Santa Fe Institute Events Wiki - User contributions [en]

Complex Systems Summer School 2015-Projects & Working Groups

2015-07-02T22:10:12Z

Rbarnes: /* Richard's Current Table */

{{Complex Systems Summer School 2015}}

==[[Reservations for Evans Science rm. 215]]==

Click on the title link to reserve Evans Science rm. 215 for group usage.

==Californian Drought Model==
Problem definition:
Water is an ongoing problem in California. Although corrective measures are taken for some years, periods of droughts are depleting water ground levels to not sustainable levels.
Aims:
A Netlogo simulation on how likely Californian agents evolve with regards to drought. The ABM could be used to explore the potential impacts of forms of regulation.
Outcomes:
Create an exploratory agent based model based on real data, enabling the possibility of simulating different methods of regulation, including feedbacks between the economic and hydrologic systems.

Extension 1: We will conduct a network analysis of Twitter data (hashtags #drought #California) to explore the social dynamics and information flows between stakeholders.

Extension 2: Phase-space reconstruction of groundwater level time series data, comparing the dynamics in different parts of the Central Valley.

==City Resilience==
'''Summary:''' This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities. 
'''Contact:''' Richard Barnes (rbarnes@umn.edu) 
'''Participants:''' Alex Tejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang, Martina Steffen, Juan Carlos Castilla, Brent Schneeman 
'''Wiki page:''' [[Resilience of Cities]]

==Complex Adaptive Systems and the Narrative approach: transdisciplinary methodologies for Complexity Science==
The narrative approach and it causality is different from causality in logico-scientific approaches. Making bridges among methodologies transfers knowledge, encourages important questions and switches philosophical paradigms. Formal frameworks of C(A)S can realy be complemented with the narrative, and actually should. The narrative approaches in Complexity Science are an emerging trend for fund-granting, and is really up to date to process documentary and speech-based data, reveal hidden meanings, distinguish causes from effects in overlapping systems of realms - "at the edge" of technology, social, economic, scientific, legal and other domains. The role of time in coding, intuitively generated conditional rules and computational paths. Combinations of C(A)S and the narrative is combination of quantitative and qualitative data and thinking in original - out of convertation and information loss. Synthesis of sciences is what it is about.

'''Questions''':
How to combine CS approaches (in particular CAS) and the narrative ones in a rule-based way? What to start from? What vizualizations there can be designed and used? Other questions at your descrete.

'''Some references''':
- Non-Equilibrium Social Science in ICT and Economics, CORDIS, EU: http://cordis.europa.eu/project/rcn/102344_en.html, http://www.nessnet.eu/

- Combining Complexity Theory with Narrative Research: https://youtu.be/pHjeFFGug1Y

- Haridimos Tsoukas and Mary Jo Hatch (2001), "Complex thinking, complex practice: The case for a narrative approach to organizational complexity: http://www.brown.uk.com/teaching/qualitativepostgrad/tsoukas.pdf

- David Christian, "Big History", Astrophysics, Chemistry, Biology, Information, emergence of life, technology: https://youtu.be/GlmvFjFceD8

'''Contact''': Anna (annza944@gmail.com)

Meet on Monday over lunch

'''Interested''': Marie Pierre, Jeroen, Jim, Melissa

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
 
'Carolina'
 
'Alice'
 
'Federico'
 
'Jean Gab'
 
'Sola'
 
'Ilaria'
 
'Daniel T'
 
'Sahil Garg

==Ebola==
The 2014-15 Ebola virus disease (EVD) outbreak in West Africa presented both unique opportunities and unique challenges to the epidemiological modeling community. For the first time during an emerging infectious disease outbreak, high resolution data--from a variety of sources--were made available to the academic community and many public health decision makers genuinely engaged with mathematical and computational modelers. However, the popular and scientific press were highly critical of most models ability to project the outbreak's course. The following key and open questions seem ripe for investigation using a complex adaptive systems lens:
 
1) What features of EVD transmission are most problematic for reliable, robust forecasting: changing behavior, intervention, viral evolution, complex social networks, etc?
 
2) How/can we use digital data to either improve forecasts or inform model selection?
 
3) Can one quantify the value of additional information in real-time?
 
'''Contact:''' Samuel Scarpino, SFI Omidyar Fellow, Santa Fe Institute - scarpino@santafe.edu
 
 
'''Interested:'''
 
Marie-Pierre Hasne 
Chris Verzijl 
Junming Huang 
'Sola Omoju 
Christine Harvey 
Daniel Citron (dtc65@cornell.edu) 
William Chang (williamkurtischang at gee-mail)

==Effect of landscape topography on vegetation connectivity and navigability==
''Context''
 
Landscape topography influences the dynamics of the processes that take place on it. Evolution of ecosystems networks, river networks, vegetation cover type, microclimate cycles are all interlinked deeply to the local landscape topography.

In addition to the landscape these networks are also influenced by each other conditioning the emergence and stability of ecosystems, and subsequently the behaviour of agents in the ecosystem like migration pathways of animal herds, human settlement patterns etc.

Connectivity patterns emerge from the interaction of these processes, and thus a better understanding and quantification of those patterns is critical to understanding the dynamics of the system.

 
''How do we want to approach the problem''
 
In this project, we will randomly generate landscape topography and subsequent vegetation cover from a set of parameters from known geological and biological processes. The generated data set will then be used to investigate the following questions:
 
(1) What is the connectivity of landscape patterns that emerge at different scales using different techniques such as clustering analysis, percolation theory or network theory, and can we quantify them ?
 
(2) What is the navigability of biological agents (e.g animals, humans, robots!) under such landscape patterns. We can compare mobility trails in existing landscapes to validate our hypothesis. We propose to use the tools like ABMs to simulate and characterise mobility success.
 
(3) We further aim to compare the measures of navigability with the metrics of connectivity, establishing a framework of comparison.
 

''Contact:''
 tirtha.bandy@csiro.au
 alej.tejedor@gmail.com

''Meetings''
(1) Wednesday 1pm Coffee shop

People Interested 
Martina

==[[Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe]]==
Click on the link to go to the project page for meetings, project details, and progress.
 
contact: mveitzel@ucsc.edu
 

==Mapping Complexity/Human Knowledge as a Complex Adaptive System‏==
''Context''
 
Ants leave pheromone trail patterns which they are aware of only in a local sense. They do not have the cognitive faculties to step back and look at the trails and grasp the ant-trail network as a totality. Also, the artifacts they leave behind are physical entities which then provides the aggregate feedback to the aggregate ant body to then feed the evolution of the ant body as a CAS system. In contrast, humans do have the requisite cognitive abilities. The "pheromone trails" we leave behind are the knowledge trails coded in symbolic knowledge artifacts. In contrast to the physical artifacts that ants leave behind, the knowledge artifacts that we leave behind are far more flexible and potent, both at the aggregate as well as at the individual levels. But like the ants, until recently, we did not have the means to step back and map the knowledge "pheromone trails" to obtain the big picture and its global/local dynamics. The burgeoning field of scientometrics is making available visualization tools to help us map and study the evolutionary dynamics of the knowledge network structures.
 
''Data and Questions''
 
The goals of this project include
<ol>
<li>Extract the terms from approximately 1600 working papers published by SFI</li>
<li>Map the intra/inter conceptual network structures</li>
<li>Study the evolution of these structures across time</li>
<li>High-light the gap-closure of knowledge reverse-salients (if any)</li>
<li>Capture any of the network patterns that repeat</li>
<li>Study the diffusion of concepts across the network</li>
<li>Provide visualization tools for navigating the complexity corpus, etc</li>
</ol>
 
''Possible methods''
 
Latent Semantic Analysis (LSA) and Latent Document Analysis (LDA)
 
References:
<ol>
<li> SFI Working Papers: http://www.santafe.edu/research/working-papers/</li>
<li> Atlas-Science-Visualizing: http://www.amazon.com/Atlas-Science-Visualizing-What-Know/dp/0262014459</li>
<li> Atlas-Science-Visualizing WebSite: http://scimaps.org/atlas</li>
<li> Mapping-Scientific-Frontiers-Knowledge-Visualization: http://www.amazon.com/Mapping-Scientific-Frontiers-Knowledge-Visualization/dp/1447151275</li>
<li> Katy Börner presents at Science of Science: https://www.youtube.com/watch?v=pzCqGBNzomE</li>
<li> Scholarly Data, Network Science, and (Google) Maps:https://www.youtube.com/watch?v=vos5QBDywMM</li>
<li> LSA Video Lect: http://videolectures.net/slsfs05_hofmann_lsvm/</li>
<li> What is LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf</li>
<li> LSA Wiki:http://en.wikipedia.org/wiki/Latent_semantic_analysis</li>
<li> LDA: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm</li>
<li> Fusari, A. (2014). Methodological Misconceptions in the Social Sciences: Rethinking Social Thought and Social Processes: http://www.springer.com/us/book/9789401786744 </li>
<li> Kirsh, D. (2013). Thinking with external representations. Cognition Beyond the Brain: http://adrenaline.ucsd.edu/kirsh/Articles/Interaction/thinkingexternalrepresentations.pdf </li>
<li> Holland, J. H. (1992). Adaptation in natural and artificial systems: https://mitpress.mit.edu/index.php?q=books/adaptation-natural-and-artificial-systems </li>
<li> Where to start with text mining. http://tedunderwood.com/2012/08/14/where-to-start-with-text-mining/ </li>
<li> Singular Value Decomposition Tutorial https://www.ling.ohio-state.edu/~kbaker/pubs/Singular_Value_Decomposition_Tutorial.pdf </li>
<li> Latent Semantic Analysis (LSA) Tutorial: http://www.puffinwarellc.com/index.php/news-and-articles/articles/33-latent-semantic-analysis-tutorial.html </li>
<li> LSA in Detail: http://www2.denizyuret.com/ref/berry/berry95using.pdf </li>
<li> Web Based LSA: http://webapp1.dlib.indiana.edu/newton/lsa/index.php </li>
<li> Another Technique: t-Distributed Stochastic Neighbor Embedding (t-SNE): http://lvdmaaten.github.io/tsne/ </li>
<li> tf–idf: https://en.wikipedia.org/wiki/Tf%E2%80%93idf </li>

</ol>
 
'''Interested:'''
 
<ul>
<li> John Thomas (radjohn@live.com) </li>
<li> Haitao Shang (hts@mit.edu) </li>
<li> Christopher Verzijl (cjo.verzijl@gmail.com) </li>
<li> Anna Zaytseva (annza944@gmail.com) </li>
<li> Penny Mealy (penny.mealy@gmail.com) </li>
</ul>
 
 

==Navigating Music, Brain and The Edge of Chaos ‏==
''Context'' 
[[Navigating Music, Brain and The Edge of Chaos]]
 
Brain Sciences have revealed that we/it lives "on the edge of chaos" exhibiting "self-organized criticality" that is tentatively balanced between normalcy and madness. Over the course of history, humans have used various agents and activities to shape, influence and control this living-chaos ranging from substances such as caffeine, sugar, drugs etc., to activities such as the arts (including music), social-discourse/therapy, meditation etc. Of these, music has a distinct role in shaping our moods and helping us transition between different mental states, as well as maintain it for extended periods of time. Clearly we have been using music to help us control and shape the internal chaos. But until recently, the quantitative instrumentation of this massively complex system that comprises of close to a 100-billion neurons networked into a 1000-trillion synaptic edifice has not been available to the common man. But of late, affordable, wearable EEG's are available on the market, thus making the quantitative study of the influence of music in brain dynamics feasible on a large-scale/crowd-sourcing sense. To help come to terms with the complexity of our 1000-trillion synaptic edifice, we need to gather data on a vast scale. The proposed research is a proof-of-concept, exploratory foray into making this happen.
 

History of music in 5 min
https://www.youtube.com/watch?v=Gt2zubHcER4

'''Interested:'''
 
<ul>
<li> Sara Lumbreras (sara.lumbreras@gmail.com) </li>
<li> Ilaria b (bertazzi.ilaria@alice.it) </li>
<li> Braun, Urs (urs.braun@zi-mannheim.de) </li>
<li> John Thomas (radjohn@live.com) </li>
<li> Glenn Magerman (glenn.magerman@kuleuven.be) </li>
<li> Daniel Friedman (DanielAriFriedman@gmail.com)</li>
</ul>
 
 

==Powerlaw fitting and alternative distributions - Theory/statistics ==
Clauset, Shalizi and Newman (2009) propose a maximum likelihood method to estimate the powerlaw exponent of a variable of interest. This is a great improvement on earlier methods such as OLS that dominated the literature up to then. However, one can fit a powerlaw to any dataset and the most we can say is that our observations are consistent with the hypothesis that x is drawn from a powerlaw distribution. One easily implementable method to compare the powerlaw fit to other fits is then a likelihood ratio test for both models.

One particular discussion is the distinction between a powerlaw (PL) and a log-normal (LN) fit. For an avid discussion between both fits on city size and Zipf's law, see Eeckhout (2004, 2009) and Levy (2009), where the discussion now settled on city sizes following a log-normal distribution instead of a powerlaw. Similarly for the discussion on firm size distribution: Simon and Bonini, 1958; Ijri and Simon, 1977; Stanley et al., 1995; Sutton, 1997; Axtell, 2001; Okuyama et al., 1999; Cabral and Mata, 2003; Gaffeo et al., 2003; Aoyama et al., 2004; Fujiwara et al., 2004a,b; Kaizoji et al., 2006; Takayasu et al., 2008; Duchin and Levy, 2008; Schwarzkopf and Farmer, 2008, ...

This distinction matters for several reasons:
- PL and LN come from very similar model and differences in initial conditions can lead to very different outcomes. PL exhibits a choice for x_min, below which the unit of observation is not feasible to exist (eg minimum city size, firm size, word length, ...). LN has no minimum size.
- PL and LN that look similar are the difference between infinite (PL) and large but finite variance (LN)
- shock propagation: when unit-level shocks are large enough to show aggregate perturbances if the distribution is powerlaw with infinite variance, while these shocks wash out fast when the distribution is log-normal. (Gabaix, 1999; Gabaix 2009; Acemoglu et al. 2012, ...).

I have encountered some issues which I would like to explore further:
1. The distinction between lognormal and powerlaw in the data is very sensitive to data truncation: in the above discussions, researchers have slightly different datasets, covering more or less of the population at hand. Left-truncation (i.e. observations not in the dataset because they are too small to be reported) can strongly drive the outcome of the fit, even when endogenizing the x_min cutoff. I have data on the universe of Belgian firms, much more complete than e.g. US Census data, where I have done some preliminary tests on this. The question is then: how to formalise this distinction and what are the theoretical and practical caveats to look out for when applying this method.
2. MLE fitting seems to be sensitive to the choice of units as well: rescaling a variable by a factor 1000, 1000000 etc seems to influence the endogenous x_min choice and hence the estimated parameter. This reminds me of some work on scaling invariance in negative binomial estimators. What is going on here?
3. Can we set up a model that generalises both? I've been looking at Levy stable distributions, but did not do anything with it yet.

