, energy, and ecology are growingly increasingly interconnected on a global scale. Hence, situations arise where mortgage defaults in the US bring down the Icelandic pension system, and a volcano in Iceland disrupts global air traffic. Given the scale of interconnectedness, can complexity science help develop a sense of how small perturbations in one global network might cascade into large unforeseen failures in another network? I'm wondering if we can help build a more generic grammar for describing interconnected networks and the risks posed by such systems. Would it be possible to mitigate risk at smaller local levels of scale, or do small corrections of local risk inevitably cascade into larger risks that we lack the ability to respond to (think wildfires in the Southwest where more frequent small fires would help reduce the chance of large scale fires that cannot be controlled.) I'd love to talk with anyone interested in ecology, globalization, risk, and network modelling.

My advisor at Princeton is already funded for a three year interdisciplinary symposium on Global Emergent Risk, so if people are interested, we have resources to carry on a larger research engagement.

-- Manish

- I don't know if large risk cascades are inevitable, but these are timely questions. I have colleagues at the Global Catastrophic Risk Institute who are also interested in this topic. I'm not sure what building a "more generic grammar" means, but the cross-impact balance method is pretty generic. Hopefully you can make our tutorial! Our group is kicking around methods for a project at the moment and have not decided on research questions, so perhaps we can join forces. -- Vanessa

- To clarify, what I meant by "generic grammar" is some way to describe a set of networks and cross network dependencies that can be decoupled from the content of the network. We could use this language to describe interconnections between hypothetical logistics, travel, financial networks for example, and then use simulations to understand the conditions where risks might be amplified and/or jump across from one network to another. -- Manish

- I'd love to join discussions on this as I agree that there's a need to start thinking about network of networks and not just networks in isolation. The paper by Gao, Stanley et al. on the robustness of a network of networks might be useful. -- Cheryl

- I'm in . Ren

- I'm thinking if we can possibly use a self-organized criticality (SOC) type of model to trigger a cascade of failures in a network of networks. Not sure though if it has been done. Cheryl

- Thought this paper may be useful for your work [2] - Vishwa

Alcohol Consumption and Language Fluency

There's a interesting thing about me and some of my friends who speak a foreign language. There seems to be a kind of non-linear curve that depicts my blood alcohol concentration and the fluency of my speaking English (Chinese is my native language). For example, usually after one beer, I start to talk faster, and semantically relevant words pop up in my mind more fluently or spontaneously. Or say, I express the same idea with less stuttering and in shorter time. But after some point, i.e. 32 oz of beer, even it might still sound fluent, regarding the physical property of the speech, the grammatical structure of the sentences start to break down. Ultimately, i.e. half bottle of rum, I only repeat 2-3 very short sentences/phrases independent of the presence or the identity of my audience(s) (i.e. murmuring in English for hours to a group of Chinese friends).
We know that alcohol can influence our behavior by, among other things, binding to certain neural receptors (like GABAa?). I just thought it might be interesting to model how alcohol intake influences the dynamics of language production via regulating neuron signaling. I'm also wondering about what it might imply about our brain dynamics when we're silent.
Here's a short survey [3] if you like to let me know your experience on this topic. Welcome to talk to me if you find this interesting, or silly, or you simply want to correct my grammar. Thanks!
-Mengsen Zhang

@Mengsen I'd be very interested in doing some empirical work on this topic. -Max Kleiman-Weiner

I've experienced this same thing with Spanish, Bahasa Indonesia, and playing billiards/pool. Sadly, my math skills do not improve with drinking, and math could arguably be considered a language. This is a creative topic with solid scientific underpinnings that nicely lend themselves to complex systems modeling, I suspect. Fun! – Kristen Honey

On a related note:


How the ocean can help us heal complex chronic disease -- Matrix Matters!