Interested people:
* Corbain
* Laura
*Binyang
*Sahil
*Tirtha
*Glenn
*Junming

Literature:
Powerlaw fitting in empirical data - Clauset, Shalizi and Newman (2009): http://arxiv.org/abs/0706.1062

==Organ Transplant Analysis==
Over 120,000 people in the United States are currently on the waiting list for an organ transplant. The size of this waiting list relates to over 6,000 deaths a year while waiting for a transplant and tens of billions of dollars in government spending. I have access to the following data sets:
 
* All transplants performed in the US from October 1987 to June 2014 (including follow-up data)
* All living and deceased organ donors in the same time period (with follow-up data on living donors)
* Waiting list data for everyone who signed up for the list
* 2012 National Survey on Attitudes and Behaviors on Organ Donation
* Social media data relating to organ donation since 2008

Open to ideas and suggestions for the topic, there are a lot of interesting questions to investigate including cultural/racial/gender differences in organ donation. I have several preliminary reports and exploratory analysis done on differences in donors.

Second Meeting Thursday, June 11th 4:15 in the coffee shop!

===Matching Game===
Rules for the matching game:

Objective: Create the largest chain possible using blood types in the room.

Follow the blood type guidelines for donation to form a donor chain.
*Assume that self-matches are not possible, for example someone with a donor of type AB and a recipient of type AB can not match themselves and needs to find someone else to complete their chain.
*Create a donor chain to help as many people as possible.
*Post results below for the prize
*There will be several "good samaritan" donors which will donate and need nothing in return, this is a great way to start your chain. Using a good samaritan chain also means you don't need a donor for the end point.

====Game Workspace====
Use this workspace to post for and find matches!

==== Donor Chains ====
Post your chain here. Example is given below:

=====Example Chain 1=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| A
|-
| Laura
| AB
| B
|-
| Matt
| A
| AB
|}

=====Example Chain 2=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| None
|-
| Laura
| A
| B
|-
| Matt
| None
| A
|}

======Richard's Current Table======

Iterations: 3,610
Length: 37 (everyone that needed an organ got one)

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| LauraCondon
| O
| X
|-
| Jeroen
| O
| O
|-
| Keith
| AB
| O
|-
| Brent
| O
| AB
|-
| DanielF
| AB
| O
|-
| Jelle
| O
| AB
|-
| AndySchauf
| A
| A
|-
| Chris
| O
| A
|-
| MatthewO
| AB
| O
|-
| Sahil
| O
| AB
|-
| Maria
| O
| A
|-
| MatthIn
| AB
| O
|-
| Susanne
| A
| AB
|-
| Juan
| AB
| A
|-
| Alejandro
| O
| AB
|-
| Urs
| B
| A
|-
| Sam
| O
| B
|-
| Jakub
| O
| A
|-
| Michael
| O
| A
|-
| WillChang
| O
| A
|-
| Laurence
| O
| B
|-
| James
| AB
| O
|-
| Vanessa
| A
| AB
|-
| Richard
| A
| A
|-
| Jae
| B
| A
|-
| Melissa
| A
| B
|-
| JamesCuton
| O
| A
|-
| Masa
| A
| O
|-
| Cobain
| B
| A
|-
| Charon
| B
| B
|-
| Nilton
| O
| B
|-
| Martina
| A
| O
|-
| Carolina
| O
| A
|-
| Glenn
| O
| A
|-
| MariePierre
| A
| O
|-
| Kleber
| O
| A
|-
| Alex
| AB
| O
|-
|}

Unused:

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Alice
| AB
| O
|-
| Christine
| O
| X
|-
| Havier
| AB
| O
|-
| JGab
| O
| X
|-
| Maggie
| O
| X
|-
| Matthew
| O
| X
|-
| Sander
| A
| X
|-
|}

==Multi-dimensional social networks in the evolution, development and resilience of informal economies. ==
Informal economies are defined as economic activities that occur outside the purview of corporate public and private institutions. These types of economies proliferate where traditional economic actors are unable to productively exercise their activities, especially due to costly constraints (De Soto 2003). Firms may face adverse incentives to expand production by hiring more workers or incorporating more capital due to the low productivity of their workers or the predatory practices of rapacious elites or corrupt governments. Labor itself, i.e. people, may face hurdles in trying to make the jump towards entrepreneurial activities or jobs in the formal economy which allow the accumulation of experience, retirement savings, access to insurance or precautionary savings to face unexpected events (like disease, disability, etc.) due to lack of capital access (whether human and financial).

'''For this project, we propose a different interpretation: We seek to understand and model how multi-faceted social networks provide robust alternatives to formal economies, which far from being seen as degenerate forms of social organization, in many instances co-exist, challenge and compete with formal employment and economic activities. While some types of informal economies operate under adverse contexts, their resiliency may be understood as a type of adaptive fitness and not the mere result of stubborn cultural path-dependency. '''

For the most part, informal economies have been analysed as counterpoint to an ideal type of a formal economy with low levels of trust, respect of property rights, access to credit and public institutions - like the court systems (see Losby et al. 2002 for a review). However, informal economies have been coupled with their formal counterparts since the development of capitalism in the 19th Century. To name a famous historical example, Old London's East End's informal sector was described harshly by Engels (1844) in his famous tract, "The Condition of the Working Class in England". But almost fifty years later, in a new preface to the book, Engels recognized the progress that took place there under the aegis of working class organizations in the area.

Research has begun to catch on to this idea – developing a literature in the areas of informal risk sharing and remittances, which are in some sense informal counterparts to insurance and banking. Consumption patterns in poor, rural villages are remarkably smooth suggesting that risk-sharing measures are prevalent, although imperfect (Townsend). Field studies of networks underlying village risk sharing systems have found that households primarily receive help from existing social connections, such as friends and relatives, in the form of informal loans or transfers. Theoretical work in network economics, by authors such as Matt Jackson, found that certain empirically prevalent network structures may directly benefit the stability of favor exchange systems. Another line of inquiry focuses on remittance income, generally informal transfers across kinship ties. World Bank researchers have found that remittances from overseas migrants respond dramatically to regional income shocks, replacing upwards of 60% of lost income in households with international migrants. Further studies, cited below, have generalized those results. Furthermore, reductions in the cost of sending remittances, which occurs with the advent of mobile money, further mitigates exposure to risk in receiving households.

Also recently, authors have written on how communities come together in situations where formal economies are unavailable by external shocks (like disasters) or due to lack of access (due to conditions of abject poverty). In the case of the latter, Venkatesh (2008) provided gripping testimonies of how informal economies arose and developed in the South Side of Chicago in low trust environments via cash transactions and informal service contracts enforced by (and for the benefit of) local criminal gangs. These gangs were seen, surprisingly, as alternative coordinating mechanisms to settle claims between neighbors. In the case of the former, Storr and Grube (2013) in a series of papers has argued how "shared histories and perspectives, and the stability of social networks within the community" allows communities who suffered disasters to cope and endogenously resolve immediate and complex problems. Providing an example of these social networks in action, research has found that remittances flow quickly into areas affected by natural disasters when there is a technology in place for it to happen.

Taking these lines of inquiry together, the findings suggest that informal handling of risk takes place on a large scale and that social networks informally connect communities in ways that impact their economies. Recent trends suggest that the interaction of formal and informal economic processes is growing on a nationwide scale. First, the aggregate value of remittance flows is large and growing, nearing the value of foreign direct investment. Second, advancements that formalize previously informal transactions are expanding dramatically in emerging economies through various forms of branchless banking. National economies may be embedded in profoundly influential informal systems that have never been holistically studied.

On a more general note, this project touches lingering questions in economics. For example, some might argue that more stringent labor regulations should have increased informal employment, but in fact the opposite happened. And while the economic rationale for the former statement remains valid, we suggest that unions, as other types of social organizations, as examples of ways whereas people interact via organized networks, provide richer dimensions than those suggested by their interaction as mere economic agents. Hence, unions, as well as other types of faith-based, ethnic, community and other interest groups may provide ways of interaction that escape narrow economic outcomes.

====References====

• De Soto, Hernando (2000/2003) The Mystery of Capital. Basic Books.
• Losby, Jan; Else, John; Kingslow, Marcia; Edgcomb, Elaine; Malm, Erika and Vivian Kao (2002) The Informal Economy: A Literature Review. ISED Consulting and Research and the Aspen Institute Working Paper. http://www.kingslow-assoc.com/images/Informal_Economy_Lit_Review.pdf
• Townsend, Robert M. "Risk and Insurance in Village India." (1994)
• Fafchamps, Marcel, and Susan Lund. "Risk-sharing Networks in Rural Philippines." (2003)
• Weerdt, Joachim De, and Stefan Dercon. "Risk-sharing Networks and Insurance against Illness." (2006)
• Matthew O. Jackson, Tomas Rodriguez-Barraquer and Xu Tan. “Social Capital and Social Quilts: Network Patterns of Favor Exchange.” (2012)
• Yang D and H Choi."Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines"(2007)
• Kurosaki, Takashi. "Consumption vulnerability to risk in rural Pakistan." (2006)
• Jack, W, and T. Suri. "Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution” (2014)
• Engels, Friedrich (1844/1892) The Condition of the Working Class in England. http://www.gutenberg.org/files/17306/17306-h/17306-h.htm
• Venkatesh, Sudhir (2008) Gang Leader for a Day: A Rogue Sociologist Takes to the Streets. Penguin Books.
• Storr, Virgil and Laura Grube (2013) The Capacity for Self-Governance and Post-Disaster Resiliency. George Mason University, Department of Economics Working Paper 13-37. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2350830##
• Blumenstock, Joshua Evan and Fafchamps, Marcel and Eagle, Nathan. “Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda” (2011).
• Ratha, Dilip. "Workers’ remittances: an important and stable source of external development finance." (2005).
• Pénicaud, Claire, and Arunjay Katakam. State of the Industry 2013: Mobile Financial Services for the Unbanked. Rep. N.p.: GSMA MMU, 2013.

If interested, please list your name below.

'''Interested:'''
<li> Eloy Fisher (eloy.fisher@fulbrightmail.org) </li>
<li> Carolina Mattsson (carromattsson@gmail.com) </li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>

<li> Jakub Rojcek (jakub.rojcek@gmail.com) </li>

==Exploring Community Formation through Analysis of Scholarly Corpus (ArXiv)==

The Arxiv [http://arxiv.org/] is a free online repository of scientific preprint articles, mostly from physics and mathematics. It currently contains over 800,000 articles, dating back to 1991. Currently, a lot of the data associated with this repository is completely available: paper submission dates; full texts of papers; author names and coauthorship information. Additionally, there is some citations data available. (If necessary, I can also find papers' subdiscipline labels; submitting authors' email address domain names; other things.)

In the past I have been using these data to try and explore how communities of authors who have shared interests grow over time. For example, we can pinpoint the first ever paper about topological insulators, and search for all subsequent papers on that topic. As more papers are written, authors begin to join the field of research and to form strong ties by collaborating with one another. We can use the ArXiv data to visualize and analyze exactly how these communities form and grow.

===Brainstorming notes===
Isolation between topics, crossing interdisciplinary boundaries, finding (topological) separation distance between disciplines or research groups

Tipping points - can we look for separation of or mergers of two groups or disciplines?

Can we identify key papers (or groups of papers) that initiate ties between fields.

Sentiment analysis: can we identify when a paper's citation is an endorsement or a refutation?

Tools for Analysis: Change point detection; relational event models

Can we find more comprehensive/better citation data?

Lucene and indexing tools:
- Can we re-index our text database after removing stop words?
- Can we index the titles and abstracts?
- Elastic Search - tool for interacting with Lucene

This set of text-processing [http://alias-i.com/lingpipe/demos/tutorial/read-me.html tutorials] is pretty handy for background info and inspiration. The software package doesn't simply plug-into a Lucene/Solr/Elastic Search index, but it could be done. This package from [http://nlp.stanford.edu/software/ Standford] seems very capable.

===Next meeting===
Thursday @ 9AM in Coffee Shop

== PolComplex - Epistemic communities and policy dynamics in the UK Parliament ==

Over the last twenty years, there has been extensive debate about what the core topics in public policy are, and if they change according to type and number. Furthermore, it is still not clear how policy topics mutate and diffuse over time. Human-based text analysis of governmental documents has shed only some light on these research questions: 21 general topics have been proposed, while other accounts tend to restrict it to about five main clusters. Yet, this approach has not been able to devise a reliable and systemic method to capture topic dynamics, diffusion, and their relation to the human actors that talk about them.

This project is an extension of an exploratory research that intends to overcome such limitations, through the application of natural language processing (more specifically, dynamic topic modeling and sentiment analysis), topological and network analysis on a dataset containing UK House of Commons debates ranging from 1975 to 2014. The aim is manyfold. We hope to identify the structure and dynamics of epistemic policy communities - i.e. of political actors revolving around similar policy interest -, and how these relate to factors such as party membership, seniority, relevant historical events.

Some references:

[http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.734168 Taddy (2013), "Multinomial Inverse Regression for Text Analysis"] 

[https://www.cs.princeton.edu/~blei/papers/BleiLafferty2007.pdf Blei and Lafferty (2007), "A Correlated Topic Model of Science"] 

== Genomic variations from a chaotic mapping ==

Following Dabby CHAOS 6 (2), 1996 Musical variations from a chaotic mapping, this project will explore genomic variations in key metabolic genes that are known to be widely spread across bacterial phylogeny (e.g. the Nif cluster which is used in nitrogen fixation). There are several ways that genomic data can be treated as "music":

1) Nucleotide: 4 notes - A, G, C, T 
2) Amino Acids: 20 notes 
3) codon triplets: 64 notes 
4) tetra nucleotides: 256 notes. 

One possible goal is to take a seed sequence from some random organism and generate new variations. Then using homology search (e.g. BLAST) or phylogenetics, determine if that variation exists in nature? If not, perhaps model the potential protein folding (if possible?) to determine if that variation could exist in nature. This is VERY preliminary and can go in many directions.

If interested please list name below or contact Jarrod at jscott@bigelow.org

Reading
http://digitalcommons.olin.edu/cgi/viewcontent.cgi?article=1000&context=ahse_pub&sei-redir=1&referer=https%3A%2F%2Fscholar.google.com%2Fscholar%3Fhl%3Den%26q%3Ddabby%2Bchaos%26btnG%3D%26as_sdt%3D1%252C32%26as_sdtp%3D#search=%22dabby%20chaos%22

== Free Energy Theory ==
The Free Energy (FE) minimisation framework tries to explain how biological systems (such as a cell or a brain) self-organise in order to occupy the (often very limited number of) non-equilibrium states that minimise free energy. This is also known as active inference. A simple corollary of active inference is that agents behave as to minimise their prediction error, or the difference between prediction and reality. Thermodynamic free energy is a measure of energy available in a system to do useful work. This can be framed in an information theoretic setting, as the difference between how the world is being represented and how it actually is. A better fit means a lower information-theoretic free energy, as more resources are being put to ‘good use’ in representing the world. The overarching logic of FE theory is that a better model of the world help maintain structure and organisation, which ultimately helps the system resist increases in entropy.
 
 
This is not a set-in-stone project with any concrete aim (yet). We are a few people interested in exploring the theoretical and practical implications of these ideas, and you're more than welcome to join in!
 
 
'''Interested:'''
 
<ul>
<li> Jelle Bruineberg (j.bruineberg@gmail.com ) </li>
<li> Tobias Morville (tobiasmorville@gmail.com) </li>
<li> Susanne Pettersson (guspsusa85@student.edu.se) </li>
<li> Maggie Simon (margaret.w.simon@gmail.com) </li>
<li> Martina Steffen (martinasemail@gmail.com) </li>
<li> Sahil (sahilgar@usc.edu) </li>
<li> Tirtha (Tirtha.Bandy@csiro.au) </li>
<li> Anna (annza944@gmail.com) </li>
<li> Matt O (mmosmond@gmail.com) </li>
<li> Will Chang (williamkurtischang at gmail dot com) </li>
<li> Cobain (mrdc1g10@soton.ac.uk) </li>
</ul>
Reading:
 
http://rsif.royalsocietypublishing.org/content/10/86/20130475 
http://arxiv.org/abs/1503.04187 
http://www.mdpi.com/1099-4300/15/1/311 

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
'Carolina'
'Alice'
'Federico'
'Jean Gab'
'Sola'
'Ilaria'
'Daniel T'

==Comparison of Network vs Scaling Theory based Models in Ecology (Cobain - mrdc1g10@soton.ac.uk)==

One of the biggest difficulties ecologists face is trying to understand the ecosystem dynamics based on very little biological information (compared to the size of the system) - observational data is logistically difficult and can be very expensive to acquire, as well as often being very time consuming. In terms of modelling, to overcome this problem, two approaches have been utilised. 

The first is a network based approach whereby the nodes represent biomass density of a particular species (or a higher level taxanomic group and/or resources) and edges as the trophic interactions (who eats who). This method depends on what we know about the trophic behaviour of the species involved (which is often very limited and there can be many species to parameterise), and represents only the central tendency of what could potentially be very diverse behaviour within and between populations. 

The second approach is to use scaling theory to describe average trophic interactions and other biological processes based on individual organism body size along the size continuum, viewing the community as a very size structured dynamical system. This requires less specific knowledge about the organisms in the community ''per se'', however this again represents only the central tendency of a given size class of what could be very diverse behaviour. Functional differences can be potentially introduced (coupled benthic-pelagic systems for example), but to the detriment of braking down the predictability of the well known allometric scaling laws with size as specificity increases. 

There has been little to no comparison of these two methods of modelling ecological systems (at least to my knowledge) and the question arises that, given the same starting information, how well do both approaches model the dynamics of an ecosystem, given the limited biological information we have? Is one approach better at capturing energy flow through the system / community structure and stability etc. compared to the other? If the models vary then such information will better inform ecologists on which to use depending on what type of questions they are trying to answer. 

Therefore, the project proposed here aims to investigate these two methods. The community that is used to seed an experimental mesocosm setup (mesocosms are large tank set-ups designed to reflect the complexity of natural ecosystems but still being able to be artificially controlled and are still relatively simple), will be input into two models based on the separate approaches and then run to steady state. The model outputs will then be compared and contrasted with eachother as well as the mesocosm community at steady state. Further work may include examining perturbation dynamics, dependent on what data we have available.

'''Interested: Name / Email''' 
Martina Steffen (martinasemail@gmail.com)

==Dynamics of homicide (Matthew Ingram - mingram@albany.edu)==

Integrating temporal, spatial, and multi-level concepts

1:30pm in the coffee shop

Am interested in the discussion on this project...Sola Omoju

Interested - Nilton Cardoso

==Multiplex Adaptive Networks (Daniel - dtc65@cornell.edu)==

7 pm in the coffee shop

==Modeling brain diseases (or cancerous bio pathways) (Sahil - sahilgar@usc.edu)==
Interested people can put a meeting time as per their convenience here.
 
No meeting time indicated
 
One potential meeting time can be 3pm in coffee shop ?
 
Information theoretic algorithms can also be explored for the problem.
 
Discovering Structure in High-Dimensional Data Through Correlation Explanation. http://papers.nips.cc/paper/5580-positive-curvature-and-hamiltonian-monte-carlo
 
Maximally Informative Hierarchical Representations
of High-Dimensional Data. http://jmlr.org/proceedings/papers/v38/versteeg15.pdf

 
'''Interested:'''
 
<ul>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> Laura Condon (lcondon@mymail.mines.edu)</li>
</ul>
 
 

==Analysis of UK parliament speeches 1935-2014 (Stefano - etstefano@gmail.com)==

No meeting time indicated

==Mapping Complexity/Human Knowledge as a Complex Adaptive System (radjohn@live.com)==
2pm/Wed 6/10 in Conference Room (Tentative)

==Navigating Music, Brain and The Edge of Chaos (radjohn@live.com)==
10:45am/Wed 6/10 in Conference Room
9:00am/Thu 7/10 at the Coffee Shop

'''Interested:'''
Sahil Garg

==Analysis of rule-based modeling for dopamine-dependent synaptic plasticity (emilia.m.wysocka@gmail.com)==

----------------------------------------------------------------------------------
Modeling and analysis of phenomenons and evolution of stochastic, combinatorially complex signalling systems in a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system).

----------------------------------------------------------------------------------
Rule-based modeling features:

*biological systems as concurrent processes
*dynamics of post-translational modifications
*domain availability
*competitive binding
*causality and intrinsic structure
*binding sites
*interaction rules replace reaction equations
*infinite number of reactions with a small and finite number of rules
*reduction of parameter space
*“don't care don't write” - adjustable rule contextualization
*single reaction rule and parameters generalize classes of multiple rules
*modular and extensible language
*specification language & simulation/integration environment
*static and causal analysis
*Kappa/KaSim & BioNetGen/NFsim -- specification language/network-free simulator

----------------------------------------------------------------------------------
----------------------------------------------------------------------------------
Meetings:
----------------------------------------------------------------------------------
* WEDNESDAY 11/06/2015

Addressing problems in terms of the other aspects of the projects apart from the biological questions and verification (matching results to some published experiments or known behaviour). So my plan is as follows:

* divide the group (roughly) into people who look in to biological meaning and validation and the one which tries to do the analysis of system phenomenons and evolution of stochastic, combinatorially complex signalling systems in both a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system) - all possible states, scenarios of the system abstracted from the biological meaning.

Things that could be modelled (look in dropbox folder: https://www.dropbox.com/sh/g080w60pt2vgi7v/AACHlMf2JwpP2EZqKq7Di6N2a/possible%20models?dl=0):

* to make things easier and have the modeling part done quickly- base the model on the dopamine-related synaptic plasticity (SBML ODE model): http://www.ebi.ac.uk/biomodels-main/MODEL1101170000;

* I found a series of papers suitable to the subject of the Complex Systems course, e.g.: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC344803/. I was thinking to even try to compare higher level brain data (networks, spiking ..) with purely molecular model

* noise and low copy number (http://www.princeton.edu/~wbialek/our_papers/bialek+setayeshgar_08.pdf); this is quite interesting as for sufficiency of binding events -> http://www.princeton.edu/~wbialek/our_papers/bialek+ranganathan_07.pdf

* spontaneous flipping of interactions (phosphorylations and others) between proteins, described by the god of non-linear dynamics (S. Strogatz)

----------------------------------------------------------------------------------
* THURSDAY 11/06/2015 : http://doodle.com/se23ibwtkrkccs4s

----------------------------------------------------------------------------------
Some refs:
*https://www.dropbox.com/sh/g080w60pt2vgi7v/AADUy7Ge-J-oSYkyTeYOo7V8a?dl=0)
*http://www.pps.univ-paris-diderot.fr/~jkrivine/homepage/Teaching.html

==Ecology non-working group (williamkurtischang at gee-mail dot com)==
[[Informal ecology interest group]].

==Making Supply Chains Resilient to Disasters (mh2905 att columbia dott edu)==
'''Key words''': resilience, natural hazards, supply chains, interdependency, interconnected risks, cascading failures

'''Rational''': I am interested in examining what kinds of network structures and features contribute to increasing resilience of supply chains to natural disasters. I believe this area of work is important because regional disasters negatively impact the global economy through disruptions in supply chain networks. The pioneer study published in Nature urges the need for making supply chains climate-smart (Levermann 2014). Also in the industry, the World Economic Forum published a report to address this issue in 2013. Few researches, however, assess and model the impacts of adverse weather on supply chains. I would like to evaluate the impacts based on modeling.

'''Data set''': Supply chains data of a multinational manufacturing company.Global climate data, disaster data, etc.

'''Possible techniques''': Agent-Based Model, Complexity science, network theory and evolution, complex adaptive systems, GIS, operations research, manufacturing engineering, and I am open to any techniques.

'''Myself''': As I joined the program yesterday, please find my background here.
http://tuvalu.santafe.edu/events/workshops/index.php/Masahiko_Haraguchi

'''Please put your names below if you want to be informed about this project by email'''

'''Interested''':
Masa Haraguchi

==From Ethnic Diversity to Religious Zeal: Retrospective/Predictive Construction of the World Ethnic Map (vdzutsati@asu.edu)==

There is a new dataset on ethnic groups (2014), which claims to include all ethnic groups of the world http://www.cidcm.umd.edu/mar/amar_project.asp. There are also several cross national datasets on religion and other variables of interest, normally, socio-economic http://www.thearda.com/Archive/CrossNational.asp. Coming from social sciences I thought it would be really interesting to apply methods and perspectives from other disciplines to social science data.

One idea that I have in mind is to explore how ethnicity and religion interact.

Quick empirical checks indicate that Subsaharan Africa is home to about 1,500 ethnic and subethnic groups, in comparison to about 90 ethnic groups in the Middle East and North Africa. India alone accounts for nearly 2,000 ethnic and subethnic groups, while Europe, including all the countries of the former USSR and large immigrant groups from other parts of the world, has only about 260 ethnic groups. The picture that emerges from this simple comparison is that the spread of Abrahamic religions appears to be associated with the high depletion rate of ethnic groups. The exception of China, which has little more than 50 ethnic groups, can be explained by the country’s long history of a centralized state.

The idea then is to assume that we have had the control group of nations that did not experience the influx of Abrahamic religions (or more precisely received limited exposure to them) and the experimental group that was exposed to the spread of Abrahamic religions. Since we know what the distribution of ethnic groups in the control group is we can project what the distribution of ethnic groups in the experimental group would be, if the group were not exposed to Abrahamic religions.

Of course, we could go in the other direction too and make a projection about future – what would happen to ethnic groups if all of them adopted an Abrahamic religion.

One of the challenges in this project is absence of a dynamical system, I.e. We don’t have data on how these things changed over time, but I still think that projection about the future and the past would be kinda cool:)

'''Interested''':

==Nationalist vs religious rebel violence (vdzutsati@asu.edu)==

I would like to propose yet another possible project. This time it’s on rebel violence. I have a large dataset on rebel (and government) violence (~1 million observations). The paper that used this data was published couple of months ago (Islamists and Nationalists: Rebel Motivation and Counterinsurgency in Russia’s North Caucasus). The dataset contains event counts, GIS data, demographic and economic characteristics and some other stuff. The primary hunch of the paper was to discover the differences between nationalist and Islamist rebels.

Given all the cool methods we are learning here, my first instinct is to use the methods and tools (TISEAN?) to explore and discover whether nationalist violence is substantively different from religious violence and try to answer why.

One problem that I can see is that the event coding was done by a machine, so this may have “contaminated” the data. But if both religious and nationalist data were contaminated equally then the difference should still be evident.

'''Interested''':

==[[Archived Projects ]]==

Complex Systems Summer School 2015-Projects & Working Groups

2015-07-02T21:26:38Z

Rbarnes: /* Richard's Current Table */

{{Complex Systems Summer School 2015}}

==[[Reservations for Evans Science rm. 215]]==

Click on the title link to reserve Evans Science rm. 215 for group usage.

==Californian Drought Model==
Problem definition:
Water is an ongoing problem in California. Although corrective measures are taken for some years, periods of droughts are depleting water ground levels to not sustainable levels.
Aims:
A Netlogo simulation on how likely Californian agents evolve with regards to drought. The ABM could be used to explore the potential impacts of forms of regulation.
Outcomes:
Create an exploratory agent based model based on real data, enabling the possibility of simulating different methods of regulation, including feedbacks between the economic and hydrologic systems.

Extension 1: We will conduct a network analysis of Twitter data (hashtags #drought #California) to explore the social dynamics and information flows between stakeholders.

Extension 2: Phase-space reconstruction of groundwater level time series data, comparing the dynamics in different parts of the Central Valley.

==City Resilience==
'''Summary:''' This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities. 
'''Contact:''' Richard Barnes (rbarnes@umn.edu) 
'''Participants:''' Alex Tejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang, Martina Steffen, Juan Carlos Castilla, Brent Schneeman 
'''Wiki page:''' [[Resilience of Cities]]

==Complex Adaptive Systems and the Narrative approach: transdisciplinary methodologies for Complexity Science==
The narrative approach and it causality is different from causality in logico-scientific approaches. Making bridges among methodologies transfers knowledge, encourages important questions and switches philosophical paradigms. Formal frameworks of C(A)S can realy be complemented with the narrative, and actually should. The narrative approaches in Complexity Science are an emerging trend for fund-granting, and is really up to date to process documentary and speech-based data, reveal hidden meanings, distinguish causes from effects in overlapping systems of realms - "at the edge" of technology, social, economic, scientific, legal and other domains. The role of time in coding, intuitively generated conditional rules and computational paths. Combinations of C(A)S and the narrative is combination of quantitative and qualitative data and thinking in original - out of convertation and information loss. Synthesis of sciences is what it is about.

'''Questions''':
How to combine CS approaches (in particular CAS) and the narrative ones in a rule-based way? What to start from? What vizualizations there can be designed and used? Other questions at your descrete.

'''Some references''':
- Non-Equilibrium Social Science in ICT and Economics, CORDIS, EU: http://cordis.europa.eu/project/rcn/102344_en.html, http://www.nessnet.eu/

- Combining Complexity Theory with Narrative Research: https://youtu.be/pHjeFFGug1Y

- Haridimos Tsoukas and Mary Jo Hatch (2001), "Complex thinking, complex practice: The case for a narrative approach to organizational complexity: http://www.brown.uk.com/teaching/qualitativepostgrad/tsoukas.pdf

- David Christian, "Big History", Astrophysics, Chemistry, Biology, Information, emergence of life, technology: https://youtu.be/GlmvFjFceD8

'''Contact''': Anna (annza944@gmail.com)

Meet on Monday over lunch

'''Interested''': Marie Pierre, Jeroen, Jim, Melissa

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
 
'Carolina'
 
'Alice'
 
'Federico'
 
'Jean Gab'
 
'Sola'
 
'Ilaria'
 
'Daniel T'
 
'Sahil Garg

==Ebola==
The 2014-15 Ebola virus disease (EVD) outbreak in West Africa presented both unique opportunities and unique challenges to the epidemiological modeling community. For the first time during an emerging infectious disease outbreak, high resolution data--from a variety of sources--were made available to the academic community and many public health decision makers genuinely engaged with mathematical and computational modelers. However, the popular and scientific press were highly critical of most models ability to project the outbreak's course. The following key and open questions seem ripe for investigation using a complex adaptive systems lens:
 
1) What features of EVD transmission are most problematic for reliable, robust forecasting: changing behavior, intervention, viral evolution, complex social networks, etc?
 
2) How/can we use digital data to either improve forecasts or inform model selection?
 
3) Can one quantify the value of additional information in real-time?
 
'''Contact:''' Samuel Scarpino, SFI Omidyar Fellow, Santa Fe Institute - scarpino@santafe.edu
 
 
'''Interested:'''
 
Marie-Pierre Hasne 
Chris Verzijl 
Junming Huang 
'Sola Omoju 
Christine Harvey 
Daniel Citron (dtc65@cornell.edu) 
William Chang (williamkurtischang at gee-mail)

==Effect of landscape topography on vegetation connectivity and navigability==
''Context''
 
Landscape topography influences the dynamics of the processes that take place on it. Evolution of ecosystems networks, river networks, vegetation cover type, microclimate cycles are all interlinked deeply to the local landscape topography.