The human body is its own ecosystem -- much like the ocean -- with resilience, hysteresis, synergistic properties, and multi-system dynamics that depend on matrix conditions. Can we use our understanding of the ocean and ecosystems to help us heal? Can we model different scales -- an ocean, a human body, and a microbial community -- to explore ecosystem/human/microbial health in a holistic context that depends on shared key elements like flow & trace minerals as building blocks for function? As one example to think about, chronic illnesses like HIV/AIDS or Lyme disease disrupt the immune system and human body functions (e.g., methylation pathways, detox pathways), preventing optimal function that weakens the human body and makes it vulnerable to other infections. It’s a downward spiral of negative feedbacks, analogous to a backed-up, atrophying ocean or estuary that causes fish kills, destroys coral reefs, etc... analogous to microbial communities that shift when environmental/matrix condition change. I suspect our knowledge of the ocean & large ecosystems, which we can see and visualize, can inform new thinking about system dynamics for health & recovery at the scale of a human body and at the scale of individual microbes & microbial communities...

Anyone else interested? – Kristen Honey

- This sounds like a really nice idea. It would be interesting to understand how the disease-mediated degradation of immune/metabolic networks (the loss or alteration of edges and nodes?) affects the response of these networks to further perturbations (e.g. asymptotic stability and resilience, transient reactivity, cascading effects of node loss, etc.). I have little knowledge of the medical literature, but I am experienced in ecological network analysis including information theoretic analyses of weighted food webs. -- Ashkaan

- I liked the idea very much. In fact I am looking at similar issues related to management of ecosystems which display hysteresis and regime shifts. Here is some pertinet information related to shallow lakes and its complex dynamics. Your idea of scaling down to human level is interesting -- Vishwa

- Sounds interesting. I have experience in microbial oceanography, but not so much with infectious disease. I'll be there for the dinner meeting. - Jody Wright

FRIDAY 6/7 BEER & WINE BRAINSTORMING ACCOMPLISHED!: Let's convene tonight after SFI, 8:30ish or 9:00ish, Friday June 7th, downtown for some DOWN TIME! We've already put our brains together & focused project scope. Now it's beer & wine & fun in Santa Fe with everyone! :)

Here is the new project page [4]

== Join us for project discussion & brainstorming ideas, Wednesday June 5th dinner at 5:00. Everyone welcome! ==


I'd love to play around with the new Global Data on Events, Location and Tone (GDELT) dataset, which has 200+ million timestamped and geocoded political events. Here's a writeup of it in Foreign Policy -- David

Research Network Formation

I'd be interested in collecting some data from CSSS attendants. Some kind of way to study social network formation. -- Todd

Perhaps we could collect survey questions people might be interested in looking at in a Google Doc? -- Molly

My new crazy idea, inspired by these guys, is doing something with computer vision. Maybe there's a way to photograph sitting arrangements and extract data from that? -- David

Project meeting after the lab Wednesday 6/5 for all those interested.

Self-consistent networks for socio-economic institutions (CIB analysis and Markov chains)

Project update: Some core members of the group are starting to assign tasks for the project. If some folks are still shopping for projects and want to check out what we're up to, please visit our project page. --Vanessas 02:12, 6 June 2013 (UTC)

Pablo and I started to discuss a project where we could use cross-impact balances (CIB) to investigate the implications of alternative hypotheses for interrelationships between various socio-economic/political factors. We began discussing this from the perspective of testing competing political economic theories to see what types of institutions (e.g. styles and stability of governance) would be self-consistent according to the theories. However, I would be open to other topics, including research questions inspired by GDELT. If there is interest to learn more about the CIB technique, I could put together a tutorial. -- Vanessa

- There's a paper written by Brian Arthur here at the SFI that might help us frame our topic. Its called Complexity Economics. Basically sets the "rules" for thinking about economics in a whole different way. It is a very good starting point so we don't go any further wasting time taking into account economic models that are vague, non-accurate and out of date. Pablo [?]

Elaborating more on the idea. If we focus on human action as the essence of culture. Understanding human action as the use of beliefs, attitudes and resources (which are scarce) pursuing a state of higher satisfaction. And culture as the sum of all beliefs, attitudes and unintentional consequences of the human action. We can state that the emergence of socio-econmic/political factors are the unintentional consequence of intentional individual actions that at the same time affect the way people act, in what they believe and what attitude they'll have toward the satisfaction of their needs. (Theres a loop between human action and culture - culture affects human action and human action affects culture and so forth) Some "institutions" will emerge as a consequence of human action but not human design. That is one of the characteristics of a "good" type of institution. (e.g. Money, language, private property, contracts, a certain type of government) and it would be interesting to test the robustness of that spontaneously emerged institution against human designed institution who's robustness is just that is law enforced. I dunno If you get my point? Compare the qualities of spontaneously emerged institutions against human designed institutions. Its more or less comparing spontaneously emerged institutions (no leader needed to coordinate like the birds flocking) vs. human designed institutions (leader needed to coordinate al human actions).

== A tutorial on cross-impact balances and Markov chains is scheduled for Wed. June 5 at ~4:15. Everyone is welcome, even if your project interests lie elsewhere. ==

Genetic algorithms to evaluate network formation or real-world data

I have an ill-defined, wacky idea to possibly use genetic algorithms to evaluate the formation of networks as either following preferential attachment or homophily (aka similarity) rules. This short Nature paper looks at the debate between preferential attachment and similarity/homophily dynamics. I don't have a clear idea of what this would look like, but I think it might be fun to think about ways to use genetic algorithms to solve network problems. Talk to me if you think this remotely interesting and we can evolve an idea together? -- Molly

Another possibility would be using genetic algorithms or attachment algorithms to compare to models of real-world data to understand how these networks likely formed and predict future edges.

Molly, I would like to investigate using GA to create an organizational network structure (think org chart or military chain of command or even project groups at the CSSS) and then compare it to existing structures. Let's talk! -John L

Some people were also talking about co-evolution of a network and an attacker that disconnects nodes or edges. -- David

Everyone interested in this and related meet Wed 6/5 at 4pm in main lecture hall - group of folks interested in studying network evolution/fitness/information/energy spread meeting. --Molly

Seem to be two branches of this:
1) network evolution toward a predefined fitness function (energy, information efficiency, etc.) via genetic algorithms - what structures evolve?
2) co-evolution of a network and attacks of different forms - what structural changes take place? which structures are robust to attacks?

1) network evolution toward a predefined fitness function

2) co-evolution of a network and attacker

Interested members: Elena, Andrea, Stephan, Bruno, Johannes, David M., Holly, Mauricio

We're thinking about co-evolving a network and an attacker agent. The network's fitness is robustness (to be defined later) to attack, and the attacker's fitness is disruption of the network. Both also need to be subject to some sort of resource constraint -- otherwise the optimal network is fully-connected, and the attacker's optimal strategy is just disconnecting all of the nodes.

Some background reading:

Tools: Probably Java or Python. We need something that has network metrics already, so we don't need to code them ourselves. Possibly use a GA package / library as well.

I created a page for the project and started to discuss some of the issues: NetAttac

Caribou Management Dynamics

This project would model caribou management dynamics in a prototype NW Alaska community during a caribou shortage. Agents in the model would be informed by data from household subsistence surveys and from management history. The goal would be to evaluate the abilities of different management strategies to achieve biological harvest goals while maximizing economic efficiencies in the community. This is a real-world problem with near-term applications. Caribou cycle on 30-to-50 year periods. The Western Arctic Caribou Herd is currently in decline. During the last caribou “crash” in this region, the state management system attempted to reorganize caribou production, which generated considerable political and social disruption, precipitated widespread passive resistance among Native peoples, and left a legacy of contempt for both management (among some Inuit) and for Inuit hunters (among some sport users). The hope is to reduce conflicts during the expected nadir of the population. Comments and cooperators welcome! Jim

Neat topic with data, I assume! I'm interested & would love to talk more. – Kristen Honey

Evolving synchronized flashes in fireflies, and other polymorphic traits

I was thinking about how some, but not all, species of fireflies can synchronize their flashes, as was mentioned in both lectures today (June 4). The mechanism is fairly simple, it seems, so we should be able to evolve it using a simple genetic algorithm, right? This is only half-baked at the moment, and I haven't checked to see if it has been done already, but I thought it would be neat to explore the space around these biological phenomena. More of a fun project than a serious "lets publish this!" type of project. Bonus points if we can work some neural network stuff into it. Bryn_Gaertner. -- EDIT -- Upon further discussion with Rebecca and Holly, we would like to extend this. Still using genetic algorithms, under what conditions can we evolve a stable polymorphic trait in a population, and under what conditions does a monomorphic trait evolve? This is applicable for traits in a population, but we would like to use the same model to evolve (for example) multiple or novel sensory modalities in a species, number of members in a food web, predation strategies, etc. Interested? Find us at lunch!

How do historic facts collapse into written history?