In addition to the landscape these networks are also influenced by each other conditioning the emergence and stability of ecosystems, and subsequently the behaviour of agents in the ecosystem like migration pathways of animal herds, human settlement patterns etc.

Connectivity patterns emerge from the interaction of these processes, and thus a better understanding and quantification of those patterns is critical to understanding the dynamics of the system.

 
''How do we want to approach the problem''
 
In this project, we will randomly generate landscape topography and subsequent vegetation cover from a set of parameters from known geological and biological processes. The generated data set will then be used to investigate the following questions:
 
(1) What is the connectivity of landscape patterns that emerge at different scales using different techniques such as clustering analysis, percolation theory or network theory, and can we quantify them ?
 
(2) What is the navigability of biological agents (e.g animals, humans, robots!) under such landscape patterns. We can compare mobility trails in existing landscapes to validate our hypothesis. We propose to use the tools like ABMs to simulate and characterise mobility success.
 
(3) We further aim to compare the measures of navigability with the metrics of connectivity, establishing a framework of comparison.
 

''Contact:''
 tirtha.bandy@csiro.au
 alej.tejedor@gmail.com

''Meetings''
(1) Wednesday 1pm Coffee shop

People Interested 
Martina

==[[Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe]]==
Click on the link to go to the project page for meetings, project details, and progress.
 
contact: mveitzel@ucsc.edu
 

==Mapping Complexity/Human Knowledge as a Complex Adaptive System‏==
''Context''
 
Ants leave pheromone trail patterns which they are aware of only in a local sense. They do not have the cognitive faculties to step back and look at the trails and grasp the ant-trail network as a totality. Also, the artifacts they leave behind are physical entities which then provides the aggregate feedback to the aggregate ant body to then feed the evolution of the ant body as a CAS system. In contrast, humans do have the requisite cognitive abilities. The "pheromone trails" we leave behind are the knowledge trails coded in symbolic knowledge artifacts. In contrast to the physical artifacts that ants leave behind, the knowledge artifacts that we leave behind are far more flexible and potent, both at the aggregate as well as at the individual levels. But like the ants, until recently, we did not have the means to step back and map the knowledge "pheromone trails" to obtain the big picture and its global/local dynamics. The burgeoning field of scientometrics is making available visualization tools to help us map and study the evolutionary dynamics of the knowledge network structures.
 
''Data and Questions''
 
The goals of this project include
<ol>
<li>Extract the terms from approximately 1600 working papers published by SFI</li>
<li>Map the intra/inter conceptual network structures</li>
<li>Study the evolution of these structures across time</li>
<li>High-light the gap-closure of knowledge reverse-salients (if any)</li>
<li>Capture any of the network patterns that repeat</li>
<li>Study the diffusion of concepts across the network</li>
<li>Provide visualization tools for navigating the complexity corpus, etc</li>
</ol>
 
''Possible methods''
 
Latent Semantic Analysis (LSA) and Latent Document Analysis (LDA)
 
References:
<ol>
<li> SFI Working Papers: http://www.santafe.edu/research/working-papers/</li>
<li> Atlas-Science-Visualizing: http://www.amazon.com/Atlas-Science-Visualizing-What-Know/dp/0262014459</li>
<li> Atlas-Science-Visualizing WebSite: http://scimaps.org/atlas</li>
<li> Mapping-Scientific-Frontiers-Knowledge-Visualization: http://www.amazon.com/Mapping-Scientific-Frontiers-Knowledge-Visualization/dp/1447151275</li>
<li> Katy Börner presents at Science of Science: https://www.youtube.com/watch?v=pzCqGBNzomE</li>
<li> Scholarly Data, Network Science, and (Google) Maps:https://www.youtube.com/watch?v=vos5QBDywMM</li>
<li> LSA Video Lect: http://videolectures.net/slsfs05_hofmann_lsvm/</li>
<li> What is LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf</li>
<li> LSA Wiki:http://en.wikipedia.org/wiki/Latent_semantic_analysis</li>
<li> LDA: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm</li>
<li> Fusari, A. (2014). Methodological Misconceptions in the Social Sciences: Rethinking Social Thought and Social Processes: http://www.springer.com/us/book/9789401786744 </li>
<li> Kirsh, D. (2013). Thinking with external representations. Cognition Beyond the Brain: http://adrenaline.ucsd.edu/kirsh/Articles/Interaction/thinkingexternalrepresentations.pdf </li>
<li> Holland, J. H. (1992). Adaptation in natural and artificial systems: https://mitpress.mit.edu/index.php?q=books/adaptation-natural-and-artificial-systems </li>
<li> Where to start with text mining. http://tedunderwood.com/2012/08/14/where-to-start-with-text-mining/ </li>
<li> Singular Value Decomposition Tutorial https://www.ling.ohio-state.edu/~kbaker/pubs/Singular_Value_Decomposition_Tutorial.pdf </li>
<li> Latent Semantic Analysis (LSA) Tutorial: http://www.puffinwarellc.com/index.php/news-and-articles/articles/33-latent-semantic-analysis-tutorial.html </li>
<li> LSA in Detail: http://www2.denizyuret.com/ref/berry/berry95using.pdf </li>
<li> Web Based LSA: http://webapp1.dlib.indiana.edu/newton/lsa/index.php </li>
<li> Another Technique: t-Distributed Stochastic Neighbor Embedding (t-SNE): http://lvdmaaten.github.io/tsne/ </li>
<li> tf–idf: https://en.wikipedia.org/wiki/Tf%E2%80%93idf </li>

</ol>
 
'''Interested:'''
 
<ul>
<li> John Thomas (radjohn@live.com) </li>
<li> Haitao Shang (hts@mit.edu) </li>
<li> Christopher Verzijl (cjo.verzijl@gmail.com) </li>
<li> Anna Zaytseva (annza944@gmail.com) </li>
<li> Penny Mealy (penny.mealy@gmail.com) </li>
</ul>
 
 

==Navigating Music, Brain and The Edge of Chaos ‏==
''Context'' 
[[Navigating Music, Brain and The Edge of Chaos]]
 
Brain Sciences have revealed that we/it lives "on the edge of chaos" exhibiting "self-organized criticality" that is tentatively balanced between normalcy and madness. Over the course of history, humans have used various agents and activities to shape, influence and control this living-chaos ranging from substances such as caffeine, sugar, drugs etc., to activities such as the arts (including music), social-discourse/therapy, meditation etc. Of these, music has a distinct role in shaping our moods and helping us transition between different mental states, as well as maintain it for extended periods of time. Clearly we have been using music to help us control and shape the internal chaos. But until recently, the quantitative instrumentation of this massively complex system that comprises of close to a 100-billion neurons networked into a 1000-trillion synaptic edifice has not been available to the common man. But of late, affordable, wearable EEG's are available on the market, thus making the quantitative study of the influence of music in brain dynamics feasible on a large-scale/crowd-sourcing sense. To help come to terms with the complexity of our 1000-trillion synaptic edifice, we need to gather data on a vast scale. The proposed research is a proof-of-concept, exploratory foray into making this happen.
 

History of music in 5 min
https://www.youtube.com/watch?v=Gt2zubHcER4

'''Interested:'''
 
<ul>
<li> Sara Lumbreras (sara.lumbreras@gmail.com) </li>
<li> Ilaria b (bertazzi.ilaria@alice.it) </li>
<li> Braun, Urs (urs.braun@zi-mannheim.de) </li>
<li> John Thomas (radjohn@live.com) </li>
<li> Glenn Magerman (glenn.magerman@kuleuven.be) </li>
<li> Daniel Friedman (DanielAriFriedman@gmail.com)</li>
</ul>
 
 

==Powerlaw fitting and alternative distributions - Theory/statistics ==
Clauset, Shalizi and Newman (2009) propose a maximum likelihood method to estimate the powerlaw exponent of a variable of interest. This is a great improvement on earlier methods such as OLS that dominated the literature up to then. However, one can fit a powerlaw to any dataset and the most we can say is that our observations are consistent with the hypothesis that x is drawn from a powerlaw distribution. One easily implementable method to compare the powerlaw fit to other fits is then a likelihood ratio test for both models.

One particular discussion is the distinction between a powerlaw (PL) and a log-normal (LN) fit. For an avid discussion between both fits on city size and Zipf's law, see Eeckhout (2004, 2009) and Levy (2009), where the discussion now settled on city sizes following a log-normal distribution instead of a powerlaw. Similarly for the discussion on firm size distribution: Simon and Bonini, 1958; Ijri and Simon, 1977; Stanley et al., 1995; Sutton, 1997; Axtell, 2001; Okuyama et al., 1999; Cabral and Mata, 2003; Gaffeo et al., 2003; Aoyama et al., 2004; Fujiwara et al., 2004a,b; Kaizoji et al., 2006; Takayasu et al., 2008; Duchin and Levy, 2008; Schwarzkopf and Farmer, 2008, ...

This distinction matters for several reasons:
- PL and LN come from very similar model and differences in initial conditions can lead to very different outcomes. PL exhibits a choice for x_min, below which the unit of observation is not feasible to exist (eg minimum city size, firm size, word length, ...). LN has no minimum size.
- PL and LN that look similar are the difference between infinite (PL) and large but finite variance (LN)
- shock propagation: when unit-level shocks are large enough to show aggregate perturbances if the distribution is powerlaw with infinite variance, while these shocks wash out fast when the distribution is log-normal. (Gabaix, 1999; Gabaix 2009; Acemoglu et al. 2012, ...).

I have encountered some issues which I would like to explore further:
1. The distinction between lognormal and powerlaw in the data is very sensitive to data truncation: in the above discussions, researchers have slightly different datasets, covering more or less of the population at hand. Left-truncation (i.e. observations not in the dataset because they are too small to be reported) can strongly drive the outcome of the fit, even when endogenizing the x_min cutoff. I have data on the universe of Belgian firms, much more complete than e.g. US Census data, where I have done some preliminary tests on this. The question is then: how to formalise this distinction and what are the theoretical and practical caveats to look out for when applying this method.
2. MLE fitting seems to be sensitive to the choice of units as well: rescaling a variable by a factor 1000, 1000000 etc seems to influence the endogenous x_min choice and hence the estimated parameter. This reminds me of some work on scaling invariance in negative binomial estimators. What is going on here?
3. Can we set up a model that generalises both? I've been looking at Levy stable distributions, but did not do anything with it yet.

Interested people:
* Corbain
* Laura
*Binyang
*Sahil
*Tirtha
*Glenn
*Junming

Literature:
Powerlaw fitting in empirical data - Clauset, Shalizi and Newman (2009): http://arxiv.org/abs/0706.1062

==Organ Transplant Analysis==
Over 120,000 people in the United States are currently on the waiting list for an organ transplant. The size of this waiting list relates to over 6,000 deaths a year while waiting for a transplant and tens of billions of dollars in government spending. I have access to the following data sets:
 
* All transplants performed in the US from October 1987 to June 2014 (including follow-up data)
* All living and deceased organ donors in the same time period (with follow-up data on living donors)
* Waiting list data for everyone who signed up for the list
* 2012 National Survey on Attitudes and Behaviors on Organ Donation
* Social media data relating to organ donation since 2008

Open to ideas and suggestions for the topic, there are a lot of interesting questions to investigate including cultural/racial/gender differences in organ donation. I have several preliminary reports and exploratory analysis done on differences in donors.

Second Meeting Thursday, June 11th 4:15 in the coffee shop!

===Matching Game===
Rules for the matching game:

Objective: Create the largest chain possible using blood types in the room.

Follow the blood type guidelines for donation to form a donor chain.
*Assume that self-matches are not possible, for example someone with a donor of type AB and a recipient of type AB can not match themselves and needs to find someone else to complete their chain.
*Create a donor chain to help as many people as possible.
*Post results below for the prize
*There will be several "good samaritan" donors which will donate and need nothing in return, this is a great way to start your chain. Using a good samaritan chain also means you don't need a donor for the end point.

====Game Workspace====
Use this workspace to post for and find matches!

==== Donor Chains ====
Post your chain here. Example is given below:

=====Example Chain 1=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| A
|-
| Laura
| AB
| B
|-
| Matt
| A
| AB
|}

=====Example Chain 2=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| None
|-
| Laura
| A
| B
|-
| Matt
| None
| A
|}

======Richard's Current Table======

Iterations: 100,578
Length: 36 (everyone that needed an organ got one)

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| X
|-
| MatthewO
| AB
| O
|-
| Sahil
| O
| AB
|-
| Alice
| AB
| O
|-
| Vanessa
| A
| AB
|-
| Cobain
| B
| A
|-
| Charon
| B
| B
|-
| Melissa
| A
| B
|-
| WillChang
| O
| A
|-
| Kleber
| O
| A
|-
| DanielF
| AB
| O
|-
| Jelle
| O
| AB
|-
| Glenn
| O
| A
|-
| Maria
| O
| A
|-
| MatthIn
| AB
| O
|-
| Brent
| O
| AB
|-
| Jeroen
| O
| O
|-
| Masa
| A
| O
|-
| Jae
| B
| A
|-
| Sam
| O
| B
|-
| MariePierre
| A
| O
|-
| Jakub
| O
| A
|-
| James
| AB
| O
|-
| Alejandro
| O
| AB
|-
| Havier
| AB
| O
|-
| Susanne
| A
| AB
|-
| Urs
| B
| A
|-
| Laurence
| O
| B
|-
| Martina
| A
| O
|-
| Richard
| A
| A
|-
| Carolina
| O
| A
|-
| AndySchauf
| A
| A
|-
| Chris
| O
| A
|-
| Michael
| O
| A
|-
| Nilton
| O
| B
|-
| Keith
| AB
| O
|-
|}

Unused:

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| JGab
| O
| X
|-
| LauraCondon
| O
| X
|-
| Maggie
| O
| X
|-
| Matthew
| O
| X
|-
|}

==Multi-dimensional social networks in the evolution, development and resilience of informal economies. ==
Informal economies are defined as economic activities that occur outside the purview of corporate public and private institutions. These types of economies proliferate where traditional economic actors are unable to productively exercise their activities, especially due to costly constraints (De Soto 2003). Firms may face adverse incentives to expand production by hiring more workers or incorporating more capital due to the low productivity of their workers or the predatory practices of rapacious elites or corrupt governments. Labor itself, i.e. people, may face hurdles in trying to make the jump towards entrepreneurial activities or jobs in the formal economy which allow the accumulation of experience, retirement savings, access to insurance or precautionary savings to face unexpected events (like disease, disability, etc.) due to lack of capital access (whether human and financial).

'''For this project, we propose a different interpretation: We seek to understand and model how multi-faceted social networks provide robust alternatives to formal economies, which far from being seen as degenerate forms of social organization, in many instances co-exist, challenge and compete with formal employment and economic activities. While some types of informal economies operate under adverse contexts, their resiliency may be understood as a type of adaptive fitness and not the mere result of stubborn cultural path-dependency. '''

For the most part, informal economies have been analysed as counterpoint to an ideal type of a formal economy with low levels of trust, respect of property rights, access to credit and public institutions - like the court systems (see Losby et al. 2002 for a review). However, informal economies have been coupled with their formal counterparts since the development of capitalism in the 19th Century. To name a famous historical example, Old London's East End's informal sector was described harshly by Engels (1844) in his famous tract, "The Condition of the Working Class in England". But almost fifty years later, in a new preface to the book, Engels recognized the progress that took place there under the aegis of working class organizations in the area.

Research has begun to catch on to this idea – developing a literature in the areas of informal risk sharing and remittances, which are in some sense informal counterparts to insurance and banking. Consumption patterns in poor, rural villages are remarkably smooth suggesting that risk-sharing measures are prevalent, although imperfect (Townsend). Field studies of networks underlying village risk sharing systems have found that households primarily receive help from existing social connections, such as friends and relatives, in the form of informal loans or transfers. Theoretical work in network economics, by authors such as Matt Jackson, found that certain empirically prevalent network structures may directly benefit the stability of favor exchange systems. Another line of inquiry focuses on remittance income, generally informal transfers across kinship ties. World Bank researchers have found that remittances from overseas migrants respond dramatically to regional income shocks, replacing upwards of 60% of lost income in households with international migrants. Further studies, cited below, have generalized those results. Furthermore, reductions in the cost of sending remittances, which occurs with the advent of mobile money, further mitigates exposure to risk in receiving households.

Also recently, authors have written on how communities come together in situations where formal economies are unavailable by external shocks (like disasters) or due to lack of access (due to conditions of abject poverty). In the case of the latter, Venkatesh (2008) provided gripping testimonies of how informal economies arose and developed in the South Side of Chicago in low trust environments via cash transactions and informal service contracts enforced by (and for the benefit of) local criminal gangs. These gangs were seen, surprisingly, as alternative coordinating mechanisms to settle claims between neighbors. In the case of the former, Storr and Grube (2013) in a series of papers has argued how "shared histories and perspectives, and the stability of social networks within the community" allows communities who suffered disasters to cope and endogenously resolve immediate and complex problems. Providing an example of these social networks in action, research has found that remittances flow quickly into areas affected by natural disasters when there is a technology in place for it to happen.

Taking these lines of inquiry together, the findings suggest that informal handling of risk takes place on a large scale and that social networks informally connect communities in ways that impact their economies. Recent trends suggest that the interaction of formal and informal economic processes is growing on a nationwide scale. First, the aggregate value of remittance flows is large and growing, nearing the value of foreign direct investment. Second, advancements that formalize previously informal transactions are expanding dramatically in emerging economies through various forms of branchless banking. National economies may be embedded in profoundly influential informal systems that have never been holistically studied.

On a more general note, this project touches lingering questions in economics. For example, some might argue that more stringent labor regulations should have increased informal employment, but in fact the opposite happened. And while the economic rationale for the former statement remains valid, we suggest that unions, as other types of social organizations, as examples of ways whereas people interact via organized networks, provide richer dimensions than those suggested by their interaction as mere economic agents. Hence, unions, as well as other types of faith-based, ethnic, community and other interest groups may provide ways of interaction that escape narrow economic outcomes.

====References====

• De Soto, Hernando (2000/2003) The Mystery of Capital. Basic Books.
• Losby, Jan; Else, John; Kingslow, Marcia; Edgcomb, Elaine; Malm, Erika and Vivian Kao (2002) The Informal Economy: A Literature Review. ISED Consulting and Research and the Aspen Institute Working Paper. http://www.kingslow-assoc.com/images/Informal_Economy_Lit_Review.pdf
• Townsend, Robert M. "Risk and Insurance in Village India." (1994)
• Fafchamps, Marcel, and Susan Lund. "Risk-sharing Networks in Rural Philippines." (2003)
• Weerdt, Joachim De, and Stefan Dercon. "Risk-sharing Networks and Insurance against Illness." (2006)
• Matthew O. Jackson, Tomas Rodriguez-Barraquer and Xu Tan. “Social Capital and Social Quilts: Network Patterns of Favor Exchange.” (2012)
• Yang D and H Choi."Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines"(2007)
• Kurosaki, Takashi. "Consumption vulnerability to risk in rural Pakistan." (2006)
• Jack, W, and T. Suri. "Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution” (2014)
• Engels, Friedrich (1844/1892) The Condition of the Working Class in England. http://www.gutenberg.org/files/17306/17306-h/17306-h.htm
• Venkatesh, Sudhir (2008) Gang Leader for a Day: A Rogue Sociologist Takes to the Streets. Penguin Books.
• Storr, Virgil and Laura Grube (2013) The Capacity for Self-Governance and Post-Disaster Resiliency. George Mason University, Department of Economics Working Paper 13-37. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2350830##
• Blumenstock, Joshua Evan and Fafchamps, Marcel and Eagle, Nathan. “Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda” (2011).
• Ratha, Dilip. "Workers’ remittances: an important and stable source of external development finance." (2005).
• Pénicaud, Claire, and Arunjay Katakam. State of the Industry 2013: Mobile Financial Services for the Unbanked. Rep. N.p.: GSMA MMU, 2013.

If interested, please list your name below.

'''Interested:'''
<li> Eloy Fisher (eloy.fisher@fulbrightmail.org) </li>
<li> Carolina Mattsson (carromattsson@gmail.com) </li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>

<li> Jakub Rojcek (jakub.rojcek@gmail.com) </li>

==Exploring Community Formation through Analysis of Scholarly Corpus (ArXiv)==

The Arxiv [http://arxiv.org/] is a free online repository of scientific preprint articles, mostly from physics and mathematics. It currently contains over 800,000 articles, dating back to 1991. Currently, a lot of the data associated with this repository is completely available: paper submission dates; full texts of papers; author names and coauthorship information. Additionally, there is some citations data available. (If necessary, I can also find papers' subdiscipline labels; submitting authors' email address domain names; other things.)

In the past I have been using these data to try and explore how communities of authors who have shared interests grow over time. For example, we can pinpoint the first ever paper about topological insulators, and search for all subsequent papers on that topic. As more papers are written, authors begin to join the field of research and to form strong ties by collaborating with one another. We can use the ArXiv data to visualize and analyze exactly how these communities form and grow.

===Brainstorming notes===
Isolation between topics, crossing interdisciplinary boundaries, finding (topological) separation distance between disciplines or research groups

Tipping points - can we look for separation of or mergers of two groups or disciplines?

Can we identify key papers (or groups of papers) that initiate ties between fields.

Sentiment analysis: can we identify when a paper's citation is an endorsement or a refutation?

Tools for Analysis: Change point detection; relational event models

Can we find more comprehensive/better citation data?

Lucene and indexing tools:
- Can we re-index our text database after removing stop words?
- Can we index the titles and abstracts?
- Elastic Search - tool for interacting with Lucene

This set of text-processing [http://alias-i.com/lingpipe/demos/tutorial/read-me.html tutorials] is pretty handy for background info and inspiration. The software package doesn't simply plug-into a Lucene/Solr/Elastic Search index, but it could be done. This package from [http://nlp.stanford.edu/software/ Standford] seems very capable.

===Next meeting===
Thursday @ 9AM in Coffee Shop

== PolComplex - Epistemic communities and policy dynamics in the UK Parliament ==

Over the last twenty years, there has been extensive debate about what the core topics in public policy are, and if they change according to type and number. Furthermore, it is still not clear how policy topics mutate and diffuse over time. Human-based text analysis of governmental documents has shed only some light on these research questions: 21 general topics have been proposed, while other accounts tend to restrict it to about five main clusters. Yet, this approach has not been able to devise a reliable and systemic method to capture topic dynamics, diffusion, and their relation to the human actors that talk about them.

This project is an extension of an exploratory research that intends to overcome such limitations, through the application of natural language processing (more specifically, dynamic topic modeling and sentiment analysis), topological and network analysis on a dataset containing UK House of Commons debates ranging from 1975 to 2014. The aim is manyfold. We hope to identify the structure and dynamics of epistemic policy communities - i.e. of political actors revolving around similar policy interest -, and how these relate to factors such as party membership, seniority, relevant historical events.

Some references:

[http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.734168 Taddy (2013), "Multinomial Inverse Regression for Text Analysis"] 

[https://www.cs.princeton.edu/~blei/papers/BleiLafferty2007.pdf Blei and Lafferty (2007), "A Correlated Topic Model of Science"] 

== Genomic variations from a chaotic mapping ==

Following Dabby CHAOS 6 (2), 1996 Musical variations from a chaotic mapping, this project will explore genomic variations in key metabolic genes that are known to be widely spread across bacterial phylogeny (e.g. the Nif cluster which is used in nitrogen fixation). There are several ways that genomic data can be treated as "music":

1) Nucleotide: 4 notes - A, G, C, T 
2) Amino Acids: 20 notes 
3) codon triplets: 64 notes 
4) tetra nucleotides: 256 notes. 

One possible goal is to take a seed sequence from some random organism and generate new variations. Then using homology search (e.g. BLAST) or phylogenetics, determine if that variation exists in nature? If not, perhaps model the potential protein folding (if possible?) to determine if that variation could exist in nature. This is VERY preliminary and can go in many directions.

If interested please list name below or contact Jarrod at jscott@bigelow.org

Reading
http://digitalcommons.olin.edu/cgi/viewcontent.cgi?article=1000&context=ahse_pub&sei-redir=1&referer=https%3A%2F%2Fscholar.google.com%2Fscholar%3Fhl%3Den%26q%3Ddabby%2Bchaos%26btnG%3D%26as_sdt%3D1%252C32%26as_sdtp%3D#search=%22dabby%20chaos%22

== Free Energy Theory ==
The Free Energy (FE) minimisation framework tries to explain how biological systems (such as a cell or a brain) self-organise in order to occupy the (often very limited number of) non-equilibrium states that minimise free energy. This is also known as active inference. A simple corollary of active inference is that agents behave as to minimise their prediction error, or the difference between prediction and reality. Thermodynamic free energy is a measure of energy available in a system to do useful work. This can be framed in an information theoretic setting, as the difference between how the world is being represented and how it actually is. A better fit means a lower information-theoretic free energy, as more resources are being put to ‘good use’ in representing the world. The overarching logic of FE theory is that a better model of the world help maintain structure and organisation, which ultimately helps the system resist increases in entropy.
 
 
This is not a set-in-stone project with any concrete aim (yet). We are a few people interested in exploring the theoretical and practical implications of these ideas, and you're more than welcome to join in!
 
 
'''Interested:'''
 
<ul>
<li> Jelle Bruineberg (j.bruineberg@gmail.com ) </li>
<li> Tobias Morville (tobiasmorville@gmail.com) </li>
<li> Susanne Pettersson (guspsusa85@student.edu.se) </li>
<li> Maggie Simon (margaret.w.simon@gmail.com) </li>
<li> Martina Steffen (martinasemail@gmail.com) </li>
<li> Sahil (sahilgar@usc.edu) </li>
<li> Tirtha (Tirtha.Bandy@csiro.au) </li>
<li> Anna (annza944@gmail.com) </li>
<li> Matt O (mmosmond@gmail.com) </li>
<li> Will Chang (williamkurtischang at gmail dot com) </li>
<li> Cobain (mrdc1g10@soton.ac.uk) </li>
</ul>
Reading:
 
http://rsif.royalsocietypublishing.org/content/10/86/20130475 
http://arxiv.org/abs/1503.04187 
http://www.mdpi.com/1099-4300/15/1/311 

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
'Carolina'
'Alice'
'Federico'
'Jean Gab'
'Sola'
'Ilaria'
'Daniel T'

==Comparison of Network vs Scaling Theory based Models in Ecology (Cobain - mrdc1g10@soton.ac.uk)==

One of the biggest difficulties ecologists face is trying to understand the ecosystem dynamics based on very little biological information (compared to the size of the system) - observational data is logistically difficult and can be very expensive to acquire, as well as often being very time consuming. In terms of modelling, to overcome this problem, two approaches have been utilised. 

The first is a network based approach whereby the nodes represent biomass density of a particular species (or a higher level taxanomic group and/or resources) and edges as the trophic interactions (who eats who). This method depends on what we know about the trophic behaviour of the species involved (which is often very limited and there can be many species to parameterise), and represents only the central tendency of what could potentially be very diverse behaviour within and between populations. 

The second approach is to use scaling theory to describe average trophic interactions and other biological processes based on individual organism body size along the size continuum, viewing the community as a very size structured dynamical system. This requires less specific knowledge about the organisms in the community ''per se'', however this again represents only the central tendency of a given size class of what could be very diverse behaviour. Functional differences can be potentially introduced (coupled benthic-pelagic systems for example), but to the detriment of braking down the predictability of the well known allometric scaling laws with size as specificity increases. 

There has been little to no comparison of these two methods of modelling ecological systems (at least to my knowledge) and the question arises that, given the same starting information, how well do both approaches model the dynamics of an ecosystem, given the limited biological information we have? Is one approach better at capturing energy flow through the system / community structure and stability etc. compared to the other? If the models vary then such information will better inform ecologists on which to use depending on what type of questions they are trying to answer. 

Therefore, the project proposed here aims to investigate these two methods. The community that is used to seed an experimental mesocosm setup (mesocosms are large tank set-ups designed to reflect the complexity of natural ecosystems but still being able to be artificially controlled and are still relatively simple), will be input into two models based on the separate approaches and then run to steady state. The model outputs will then be compared and contrasted with eachother as well as the mesocosm community at steady state. Further work may include examining perturbation dynamics, dependent on what data we have available.

'''Interested: Name / Email''' 
Martina Steffen (martinasemail@gmail.com)

==Dynamics of homicide (Matthew Ingram - mingram@albany.edu)==

Integrating temporal, spatial, and multi-level concepts

1:30pm in the coffee shop

Am interested in the discussion on this project...Sola Omoju

Interested - Nilton Cardoso

==Multiplex Adaptive Networks (Daniel - dtc65@cornell.edu)==

7 pm in the coffee shop

==Modeling brain diseases (or cancerous bio pathways) (Sahil - sahilgar@usc.edu)==
Interested people can put a meeting time as per their convenience here.
 
No meeting time indicated
 
One potential meeting time can be 3pm in coffee shop ?
 
Information theoretic algorithms can also be explored for the problem.
 
Discovering Structure in High-Dimensional Data Through Correlation Explanation. http://papers.nips.cc/paper/5580-positive-curvature-and-hamiltonian-monte-carlo
 
Maximally Informative Hierarchical Representations
of High-Dimensional Data. http://jmlr.org/proceedings/papers/v38/versteeg15.pdf

 
'''Interested:'''
 
<ul>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> Laura Condon (lcondon@mymail.mines.edu)</li>
</ul>
 
 

==Analysis of UK parliament speeches 1935-2014 (Stefano - etstefano@gmail.com)==

No meeting time indicated

==Mapping Complexity/Human Knowledge as a Complex Adaptive System (radjohn@live.com)==
2pm/Wed 6/10 in Conference Room (Tentative)

==Navigating Music, Brain and The Edge of Chaos (radjohn@live.com)==
10:45am/Wed 6/10 in Conference Room
9:00am/Thu 7/10 at the Coffee Shop

'''Interested:'''
Sahil Garg

==Analysis of rule-based modeling for dopamine-dependent synaptic plasticity (emilia.m.wysocka@gmail.com)==

----------------------------------------------------------------------------------
Modeling and analysis of phenomenons and evolution of stochastic, combinatorially complex signalling systems in a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system).