Let's begin with a nice example: Gilgamesh, the fifth king of Uruk, decided to gather together some stories that local tribes and surrounding cultures had been telling for years, along with things that previous kings had done. This became the Epic of Gilgamesh. Someone later does a cover of the original book with some new contributions and turns it into what nowadays is the bible and the torah. Another remake of the tale turns these books into the quran, and until today... you know the rest of the story already. Peer reviewed quality, just like Nature or PNAS.

It might be interesting to study how history goes from facts to a written, definitive form which is not (and maybe cannot be) completely faithfully to the actual events. There is huge room to use, for example, models of agents that contribute to form a History with pieces of information that sums up, sometimes with contradicting versions, sometimes with hidden interests, etc etc. Furthermore, we have a great tool in the wikipedia!! We can track, for example, how many changes are made on different entries over time. We can check whether there are some generalities, how the number of edits depends on the time gone after the historic event, maybe we can quantify how successive stories differ from each other and whether there are turning points that dramatically change the whole thing...

So this is the general framework. I think this is a very exciting topic and I'd be glad to talk about this with anyone!! Just contact me! -- Luíño

So this is the book I told you about: Pablo_Galindo

You may be interested in related idea about diversification of religion: -- Cesar

You might be interested in this network approach to history [5] -- Andrea

Wiki site here!

Meta Food Webs

I'd like to throw out an idea I've had for a while: Most animals use space in very important ways -- predators encounter and consume prey in both 2D and 3D environments, birds and fish migrate across continents in search of resources and mates, and plant pollinators fly or walk from flower to flower, in turn providing an indispensable economic service to humans. The study of food webs attempts to understand how networks of species that eat each other persist in the face of (sometimes constant) external perturbations. Yet, network-level food web studies seldom address the dynamics of animal movement, and I see this as a fundamental shortcoming in our understanding of nature. Recently, scientists in fields like computer science, physics and neurobiology have begun to model and explore multi-level or multiplex networks -- networks of nested networks. This seems like a reasonable candidate for the theoretical study of multiple food webs that are linked by spatial networks of animal movement. One preliminary question that comes to mind: How do the number of "mobile" species and the "speed of movement" alter important dynamical properties of complex food webs at larger spatial scales (i.e. at the meta-food web scale)? I am not dead set on answering this question, and I look forward to gaining insight from scientists who study other types of networks. I'm also not set on the multiplex network framework. Potential alternatives that come to mind are IBMs, PDEs on graphs or integrodifference equations. I look forward to any suggestions or bright ideas! -- Ashkaan

Very cool topic. Definitely interested & would love to talk more. – Kristen Honey

(Evolution of) Aging

Sorry that this one is a bit long. I'd like to brainstorm with anybody interested to see if there could be a viable project in the following direction.

Let me first define aging: deterioration that happens as an organism, e.g. a human being, gets physiologically older, eventually leading to increased mortality and/or decreased fecundity.

Some background into the 'classic' theory: Evolution is about getting to be there in the future, that is, you and/or related organisms, for instance offspring. Increased mortality and decreased fecundity as such are clearly unfavorable to getting to be there in the future. Then why could it evolve? Well, evolution tends to become less sensitive to anything happening to an organism as time progresses, because events that take place at some point in time can affect only events that are future to that event. All offspring that an organism already has at some point cannot be affected anymore, and this is a non-decreasing function of time. This can be formalized, and I'd be happy to write down the math if anybody wonders.

It has, however, limited value to theorize too much about age-specific events, while in fact events at different ages are tied together in pathways of causality, dynamic change and so on; age per se is not a cause of anything, and changes at some age do not happen independent of changes at other ages. There are a number of sufficient arguments why the 'age-specific' picture does not capture this reality. I'm skipping these arguments for now, but ask if you are interested.

We have two things that matter for the evolution of aging. 1. The declining sensitivity of fitness to age-specific changes (of some standardized magnitude). 2. The fact that there are constraints that make that what happens at age x is not independent from what happens at the ages in its neighborhood. Thus, the trick is to figure out what the constraints are - this is where complexity may come in - and to combine these with the effect on fitness that age-related change has. The effect on fitness may be solved analytically, but there are various reasons why computation may be preferable, specifically the not always realistic assumptions that are necessary to allow for analytical solutions.