----------------------------------------------------------------------------------
Rule-based modeling features:

*biological systems as concurrent processes
*dynamics of post-translational modifications
*domain availability
*competitive binding
*causality and intrinsic structure
*binding sites
*interaction rules replace reaction equations
*infinite number of reactions with a small and finite number of rules
*reduction of parameter space
*“don't care don't write” - adjustable rule contextualization
*single reaction rule and parameters generalize classes of multiple rules
*modular and extensible language
*specification language & simulation/integration environment
*static and causal analysis
*Kappa/KaSim & BioNetGen/NFsim -- specification language/network-free simulator

----------------------------------------------------------------------------------
----------------------------------------------------------------------------------
Meetings:
----------------------------------------------------------------------------------
* WEDNESDAY 11/06/2015

Addressing problems in terms of the other aspects of the projects apart from the biological questions and verification (matching results to some published experiments or known behaviour). So my plan is as follows:

* divide the group (roughly) into people who look in to biological meaning and validation and the one which tries to do the analysis of system phenomenons and evolution of stochastic, combinatorially complex signalling systems in both a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system) - all possible states, scenarios of the system abstracted from the biological meaning.

Things that could be modelled (look in dropbox folder: https://www.dropbox.com/sh/g080w60pt2vgi7v/AACHlMf2JwpP2EZqKq7Di6N2a/possible%20models?dl=0):

* to make things easier and have the modeling part done quickly- base the model on the dopamine-related synaptic plasticity (SBML ODE model): http://www.ebi.ac.uk/biomodels-main/MODEL1101170000;

* I found a series of papers suitable to the subject of the Complex Systems course, e.g.: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC344803/. I was thinking to even try to compare higher level brain data (networks, spiking ..) with purely molecular model

* noise and low copy number (http://www.princeton.edu/~wbialek/our_papers/bialek+setayeshgar_08.pdf); this is quite interesting as for sufficiency of binding events -> http://www.princeton.edu/~wbialek/our_papers/bialek+ranganathan_07.pdf

* spontaneous flipping of interactions (phosphorylations and others) between proteins, described by the god of non-linear dynamics (S. Strogatz)

----------------------------------------------------------------------------------
* THURSDAY 11/06/2015 : http://doodle.com/se23ibwtkrkccs4s

----------------------------------------------------------------------------------
Some refs:
*https://www.dropbox.com/sh/g080w60pt2vgi7v/AADUy7Ge-J-oSYkyTeYOo7V8a?dl=0)
*http://www.pps.univ-paris-diderot.fr/~jkrivine/homepage/Teaching.html

==Ecology non-working group (williamkurtischang at gee-mail dot com)==
[[Informal ecology interest group]].

==Making Supply Chains Resilient to Disasters (mh2905 att columbia dott edu)==
'''Key words''': resilience, natural hazards, supply chains, interdependency, interconnected risks, cascading failures

'''Rational''': I am interested in examining what kinds of network structures and features contribute to increasing resilience of supply chains to natural disasters. I believe this area of work is important because regional disasters negatively impact the global economy through disruptions in supply chain networks. The pioneer study published in Nature urges the need for making supply chains climate-smart (Levermann 2014). Also in the industry, the World Economic Forum published a report to address this issue in 2013. Few researches, however, assess and model the impacts of adverse weather on supply chains. I would like to evaluate the impacts based on modeling.

'''Data set''': Supply chains data of a multinational manufacturing company.Global climate data, disaster data, etc.

'''Possible techniques''': Agent-Based Model, Complexity science, network theory and evolution, complex adaptive systems, GIS, operations research, manufacturing engineering, and I am open to any techniques.

'''Myself''': As I joined the program yesterday, please find my background here.
http://tuvalu.santafe.edu/events/workshops/index.php/Masahiko_Haraguchi

'''Please put your names below if you want to be informed about this project by email'''

'''Interested''':
Masa Haraguchi

==From Ethnic Diversity to Religious Zeal: Retrospective/Predictive Construction of the World Ethnic Map (vdzutsati@asu.edu)==

There is a new dataset on ethnic groups (2014), which claims to include all ethnic groups of the world http://www.cidcm.umd.edu/mar/amar_project.asp. There are also several cross national datasets on religion and other variables of interest, normally, socio-economic http://www.thearda.com/Archive/CrossNational.asp. Coming from social sciences I thought it would be really interesting to apply methods and perspectives from other disciplines to social science data.

One idea that I have in mind is to explore how ethnicity and religion interact.

Quick empirical checks indicate that Subsaharan Africa is home to about 1,500 ethnic and subethnic groups, in comparison to about 90 ethnic groups in the Middle East and North Africa. India alone accounts for nearly 2,000 ethnic and subethnic groups, while Europe, including all the countries of the former USSR and large immigrant groups from other parts of the world, has only about 260 ethnic groups. The picture that emerges from this simple comparison is that the spread of Abrahamic religions appears to be associated with the high depletion rate of ethnic groups. The exception of China, which has little more than 50 ethnic groups, can be explained by the country’s long history of a centralized state.

The idea then is to assume that we have had the control group of nations that did not experience the influx of Abrahamic religions (or more precisely received limited exposure to them) and the experimental group that was exposed to the spread of Abrahamic religions. Since we know what the distribution of ethnic groups in the control group is we can project what the distribution of ethnic groups in the experimental group would be, if the group were not exposed to Abrahamic religions.

Of course, we could go in the other direction too and make a projection about future – what would happen to ethnic groups if all of them adopted an Abrahamic religion.

One of the challenges in this project is absence of a dynamical system, I.e. We don’t have data on how these things changed over time, but I still think that projection about the future and the past would be kinda cool:)

'''Interested''':

==Nationalist vs religious rebel violence (vdzutsati@asu.edu)==

I would like to propose yet another possible project. This time it’s on rebel violence. I have a large dataset on rebel (and government) violence (~1 million observations). The paper that used this data was published couple of months ago (Islamists and Nationalists: Rebel Motivation and Counterinsurgency in Russia’s North Caucasus). The dataset contains event counts, GIS data, demographic and economic characteristics and some other stuff. The primary hunch of the paper was to discover the differences between nationalist and Islamist rebels.

Given all the cool methods we are learning here, my first instinct is to use the methods and tools (TISEAN?) to explore and discover whether nationalist violence is substantively different from religious violence and try to answer why.

One problem that I can see is that the event coding was done by a machine, so this may have “contaminated” the data. But if both religious and nationalist data were contaminated equally then the difference should still be evident.

'''Interested''':

==[[Archived Projects ]]==

Complex Systems Summer School 2015-Projects & Working Groups

2015-07-02T21:24:39Z

Rbarnes: /* Richard's Current Table */

{{Complex Systems Summer School 2015}}

==[[Reservations for Evans Science rm. 215]]==

Click on the title link to reserve Evans Science rm. 215 for group usage.

==Californian Drought Model==
Problem definition:
Water is an ongoing problem in California. Although corrective measures are taken for some years, periods of droughts are depleting water ground levels to not sustainable levels.
Aims:
A Netlogo simulation on how likely Californian agents evolve with regards to drought. The ABM could be used to explore the potential impacts of forms of regulation.
Outcomes:
Create an exploratory agent based model based on real data, enabling the possibility of simulating different methods of regulation, including feedbacks between the economic and hydrologic systems.

Extension 1: We will conduct a network analysis of Twitter data (hashtags #drought #California) to explore the social dynamics and information flows between stakeholders.

Extension 2: Phase-space reconstruction of groundwater level time series data, comparing the dynamics in different parts of the Central Valley.

==City Resilience==
'''Summary:''' This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities. 
'''Contact:''' Richard Barnes (rbarnes@umn.edu) 
'''Participants:''' Alex Tejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang, Martina Steffen, Juan Carlos Castilla, Brent Schneeman 
'''Wiki page:''' [[Resilience of Cities]]

==Complex Adaptive Systems and the Narrative approach: transdisciplinary methodologies for Complexity Science==
The narrative approach and it causality is different from causality in logico-scientific approaches. Making bridges among methodologies transfers knowledge, encourages important questions and switches philosophical paradigms. Formal frameworks of C(A)S can realy be complemented with the narrative, and actually should. The narrative approaches in Complexity Science are an emerging trend for fund-granting, and is really up to date to process documentary and speech-based data, reveal hidden meanings, distinguish causes from effects in overlapping systems of realms - "at the edge" of technology, social, economic, scientific, legal and other domains. The role of time in coding, intuitively generated conditional rules and computational paths. Combinations of C(A)S and the narrative is combination of quantitative and qualitative data and thinking in original - out of convertation and information loss. Synthesis of sciences is what it is about.

'''Questions''':
How to combine CS approaches (in particular CAS) and the narrative ones in a rule-based way? What to start from? What vizualizations there can be designed and used? Other questions at your descrete.

'''Some references''':
- Non-Equilibrium Social Science in ICT and Economics, CORDIS, EU: http://cordis.europa.eu/project/rcn/102344_en.html, http://www.nessnet.eu/

- Combining Complexity Theory with Narrative Research: https://youtu.be/pHjeFFGug1Y

- Haridimos Tsoukas and Mary Jo Hatch (2001), "Complex thinking, complex practice: The case for a narrative approach to organizational complexity: http://www.brown.uk.com/teaching/qualitativepostgrad/tsoukas.pdf

- David Christian, "Big History", Astrophysics, Chemistry, Biology, Information, emergence of life, technology: https://youtu.be/GlmvFjFceD8

'''Contact''': Anna (annza944@gmail.com)

Meet on Monday over lunch

'''Interested''': Marie Pierre, Jeroen, Jim, Melissa

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
 
'Carolina'
 
'Alice'
 
'Federico'
 
'Jean Gab'
 
'Sola'
 
'Ilaria'
 
'Daniel T'
 
'Sahil Garg

==Ebola==
The 2014-15 Ebola virus disease (EVD) outbreak in West Africa presented both unique opportunities and unique challenges to the epidemiological modeling community. For the first time during an emerging infectious disease outbreak, high resolution data--from a variety of sources--were made available to the academic community and many public health decision makers genuinely engaged with mathematical and computational modelers. However, the popular and scientific press were highly critical of most models ability to project the outbreak's course. The following key and open questions seem ripe for investigation using a complex adaptive systems lens:
 
1) What features of EVD transmission are most problematic for reliable, robust forecasting: changing behavior, intervention, viral evolution, complex social networks, etc?
 
2) How/can we use digital data to either improve forecasts or inform model selection?
 
3) Can one quantify the value of additional information in real-time?
 
'''Contact:''' Samuel Scarpino, SFI Omidyar Fellow, Santa Fe Institute - scarpino@santafe.edu
 
 
'''Interested:'''
 
Marie-Pierre Hasne 
Chris Verzijl 
Junming Huang 
'Sola Omoju 
Christine Harvey 
Daniel Citron (dtc65@cornell.edu) 
William Chang (williamkurtischang at gee-mail)

==Effect of landscape topography on vegetation connectivity and navigability==
''Context''
 
Landscape topography influences the dynamics of the processes that take place on it. Evolution of ecosystems networks, river networks, vegetation cover type, microclimate cycles are all interlinked deeply to the local landscape topography.

In addition to the landscape these networks are also influenced by each other conditioning the emergence and stability of ecosystems, and subsequently the behaviour of agents in the ecosystem like migration pathways of animal herds, human settlement patterns etc.

Connectivity patterns emerge from the interaction of these processes, and thus a better understanding and quantification of those patterns is critical to understanding the dynamics of the system.

 
''How do we want to approach the problem''
 
In this project, we will randomly generate landscape topography and subsequent vegetation cover from a set of parameters from known geological and biological processes. The generated data set will then be used to investigate the following questions:
 
(1) What is the connectivity of landscape patterns that emerge at different scales using different techniques such as clustering analysis, percolation theory or network theory, and can we quantify them ?
 
(2) What is the navigability of biological agents (e.g animals, humans, robots!) under such landscape patterns. We can compare mobility trails in existing landscapes to validate our hypothesis. We propose to use the tools like ABMs to simulate and characterise mobility success.
 
(3) We further aim to compare the measures of navigability with the metrics of connectivity, establishing a framework of comparison.
 

''Contact:''
 tirtha.bandy@csiro.au
 alej.tejedor@gmail.com

''Meetings''
(1) Wednesday 1pm Coffee shop

People Interested 
Martina

==[[Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe]]==
Click on the link to go to the project page for meetings, project details, and progress.
 
contact: mveitzel@ucsc.edu
 

==Mapping Complexity/Human Knowledge as a Complex Adaptive System‏==
''Context''
 
Ants leave pheromone trail patterns which they are aware of only in a local sense. They do not have the cognitive faculties to step back and look at the trails and grasp the ant-trail network as a totality. Also, the artifacts they leave behind are physical entities which then provides the aggregate feedback to the aggregate ant body to then feed the evolution of the ant body as a CAS system. In contrast, humans do have the requisite cognitive abilities. The "pheromone trails" we leave behind are the knowledge trails coded in symbolic knowledge artifacts. In contrast to the physical artifacts that ants leave behind, the knowledge artifacts that we leave behind are far more flexible and potent, both at the aggregate as well as at the individual levels. But like the ants, until recently, we did not have the means to step back and map the knowledge "pheromone trails" to obtain the big picture and its global/local dynamics. The burgeoning field of scientometrics is making available visualization tools to help us map and study the evolutionary dynamics of the knowledge network structures.
 
''Data and Questions''
 
The goals of this project include
<ol>
<li>Extract the terms from approximately 1600 working papers published by SFI</li>
<li>Map the intra/inter conceptual network structures</li>
<li>Study the evolution of these structures across time</li>
<li>High-light the gap-closure of knowledge reverse-salients (if any)</li>
<li>Capture any of the network patterns that repeat</li>
<li>Study the diffusion of concepts across the network</li>
<li>Provide visualization tools for navigating the complexity corpus, etc</li>
</ol>
 
''Possible methods''
 
Latent Semantic Analysis (LSA) and Latent Document Analysis (LDA)
 
References:
<ol>
<li> SFI Working Papers: http://www.santafe.edu/research/working-papers/</li>
<li> Atlas-Science-Visualizing: http://www.amazon.com/Atlas-Science-Visualizing-What-Know/dp/0262014459</li>
<li> Atlas-Science-Visualizing WebSite: http://scimaps.org/atlas</li>
<li> Mapping-Scientific-Frontiers-Knowledge-Visualization: http://www.amazon.com/Mapping-Scientific-Frontiers-Knowledge-Visualization/dp/1447151275</li>
<li> Katy Börner presents at Science of Science: https://www.youtube.com/watch?v=pzCqGBNzomE</li>
<li> Scholarly Data, Network Science, and (Google) Maps:https://www.youtube.com/watch?v=vos5QBDywMM</li>
<li> LSA Video Lect: http://videolectures.net/slsfs05_hofmann_lsvm/</li>
<li> What is LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf</li>
<li> LSA Wiki:http://en.wikipedia.org/wiki/Latent_semantic_analysis</li>
<li> LDA: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm</li>
<li> Fusari, A. (2014). Methodological Misconceptions in the Social Sciences: Rethinking Social Thought and Social Processes: http://www.springer.com/us/book/9789401786744 </li>
<li> Kirsh, D. (2013). Thinking with external representations. Cognition Beyond the Brain: http://adrenaline.ucsd.edu/kirsh/Articles/Interaction/thinkingexternalrepresentations.pdf </li>
<li> Holland, J. H. (1992). Adaptation in natural and artificial systems: https://mitpress.mit.edu/index.php?q=books/adaptation-natural-and-artificial-systems </li>
<li> Where to start with text mining. http://tedunderwood.com/2012/08/14/where-to-start-with-text-mining/ </li>
<li> Singular Value Decomposition Tutorial https://www.ling.ohio-state.edu/~kbaker/pubs/Singular_Value_Decomposition_Tutorial.pdf </li>
<li> Latent Semantic Analysis (LSA) Tutorial: http://www.puffinwarellc.com/index.php/news-and-articles/articles/33-latent-semantic-analysis-tutorial.html </li>
<li> LSA in Detail: http://www2.denizyuret.com/ref/berry/berry95using.pdf </li>
<li> Web Based LSA: http://webapp1.dlib.indiana.edu/newton/lsa/index.php </li>
<li> Another Technique: t-Distributed Stochastic Neighbor Embedding (t-SNE): http://lvdmaaten.github.io/tsne/ </li>
<li> tf–idf: https://en.wikipedia.org/wiki/Tf%E2%80%93idf </li>

</ol>
 
'''Interested:'''
 
<ul>
<li> John Thomas (radjohn@live.com) </li>
<li> Haitao Shang (hts@mit.edu) </li>
<li> Christopher Verzijl (cjo.verzijl@gmail.com) </li>
<li> Anna Zaytseva (annza944@gmail.com) </li>
<li> Penny Mealy (penny.mealy@gmail.com) </li>
</ul>
 
 

==Navigating Music, Brain and The Edge of Chaos ‏==
''Context'' 
[[Navigating Music, Brain and The Edge of Chaos]]
 
Brain Sciences have revealed that we/it lives "on the edge of chaos" exhibiting "self-organized criticality" that is tentatively balanced between normalcy and madness. Over the course of history, humans have used various agents and activities to shape, influence and control this living-chaos ranging from substances such as caffeine, sugar, drugs etc., to activities such as the arts (including music), social-discourse/therapy, meditation etc. Of these, music has a distinct role in shaping our moods and helping us transition between different mental states, as well as maintain it for extended periods of time. Clearly we have been using music to help us control and shape the internal chaos. But until recently, the quantitative instrumentation of this massively complex system that comprises of close to a 100-billion neurons networked into a 1000-trillion synaptic edifice has not been available to the common man. But of late, affordable, wearable EEG's are available on the market, thus making the quantitative study of the influence of music in brain dynamics feasible on a large-scale/crowd-sourcing sense. To help come to terms with the complexity of our 1000-trillion synaptic edifice, we need to gather data on a vast scale. The proposed research is a proof-of-concept, exploratory foray into making this happen.
 

History of music in 5 min
https://www.youtube.com/watch?v=Gt2zubHcER4

'''Interested:'''
 
<ul>
<li> Sara Lumbreras (sara.lumbreras@gmail.com) </li>
<li> Ilaria b (bertazzi.ilaria@alice.it) </li>
<li> Braun, Urs (urs.braun@zi-mannheim.de) </li>
<li> John Thomas (radjohn@live.com) </li>
<li> Glenn Magerman (glenn.magerman@kuleuven.be) </li>
<li> Daniel Friedman (DanielAriFriedman@gmail.com)</li>
</ul>
 
 

==Powerlaw fitting and alternative distributions - Theory/statistics ==
Clauset, Shalizi and Newman (2009) propose a maximum likelihood method to estimate the powerlaw exponent of a variable of interest. This is a great improvement on earlier methods such as OLS that dominated the literature up to then. However, one can fit a powerlaw to any dataset and the most we can say is that our observations are consistent with the hypothesis that x is drawn from a powerlaw distribution. One easily implementable method to compare the powerlaw fit to other fits is then a likelihood ratio test for both models.

One particular discussion is the distinction between a powerlaw (PL) and a log-normal (LN) fit. For an avid discussion between both fits on city size and Zipf's law, see Eeckhout (2004, 2009) and Levy (2009), where the discussion now settled on city sizes following a log-normal distribution instead of a powerlaw. Similarly for the discussion on firm size distribution: Simon and Bonini, 1958; Ijri and Simon, 1977; Stanley et al., 1995; Sutton, 1997; Axtell, 2001; Okuyama et al., 1999; Cabral and Mata, 2003; Gaffeo et al., 2003; Aoyama et al., 2004; Fujiwara et al., 2004a,b; Kaizoji et al., 2006; Takayasu et al., 2008; Duchin and Levy, 2008; Schwarzkopf and Farmer, 2008, ...

This distinction matters for several reasons:
- PL and LN come from very similar model and differences in initial conditions can lead to very different outcomes. PL exhibits a choice for x_min, below which the unit of observation is not feasible to exist (eg minimum city size, firm size, word length, ...). LN has no minimum size.
- PL and LN that look similar are the difference between infinite (PL) and large but finite variance (LN)
- shock propagation: when unit-level shocks are large enough to show aggregate perturbances if the distribution is powerlaw with infinite variance, while these shocks wash out fast when the distribution is log-normal. (Gabaix, 1999; Gabaix 2009; Acemoglu et al. 2012, ...).

I have encountered some issues which I would like to explore further:
1. The distinction between lognormal and powerlaw in the data is very sensitive to data truncation: in the above discussions, researchers have slightly different datasets, covering more or less of the population at hand. Left-truncation (i.e. observations not in the dataset because they are too small to be reported) can strongly drive the outcome of the fit, even when endogenizing the x_min cutoff. I have data on the universe of Belgian firms, much more complete than e.g. US Census data, where I have done some preliminary tests on this. The question is then: how to formalise this distinction and what are the theoretical and practical caveats to look out for when applying this method.
2. MLE fitting seems to be sensitive to the choice of units as well: rescaling a variable by a factor 1000, 1000000 etc seems to influence the endogenous x_min choice and hence the estimated parameter. This reminds me of some work on scaling invariance in negative binomial estimators. What is going on here?
3. Can we set up a model that generalises both? I've been looking at Levy stable distributions, but did not do anything with it yet.

Interested people:
* Corbain
* Laura
*Binyang
*Sahil
*Tirtha
*Glenn
*Junming

Literature:
Powerlaw fitting in empirical data - Clauset, Shalizi and Newman (2009): http://arxiv.org/abs/0706.1062

==Organ Transplant Analysis==
Over 120,000 people in the United States are currently on the waiting list for an organ transplant. The size of this waiting list relates to over 6,000 deaths a year while waiting for a transplant and tens of billions of dollars in government spending. I have access to the following data sets:
 
* All transplants performed in the US from October 1987 to June 2014 (including follow-up data)
* All living and deceased organ donors in the same time period (with follow-up data on living donors)
* Waiting list data for everyone who signed up for the list
* 2012 National Survey on Attitudes and Behaviors on Organ Donation
* Social media data relating to organ donation since 2008

Open to ideas and suggestions for the topic, there are a lot of interesting questions to investigate including cultural/racial/gender differences in organ donation. I have several preliminary reports and exploratory analysis done on differences in donors.

Second Meeting Thursday, June 11th 4:15 in the coffee shop!

===Matching Game===
Rules for the matching game:

Objective: Create the largest chain possible using blood types in the room.

Follow the blood type guidelines for donation to form a donor chain.
*Assume that self-matches are not possible, for example someone with a donor of type AB and a recipient of type AB can not match themselves and needs to find someone else to complete their chain.
*Create a donor chain to help as many people as possible.
*Post results below for the prize
*There will be several "good samaritan" donors which will donate and need nothing in return, this is a great way to start your chain. Using a good samaritan chain also means you don't need a donor for the end point.

====Game Workspace====
Use this workspace to post for and find matches!

==== Donor Chains ====
Post your chain here. Example is given below:

=====Example Chain 1=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| A
|-
| Laura
| AB
| B
|-
| Matt
| A
| AB
|}

=====Example Chain 2=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| None
|-
| Laura
| A
| B
|-
| Matt
| None
| A
|}

======Richard's Current Table======

Iterations: 4638
Length: 34

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| X
|-
| Glenn
| O
| A
|-
| Havier
| AB
| O
|-
| Alejandro
| O
| AB
|-
| DanielF
| AB
| O
|-
| Sahil
| O
| AB
|-
| Alice
| AB
| O
|-
| Vanessa
| A
| AB
|-
| Cobain
| B
| A
|-
| Sam
| O
| B
|-
| Urs
| B
| A
|-
| Nilton
| O
| B
|-
| Charon
| B
| B
|-
| Melissa
| A
| B
|-
| AndySchauf
| A
| A
|-
| Jakub
| O
| A
|-
| Masa
| A
| O
|-
| Carolina
| O
| A
|-
| Jae
| B
| A
|-
| Laurence
| O
| B
|-
| Martina
| A
| O
|-
| Chris
| O
| A
|-
| Jeroen
| O
| O
|-
| Keith
| AB
| O
|-
| Susanne
| A
| AB
|-
| Michael
| O
| A
|-
| MariePierre
| A
| O
|-
| Richard
| A
| A
|-
| Kleber
| O
| A
|-
| Maria
| O
| A
|-
| MatthIn
| AB
| O
|-
| Brent
| O
| AB
|-
| WillChang
| O
| A
|-
| MatthewO
| AB
| O
|-
|}

Unused (note that there are enough altruistic donors that everyone can be saved):

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| James
| AB
| O
|-
| Jelle
| AB
| X
|-
| JGab
| O
| X
|-
| LauraCondon
| O
| X
|-
| Maggie
| O
| X
|-
| Matthew
| O
| X
|-
|}

==Multi-dimensional social networks in the evolution, development and resilience of informal economies. ==
Informal economies are defined as economic activities that occur outside the purview of corporate public and private institutions. These types of economies proliferate where traditional economic actors are unable to productively exercise their activities, especially due to costly constraints (De Soto 2003). Firms may face adverse incentives to expand production by hiring more workers or incorporating more capital due to the low productivity of their workers or the predatory practices of rapacious elites or corrupt governments. Labor itself, i.e. people, may face hurdles in trying to make the jump towards entrepreneurial activities or jobs in the formal economy which allow the accumulation of experience, retirement savings, access to insurance or precautionary savings to face unexpected events (like disease, disability, etc.) due to lack of capital access (whether human and financial).

'''For this project, we propose a different interpretation: We seek to understand and model how multi-faceted social networks provide robust alternatives to formal economies, which far from being seen as degenerate forms of social organization, in many instances co-exist, challenge and compete with formal employment and economic activities. While some types of informal economies operate under adverse contexts, their resiliency may be understood as a type of adaptive fitness and not the mere result of stubborn cultural path-dependency. '''

For the most part, informal economies have been analysed as counterpoint to an ideal type of a formal economy with low levels of trust, respect of property rights, access to credit and public institutions - like the court systems (see Losby et al. 2002 for a review). However, informal economies have been coupled with their formal counterparts since the development of capitalism in the 19th Century. To name a famous historical example, Old London's East End's informal sector was described harshly by Engels (1844) in his famous tract, "The Condition of the Working Class in England". But almost fifty years later, in a new preface to the book, Engels recognized the progress that took place there under the aegis of working class organizations in the area.

Research has begun to catch on to this idea – developing a literature in the areas of informal risk sharing and remittances, which are in some sense informal counterparts to insurance and banking. Consumption patterns in poor, rural villages are remarkably smooth suggesting that risk-sharing measures are prevalent, although imperfect (Townsend). Field studies of networks underlying village risk sharing systems have found that households primarily receive help from existing social connections, such as friends and relatives, in the form of informal loans or transfers. Theoretical work in network economics, by authors such as Matt Jackson, found that certain empirically prevalent network structures may directly benefit the stability of favor exchange systems. Another line of inquiry focuses on remittance income, generally informal transfers across kinship ties. World Bank researchers have found that remittances from overseas migrants respond dramatically to regional income shocks, replacing upwards of 60% of lost income in households with international migrants. Further studies, cited below, have generalized those results. Furthermore, reductions in the cost of sending remittances, which occurs with the advent of mobile money, further mitigates exposure to risk in receiving households.

Also recently, authors have written on how communities come together in situations where formal economies are unavailable by external shocks (like disasters) or due to lack of access (due to conditions of abject poverty). In the case of the latter, Venkatesh (2008) provided gripping testimonies of how informal economies arose and developed in the South Side of Chicago in low trust environments via cash transactions and informal service contracts enforced by (and for the benefit of) local criminal gangs. These gangs were seen, surprisingly, as alternative coordinating mechanisms to settle claims between neighbors. In the case of the former, Storr and Grube (2013) in a series of papers has argued how "shared histories and perspectives, and the stability of social networks within the community" allows communities who suffered disasters to cope and endogenously resolve immediate and complex problems. Providing an example of these social networks in action, research has found that remittances flow quickly into areas affected by natural disasters when there is a technology in place for it to happen.

Taking these lines of inquiry together, the findings suggest that informal handling of risk takes place on a large scale and that social networks informally connect communities in ways that impact their economies. Recent trends suggest that the interaction of formal and informal economic processes is growing on a nationwide scale. First, the aggregate value of remittance flows is large and growing, nearing the value of foreign direct investment. Second, advancements that formalize previously informal transactions are expanding dramatically in emerging economies through various forms of branchless banking. National economies may be embedded in profoundly influential informal systems that have never been holistically studied.

On a more general note, this project touches lingering questions in economics. For example, some might argue that more stringent labor regulations should have increased informal employment, but in fact the opposite happened. And while the economic rationale for the former statement remains valid, we suggest that unions, as other types of social organizations, as examples of ways whereas people interact via organized networks, provide richer dimensions than those suggested by their interaction as mere economic agents. Hence, unions, as well as other types of faith-based, ethnic, community and other interest groups may provide ways of interaction that escape narrow economic outcomes.

====References====

• De Soto, Hernando (2000/2003) The Mystery of Capital. Basic Books.
• Losby, Jan; Else, John; Kingslow, Marcia; Edgcomb, Elaine; Malm, Erika and Vivian Kao (2002) The Informal Economy: A Literature Review. ISED Consulting and Research and the Aspen Institute Working Paper. http://www.kingslow-assoc.com/images/Informal_Economy_Lit_Review.pdf
• Townsend, Robert M. "Risk and Insurance in Village India." (1994)
• Fafchamps, Marcel, and Susan Lund. "Risk-sharing Networks in Rural Philippines." (2003)
• Weerdt, Joachim De, and Stefan Dercon. "Risk-sharing Networks and Insurance against Illness." (2006)
• Matthew O. Jackson, Tomas Rodriguez-Barraquer and Xu Tan. “Social Capital and Social Quilts: Network Patterns of Favor Exchange.” (2012)
• Yang D and H Choi."Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines"(2007)
• Kurosaki, Takashi. "Consumption vulnerability to risk in rural Pakistan." (2006)
• Jack, W, and T. Suri. "Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution” (2014)
• Engels, Friedrich (1844/1892) The Condition of the Working Class in England. http://www.gutenberg.org/files/17306/17306-h/17306-h.htm
• Venkatesh, Sudhir (2008) Gang Leader for a Day: A Rogue Sociologist Takes to the Streets. Penguin Books.
• Storr, Virgil and Laura Grube (2013) The Capacity for Self-Governance and Post-Disaster Resiliency. George Mason University, Department of Economics Working Paper 13-37. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2350830##
• Blumenstock, Joshua Evan and Fafchamps, Marcel and Eagle, Nathan. “Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda” (2011).
• Ratha, Dilip. "Workers’ remittances: an important and stable source of external development finance." (2005).
• Pénicaud, Claire, and Arunjay Katakam. State of the Industry 2013: Mobile Financial Services for the Unbanked. Rep. N.p.: GSMA MMU, 2013.