Mechanistically, people tend to think about aging in two ways. The first is that aging is caused by the accumulation of damage. If this damage were all repaired, aging would not occur. People then try to think of reasons why repair would be imperfect. The second way to think about it is as a gradual loss of robustness/control, sometimes in the context of reliability engineering (is anybody familiar with that?). Beautiful medical example: old people need more insulin to process a standardized dose of sugar, and their regulation shows more peaks, especially upward peaks, than that of young people, who tend to need less insulin to process the standardized dose of sugar. Of course, if you consider loss of control as a type of damage, the two are the same, but the distinction is perhaps helpful because the way people tend to think about damage is not in a dynamical way, but just as protein aggregates sitting in the brain, inhibiting the function, cartilage that looses its suspension, etcetera. Again of course, both may influence each other.

Now toward a project proposal. If I think about how complexity may (in part) determine physiological constraints, I think of the following. Usually people tend to think of repair being limited by available energy. But to repair something, the body needs to have available somewhere the information necessary to restore the initial state, and use that information at the place where the damage has occurred to be able to repair. This is where I hypothesize complexity comes in. The necessity of different components of the body to interact may put constraints on repair other than just energetic, it is also a question of whether the energy can actually be used for the repair (flow through the system in the necessary way). That may require space, a certain chemical environment, hormonal setting etcetera that may be incompatible with the proper function of an organism. (Can you repair a car when it is driving?) Also in the 'control/robustness thinking', you have to get back to the original situation to avoid aging. Is that compatible with the best evolutionary outcome?

These are just some ideas I'm throwing at you, as you see it's not perfectly fleshed out yet, which is good, because it should benefit from your perspective. So anybody interested, please let me know!

Cheers, Maarten

Neat ideas & I welcome more discussion around these topics. Aging is cool from the academic science side, if not from the personal experience side, lol. It seems to me that these same models/concepts for aging also apply to loss of functionality from chronic illness... illness expedites aging? – Kristen Honey

Quantifying Synchrony in Dynamics Occuring on Networks

Recent work has focused on developing information theoretic measures for quantifying directed information transfer, with particular applications to social media. These metrics are motivated by the work being done in theoretical / computational neuroscience on the analysis of spike trains. To do this analysis, the behavior of users on a social media platform like Twitter are treated as point processes, where we only keep track of when a tweet occurs, and ignore its content. That is, we treat a user's behavior over time as 'spikes.' Despite the simplicity of the approach, it was found to be successful in identifying key actors within real social networks.

I am interested in applying a similar methodology, but using a different measure of synchrony motivated by computational mechanics. This method seeks to learn the hidden states that generate a user's behavior (very much in the flavor of a Hidden Markov Model, but with a few key twists), and then considers the mutual information between the state sequences of the two users.

A first step for this project would be implementing the methodology proposed by Shalizi, et al., on the toy model proposed by Steeg, et al.

If that is successful (and completed quickly), I have a data set (network connections and behavior) of fifteen thousand Twitter users collected over a three month period. We are interested in using this approach to identify dynamical communities (not only users who are connected, but users who behave in synchrony) within the social network. This takes us beyond typical structural community detection that has had great success in the past decade.

These approaches should work with any sort of dynamics occurring on top of a network-type structure, so if you have a different system you would like to use as a test case, I would be very interested to hear about it!

Dave Darmon

A spin off?

Sorry for editing in your proposal, Dave, but I wanted to comment an idea I have been interested in for a time now. There is this great technique used in neuroscience to pin down the most effective time-course excitation that a neuron can get so that it fires. It basically averages the input a neuron had been getting before each of its spikes. You can find a thorough description in the very popular Dayan & Abbott book on neuroscience. Since you mention the abstraction from tweets to spikes, I would be very interesting in applying such neuro-inspired analysis to this social interactions. Neuroscience has got many more techniques, so I do not pose it as a closed matter. Just open for discussion, but seems like everybody is sleeping by now ;) -- Luíño

You're referring to the spike-triggered average? I hadn't thought of that!

All of the work I've done has only considered a single user's time series for prediction. I would certainly be interested in looking at how including the 'inputs' to the user (or at least the inputs that occur on Twitter) impact this process. The spike-triggered average seems like a great first start. Computational mechanics also has some tricks in its toolbox that could be used for this sort of input-output problem. -- Dave D

comment on spin off

Hi, I used spike-triggered averaging (and higher-dimensional extensions, which might be useful in your case) during my thesis, so I would be interested in talking about the application to other systems. Rebecca

Great! The folks interested in this project will be meeting during the 3pm time slot today. We don't have a formal meeting place planned: the best I can say is to look for me!

Dave D

Hi there!! Is this group still meeting? I remain interested in many aspects of the project. Would you like to talk maybe tomorrow at SFI or afterwards? -- Luíño

Yes, we are still meeting! I'm hoping to put together a project page soon. We should definitely meet at during the project block at SFI. Dave D

Project Page

The project page may be found here: [6].

Energy resources supply patterns from biological systems to humans

My idea is to start a brainstorming, it is nothing more than some disjoints thoughts!!! And many questions without an answer so far!! :-)

I would like to investigate how biological systems obtain their energy requirements. Are there patterns or network structure that evolution has developed and that are efficient for animals/plants? Can we replicate these structures/networks/patterns in the way we (humans) obtain our energy? Can we learn something from the evolution of the energy provisioning of other species? Is that feasible? My primary idea (given my research bias) is to then apply the findings to the structure of the electrical system. Is the current centralized generation and long distance distribution something that appears in nature? Is there a more efficient way that emerged from evolution in biological context that we can use for future provisioning?

We can apply this not only to electrical systems but more in general to the way we use our resources.

Anyone interested with ideas, feedbacks, thoughts? – Andrea

Ecological networks are, indeed, finely structured both topologically and energetically. I'd very much like to talk to you about this idea in more detail. -- Ashkaan

Definitely interested & would love to talk more. – Kristen Honey

Cellular morphogenesis - The evolution of organisms' shape

I am interested in genetic design, not as much the modifications of already existing plants and animals but rather the capabilities of from-the-ground-up design of completely new organisms. One of the most basic question when it comes to multicellular organisms is how they end up with their particular shape and how it is a product of cell growth, membrane adhesion, chemical signalling etc. I suggest a project where we explore what types of shapes can emerge in simple cell growth models and how the shape can be controlled by tuning the organisms genes, the interactions. When we have something running, one interesting continuation would be to apply genetic algorithms on the growth parameters and se what happens with the emerging shape if we for example assign high fitness to high surface area but low volume, high moment of inertia or maybe concentric shells of different cell types. We can also compare our results with simple, real organisms and se if nature found similar solutions (shapes).

There are different cellular models we could use, most reasonable would be a cellular potts model (B) or a vertex dynamic model (D) [7], depending on what type of details we want to include. I suggest we start out with 2D simulations.

I also believe this project can steer into many different directions, so if you like parts of the premise and have ideas on other directions we could take this, say hi.

Max Planck instutute with a group on this subject: [8]

A vertex dynamics model investigating how a specific cell morphogenesis could occur: [9]

Oskar email me at oskarxvi at gmail dot com

How big can a city be?

Here is the project page! --Vanessas 01:36, 18 June 2013 (UTC)

West et al. have discovered striking—and universal—patterns in the way cities scale with size (see West's Ted talk, short Nature paper, longer PNAS paper). Do these scaling laws allow us to predict how big a city can be?

Here's a motivating analogy. The mass of an animal grows scales the cube of its size L, but the cross-sectional area of its leg bones scales only like the square of L. This implies that bigger animals must have bulkier leg bones in order to sustain their own weight. (Think of the legs of a mouse versus the legs of an elephant.) Since the bones can never get bigger than the animal itself, this immediately tells you that land animals cannot be arbitrarily large: they must have a maximum size. If you plug in the numbers and estimate this maximum size, you find a value consistent with the largest known dinosaurs. In fact, with similar reasonings—which were discovered by Galileo, by the way—you can easily find how tall trees can be, how high animals can jump, etc [10].

Now, to run a similar argument for cities, we should understand what constraints would limit their size (the equivalent of "the legs of an animal can never be larger than the animal itself"). These constraints may be technical, social—I'm not sure. (Crimes are perhaps an example. West et al. show that the number of crimes committed in a city grows faster than the number of inhabitants. Clearly then, at some point the likelihood to get shot the next day will get too high, and people will start leaving the city.) I wonder if the social scientists among us have any insight about such constraints, and whether we can actually come up with a prediction for the maximal size of a city based on them. – Matteo

- Scaling patterns for cities are fascinating, but a potential data limitation to the PNAS paper is that the study was applied to cities in the US, EU and China. It can be argued that these economies have particular similarities that may not be transferable to cities in developing countries that are not China (e.g. Jakarta, Delhi, Manila). To complicate matters a bit more, there is little consensus on what a city is -- is it defined by the political boundary? What about the economic boundary determined by bedroom communities (suburbs)? If one takes the latter view of a metropolitan area, the population densities of some "cities" in developing countries is truly astounding. The New York metropolitan area can be interpreted as spanning 4500 sq. miles with 20 million people (a density of ~4600 people/sq. mile). Under the same interpretation, Jakarta spans only 1075 sq. miles with 25 million people (a density of ~25,000 people/sq. mile!!!). The point of my comment is that I wonder how well the scaling findings of West et al. hold up for cities in developing countries (that do not include China). It seems possible that there are scaling patterns there as well, but they might be different. By the way, I got my numbers for population densities from Demographia. -- Vanessa

- Because of national differences, there's certainly no consensus yet on how to define the boundaries of a city. Even UN recognizes this and suggests following the boundaries established by individual countries. On the point of whether scaling patterns will be the same for developed and developing countries, the scaling patterns might still be the same (wealth creation leads to superlinear scaling while economies of scale results to sublinear scaling) but the actual value of the scaling exponents might vary. It would be interesting to see whether such two sets of exponents exist as it might explain why the experience of living in two cities of the same density can be different (case in point Manila with a pop'n density of 21.9 M vs Shanghai or NY with 20.9 M). To quantify the living conditions/living experience of cities, we can use the Economist Intelligence Unit’s 2012 Global Liveability Survey. If data is now available for developing countries, it wouldn't be hard to check this. But having lived in Manila and seeing how inefficient collection of census data in such cities can be, I doubt if we can have a substantial sample of developing countries with complete data. We can probably check here.

As for setting the thresholds in computing for the maximal city size, we can use the parameter values for Melbourne as according to the EIU survey, it is the most livable city in the world. If somehow we decide that using this is not a good way of defining the thresholds and there's no other way of setting them, jumping off from Vanessa's point, what we can also do is have several sets of parameter constraints and say that if city A falls under category A with parameter constraints {A}, then this is the maximal size it can have. We can do some clustering analysis of the parameter space to determine the city categories. I think it would be worthwhile to talk to someone from the cities group here in SFI as they've already mentioned before how population growth behaves with the different scaling patterns.-- Cheryl

— Maximal city size is an interesting question and hard to answer with the boundary problem mentioned by Vennesa and Cheryl— Michael Batty also talks about this problem in 'Cities and Complexity' In biological organisms the upper limit is bounded not only by the cross sectional area of bones but also by the metabolic rate which slows down as an organism increases in size to a 3/4 power law (Klieber, West et al.) Metabolism in organisms is basically how efficient an organism is. What would be the corresponding metabolism of a city? Here is an idea: What if 'urban metabolism' were to be measured as wealth distribution (correlating with the distribution of blood in an organism)? Functional organisms distribute blood to all service volumes and limit non-essential ones when in pathological states. Crime, as you mention Matteo, increases super-linearly and one might very well imagine a large city becoming too dangerous to function— such as the LA riots or something. Perhaps this is like pathology in organisms where crime is, in essence, the system attacking itself, i.e. cancer, immune disorders etc.

West et al. have also found that infrastructure increases sub-linearly with population size making a city, at least physically, more efficient the larger it becomes. Here is a quote from the paper 'A Unified Theory of Urban Living':

“Cities manifest remarkably universal, quantifiable features. This is shown by new analyses of large urban data sets, spanning several decades and hundreds of urban centres in regions and countries around the world from the United States and Europe to China and Brazil. Surprisingly, size is the major determinant of most characteristics of a city; history, geography and design have secondary roles. Three main characteristics vary systematically with population. One, the space required per capita shrinks, thanks to denser settlement and a more intense use of infrastructure. Two, the pace of all socio- economic activity accelerates, leading to higher productivity. And three, economic and social activities diversify and become more interdependent, resulting in new forms of economic specialization and cultural expression. We have recently shown that these general trends can be expressed as simple mathematical ‘laws’. For example, doubling the population of any city requires only about an 85% increase in infrastructure, whether that be total road surface, length of electrical cables, water pipes or number of petrol stations. This systematic 15% savings happens because, in general, creating and operating the same infrastructure at higher densities is more efficient, more economically viable, and often leads to higher-quality services and solutions that are impossible in smaller places. Interestingly, there are similar savings in carbon footprints7,8 — most large, developed cities are ‘greener’ than their national average in terms of per capita carbon emissions. It is as yet unclear whether this is also true for cities undergoing extremely rapid development, as in China or India, where data are poor or lacking. Similar economies of scale are found in organisms and communities like anthills and beehives, where the savings are closer to 20%. Such regularities originate in the mathematical properties of the multiplenetworks that sustain life, from the cardiovascular to the intracellular. This suggests that similar network dynamics underlie economies of scale in cities” (West, Bettencourt, A Unified Theory of Urban Living 2010).

I have been developing a tool in NetLogo to measure the fractal dimension of cities which, together with other metrics such as density, could afford an appropriate means to gauge the efficiency of various cities with the assumption that higher fractal dimension = more efficient distribution networks. I am interested in simulating cities with genetic algorithms and dimensioning the results— this technique could possibly support an endogenous limit to growth hypothesis using some fitness criteria such as the EIU parameters. Ecological services certainly decrease with city size and has prompted West to suggest that innovation must increase at an ever faster rate to offset the negatives imposed by growth. Here is another quote from West et al.:

“Open-ended growth is the primary assumption upon which modern cities and economies are based. Sustaining that growth with limited resources requires that major innovations — such as those historically associated with iron, coal and digital technology — be made at a continuously accelerating rate. The time between the ‘Computer Age’ and the ‘Information and Digital Age’ was some 20 years, compared to thousands of years between the Stone, Bronze and Iron Ages.Making major technological paradigm shifts systematically faster is clearly not sustainable, potentially leading to collapse of the entire urbanized socio-economic fabric. Avoiding this requires understanding whether we can continue to innovate and create wealth without continuous growth and its compounded negative social and environmental impacts” (West, Bettencourt 2010).

Fractal dimension applied to cities is still in its infancy and I wonder if this metric itself is necessary to refine relative to the questions this project presents. Multi-fractals for instance are a more nuanced metric and have not been applied to cities to my knowledge. John Driscoll

To those still interested in discussing this further, I suggest we meet at lunch time later (6 June, 12pm) -Cheryl

Somewhat relevant data viz of bus speeds in Boston. Probably replicable for many other cities too, and tells us something about city topology. --David


New media accounts are talking about the scale of the US National Security Agency's surveillance program (Hey, NSA folks!). It looks like they're running some network analysis with >70 trillion (not a typo) edges. We've got a good group of network people here. Anyone want to do something topical and try to put together a discussion / estimation of what can and can't be done with data that big? -- David

Any website with info about this? Someone interested to meet over this topic? -- Luíño

Interesting topic, let me know when/if you wanna discuss about it -- Andrea

Maybe an old and not so scientific article

Evolutionary Dynamics and Fitness Landscapes

After some extra investigation of the model presented by Tom in his talk this afternoon, we actually found some very interesting structure in the population that survived in the end - there are always pure solutions with exactly four genotypes. We have the impression that it could lead to a more formal treatment of this solution.

For those who haven't attended the lecture, the idea is the following: we start with a uniformly distributed population of genotypes (a string of 0s and 1s) and every organism always chooses to mate (cross-over + random point mutations) with the most different (according to a Hamming distance) organism nearby (there is a spatial structure). The fitness is not explicit, which makes the model somewhat more interesting!

Due to the symmetry, the corresponding landscape has several peaks with the same height, and according to the mutation rate there are some attractors which we think may be related to eigenvectors. We believe we could derive an error threshold for that model.

If anybody is interested in discussing with us informally and/or joining this group, it would definitely contribute a lot!


This sounds fun but I'm not exactly sure what you have in mind. Hope to discuss at meals!! -Mengsen.

Count me in, yes! :) – Kristen Honey