If interested, please list your name below.

'''Interested:'''
<li> Eloy Fisher (eloy.fisher@fulbrightmail.org) </li>
<li> Carolina Mattsson (carromattsson@gmail.com) </li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>

<li> Jakub Rojcek (jakub.rojcek@gmail.com) </li>

==Exploring Community Formation through Analysis of Scholarly Corpus (ArXiv)==

The Arxiv [http://arxiv.org/] is a free online repository of scientific preprint articles, mostly from physics and mathematics. It currently contains over 800,000 articles, dating back to 1991. Currently, a lot of the data associated with this repository is completely available: paper submission dates; full texts of papers; author names and coauthorship information. Additionally, there is some citations data available. (If necessary, I can also find papers' subdiscipline labels; submitting authors' email address domain names; other things.)

In the past I have been using these data to try and explore how communities of authors who have shared interests grow over time. For example, we can pinpoint the first ever paper about topological insulators, and search for all subsequent papers on that topic. As more papers are written, authors begin to join the field of research and to form strong ties by collaborating with one another. We can use the ArXiv data to visualize and analyze exactly how these communities form and grow.

===Brainstorming notes===
Isolation between topics, crossing interdisciplinary boundaries, finding (topological) separation distance between disciplines or research groups

Tipping points - can we look for separation of or mergers of two groups or disciplines?

Can we identify key papers (or groups of papers) that initiate ties between fields.

Sentiment analysis: can we identify when a paper's citation is an endorsement or a refutation?

Tools for Analysis: Change point detection; relational event models

Can we find more comprehensive/better citation data?

Lucene and indexing tools:
- Can we re-index our text database after removing stop words?
- Can we index the titles and abstracts?
- Elastic Search - tool for interacting with Lucene

This set of text-processing [http://alias-i.com/lingpipe/demos/tutorial/read-me.html tutorials] is pretty handy for background info and inspiration. The software package doesn't simply plug-into a Lucene/Solr/Elastic Search index, but it could be done. This package from [http://nlp.stanford.edu/software/ Standford] seems very capable.

===Next meeting===
Thursday @ 9AM in Coffee Shop

== PolComplex - Epistemic communities and policy dynamics in the UK Parliament ==

Over the last twenty years, there has been extensive debate about what the core topics in public policy are, and if they change according to type and number. Furthermore, it is still not clear how policy topics mutate and diffuse over time. Human-based text analysis of governmental documents has shed only some light on these research questions: 21 general topics have been proposed, while other accounts tend to restrict it to about five main clusters. Yet, this approach has not been able to devise a reliable and systemic method to capture topic dynamics, diffusion, and their relation to the human actors that talk about them.

This project is an extension of an exploratory research that intends to overcome such limitations, through the application of natural language processing (more specifically, dynamic topic modeling and sentiment analysis), topological and network analysis on a dataset containing UK House of Commons debates ranging from 1975 to 2014. The aim is manyfold. We hope to identify the structure and dynamics of epistemic policy communities - i.e. of political actors revolving around similar policy interest -, and how these relate to factors such as party membership, seniority, relevant historical events.

Some references:

[http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.734168 Taddy (2013), "Multinomial Inverse Regression for Text Analysis"] 

[https://www.cs.princeton.edu/~blei/papers/BleiLafferty2007.pdf Blei and Lafferty (2007), "A Correlated Topic Model of Science"] 

== Genomic variations from a chaotic mapping ==

Following Dabby CHAOS 6 (2), 1996 Musical variations from a chaotic mapping, this project will explore genomic variations in key metabolic genes that are known to be widely spread across bacterial phylogeny (e.g. the Nif cluster which is used in nitrogen fixation). There are several ways that genomic data can be treated as "music":

1) Nucleotide: 4 notes - A, G, C, T 
2) Amino Acids: 20 notes 
3) codon triplets: 64 notes 
4) tetra nucleotides: 256 notes. 

One possible goal is to take a seed sequence from some random organism and generate new variations. Then using homology search (e.g. BLAST) or phylogenetics, determine if that variation exists in nature? If not, perhaps model the potential protein folding (if possible?) to determine if that variation could exist in nature. This is VERY preliminary and can go in many directions.

If interested please list name below or contact Jarrod at jscott@bigelow.org

Reading
http://digitalcommons.olin.edu/cgi/viewcontent.cgi?article=1000&context=ahse_pub&sei-redir=1&referer=https%3A%2F%2Fscholar.google.com%2Fscholar%3Fhl%3Den%26q%3Ddabby%2Bchaos%26btnG%3D%26as_sdt%3D1%252C32%26as_sdtp%3D#search=%22dabby%20chaos%22

== Free Energy Theory ==
The Free Energy (FE) minimisation framework tries to explain how biological systems (such as a cell or a brain) self-organise in order to occupy the (often very limited number of) non-equilibrium states that minimise free energy. This is also known as active inference. A simple corollary of active inference is that agents behave as to minimise their prediction error, or the difference between prediction and reality. Thermodynamic free energy is a measure of energy available in a system to do useful work. This can be framed in an information theoretic setting, as the difference between how the world is being represented and how it actually is. A better fit means a lower information-theoretic free energy, as more resources are being put to ‘good use’ in representing the world. The overarching logic of FE theory is that a better model of the world help maintain structure and organisation, which ultimately helps the system resist increases in entropy.
 
 
This is not a set-in-stone project with any concrete aim (yet). We are a few people interested in exploring the theoretical and practical implications of these ideas, and you're more than welcome to join in!
 
 
'''Interested:'''
 
<ul>
<li> Jelle Bruineberg (j.bruineberg@gmail.com ) </li>
<li> Tobias Morville (tobiasmorville@gmail.com) </li>
<li> Susanne Pettersson (guspsusa85@student.edu.se) </li>
<li> Maggie Simon (margaret.w.simon@gmail.com) </li>
<li> Martina Steffen (martinasemail@gmail.com) </li>
<li> Sahil (sahilgar@usc.edu) </li>
<li> Tirtha (Tirtha.Bandy@csiro.au) </li>
<li> Anna (annza944@gmail.com) </li>
<li> Matt O (mmosmond@gmail.com) </li>
<li> Will Chang (williamkurtischang at gmail dot com) </li>
<li> Cobain (mrdc1g10@soton.ac.uk) </li>
</ul>
Reading:
 
http://rsif.royalsocietypublishing.org/content/10/86/20130475 
http://arxiv.org/abs/1503.04187 
http://www.mdpi.com/1099-4300/15/1/311 

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
'Carolina'
'Alice'
'Federico'
'Jean Gab'
'Sola'
'Ilaria'
'Daniel T'

==Comparison of Network vs Scaling Theory based Models in Ecology (Cobain - mrdc1g10@soton.ac.uk)==

One of the biggest difficulties ecologists face is trying to understand the ecosystem dynamics based on very little biological information (compared to the size of the system) - observational data is logistically difficult and can be very expensive to acquire, as well as often being very time consuming. In terms of modelling, to overcome this problem, two approaches have been utilised. 

The first is a network based approach whereby the nodes represent biomass density of a particular species (or a higher level taxanomic group and/or resources) and edges as the trophic interactions (who eats who). This method depends on what we know about the trophic behaviour of the species involved (which is often very limited and there can be many species to parameterise), and represents only the central tendency of what could potentially be very diverse behaviour within and between populations. 

The second approach is to use scaling theory to describe average trophic interactions and other biological processes based on individual organism body size along the size continuum, viewing the community as a very size structured dynamical system. This requires less specific knowledge about the organisms in the community ''per se'', however this again represents only the central tendency of a given size class of what could be very diverse behaviour. Functional differences can be potentially introduced (coupled benthic-pelagic systems for example), but to the detriment of braking down the predictability of the well known allometric scaling laws with size as specificity increases. 

There has been little to no comparison of these two methods of modelling ecological systems (at least to my knowledge) and the question arises that, given the same starting information, how well do both approaches model the dynamics of an ecosystem, given the limited biological information we have? Is one approach better at capturing energy flow through the system / community structure and stability etc. compared to the other? If the models vary then such information will better inform ecologists on which to use depending on what type of questions they are trying to answer. 

Therefore, the project proposed here aims to investigate these two methods. The community that is used to seed an experimental mesocosm setup (mesocosms are large tank set-ups designed to reflect the complexity of natural ecosystems but still being able to be artificially controlled and are still relatively simple), will be input into two models based on the separate approaches and then run to steady state. The model outputs will then be compared and contrasted with eachother as well as the mesocosm community at steady state. Further work may include examining perturbation dynamics, dependent on what data we have available.

'''Interested: Name / Email''' 
Martina Steffen (martinasemail@gmail.com)

==Dynamics of homicide (Matthew Ingram - mingram@albany.edu)==

Integrating temporal, spatial, and multi-level concepts

1:30pm in the coffee shop

Am interested in the discussion on this project...Sola Omoju

Interested - Nilton Cardoso

==Multiplex Adaptive Networks (Daniel - dtc65@cornell.edu)==

7 pm in the coffee shop

==Modeling brain diseases (or cancerous bio pathways) (Sahil - sahilgar@usc.edu)==
Interested people can put a meeting time as per their convenience here.
 
No meeting time indicated
 
One potential meeting time can be 3pm in coffee shop ?
 
Information theoretic algorithms can also be explored for the problem.
 
Discovering Structure in High-Dimensional Data Through Correlation Explanation. http://papers.nips.cc/paper/5580-positive-curvature-and-hamiltonian-monte-carlo
 
Maximally Informative Hierarchical Representations
of High-Dimensional Data. http://jmlr.org/proceedings/papers/v38/versteeg15.pdf

 
'''Interested:'''
 
<ul>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> Laura Condon (lcondon@mymail.mines.edu)</li>
</ul>
 
 

==Analysis of UK parliament speeches 1935-2014 (Stefano - etstefano@gmail.com)==

No meeting time indicated

==Mapping Complexity/Human Knowledge as a Complex Adaptive System (radjohn@live.com)==
2pm/Wed 6/10 in Conference Room (Tentative)

==Navigating Music, Brain and The Edge of Chaos (radjohn@live.com)==
10:45am/Wed 6/10 in Conference Room
9:00am/Thu 7/10 at the Coffee Shop

'''Interested:'''
Sahil Garg

==Analysis of rule-based modeling for dopamine-dependent synaptic plasticity (emilia.m.wysocka@gmail.com)==

----------------------------------------------------------------------------------
Modeling and analysis of phenomenons and evolution of stochastic, combinatorially complex signalling systems in a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system).

----------------------------------------------------------------------------------
Rule-based modeling features:

*biological systems as concurrent processes
*dynamics of post-translational modifications
*domain availability
*competitive binding
*causality and intrinsic structure
*binding sites
*interaction rules replace reaction equations
*infinite number of reactions with a small and finite number of rules
*reduction of parameter space
*“don't care don't write” - adjustable rule contextualization
*single reaction rule and parameters generalize classes of multiple rules
*modular and extensible language
*specification language & simulation/integration environment
*static and causal analysis
*Kappa/KaSim & BioNetGen/NFsim -- specification language/network-free simulator

----------------------------------------------------------------------------------
----------------------------------------------------------------------------------
Meetings:
----------------------------------------------------------------------------------
* WEDNESDAY 11/06/2015

Addressing problems in terms of the other aspects of the projects apart from the biological questions and verification (matching results to some published experiments or known behaviour). So my plan is as follows:

* divide the group (roughly) into people who look in to biological meaning and validation and the one which tries to do the analysis of system phenomenons and evolution of stochastic, combinatorially complex signalling systems in both a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system) - all possible states, scenarios of the system abstracted from the biological meaning.

Things that could be modelled (look in dropbox folder: https://www.dropbox.com/sh/g080w60pt2vgi7v/AACHlMf2JwpP2EZqKq7Di6N2a/possible%20models?dl=0):

* to make things easier and have the modeling part done quickly- base the model on the dopamine-related synaptic plasticity (SBML ODE model): http://www.ebi.ac.uk/biomodels-main/MODEL1101170000;

* I found a series of papers suitable to the subject of the Complex Systems course, e.g.: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC344803/. I was thinking to even try to compare higher level brain data (networks, spiking ..) with purely molecular model

* noise and low copy number (http://www.princeton.edu/~wbialek/our_papers/bialek+setayeshgar_08.pdf); this is quite interesting as for sufficiency of binding events -> http://www.princeton.edu/~wbialek/our_papers/bialek+ranganathan_07.pdf

* spontaneous flipping of interactions (phosphorylations and others) between proteins, described by the god of non-linear dynamics (S. Strogatz)

----------------------------------------------------------------------------------
* THURSDAY 11/06/2015 : http://doodle.com/se23ibwtkrkccs4s

----------------------------------------------------------------------------------
Some refs:
*https://www.dropbox.com/sh/g080w60pt2vgi7v/AADUy7Ge-J-oSYkyTeYOo7V8a?dl=0)
*http://www.pps.univ-paris-diderot.fr/~jkrivine/homepage/Teaching.html

==Ecology non-working group (williamkurtischang at gee-mail dot com)==
[[Informal ecology interest group]].

==Making Supply Chains Resilient to Disasters (mh2905 att columbia dott edu)==
'''Key words''': resilience, natural hazards, supply chains, interdependency, interconnected risks, cascading failures

'''Rational''': I am interested in examining what kinds of network structures and features contribute to increasing resilience of supply chains to natural disasters. I believe this area of work is important because regional disasters negatively impact the global economy through disruptions in supply chain networks. The pioneer study published in Nature urges the need for making supply chains climate-smart (Levermann 2014). Also in the industry, the World Economic Forum published a report to address this issue in 2013. Few researches, however, assess and model the impacts of adverse weather on supply chains. I would like to evaluate the impacts based on modeling.

'''Data set''': Supply chains data of a multinational manufacturing company.Global climate data, disaster data, etc.

'''Possible techniques''': Agent-Based Model, Complexity science, network theory and evolution, complex adaptive systems, GIS, operations research, manufacturing engineering, and I am open to any techniques.

'''Myself''': As I joined the program yesterday, please find my background here.
http://tuvalu.santafe.edu/events/workshops/index.php/Masahiko_Haraguchi

'''Please put your names below if you want to be informed about this project by email'''

'''Interested''':
Masa Haraguchi

==From Ethnic Diversity to Religious Zeal: Retrospective/Predictive Construction of the World Ethnic Map (vdzutsati@asu.edu)==

There is a new dataset on ethnic groups (2014), which claims to include all ethnic groups of the world http://www.cidcm.umd.edu/mar/amar_project.asp. There are also several cross national datasets on religion and other variables of interest, normally, socio-economic http://www.thearda.com/Archive/CrossNational.asp. Coming from social sciences I thought it would be really interesting to apply methods and perspectives from other disciplines to social science data.

One idea that I have in mind is to explore how ethnicity and religion interact.

Quick empirical checks indicate that Subsaharan Africa is home to about 1,500 ethnic and subethnic groups, in comparison to about 90 ethnic groups in the Middle East and North Africa. India alone accounts for nearly 2,000 ethnic and subethnic groups, while Europe, including all the countries of the former USSR and large immigrant groups from other parts of the world, has only about 260 ethnic groups. The picture that emerges from this simple comparison is that the spread of Abrahamic religions appears to be associated with the high depletion rate of ethnic groups. The exception of China, which has little more than 50 ethnic groups, can be explained by the country’s long history of a centralized state.

The idea then is to assume that we have had the control group of nations that did not experience the influx of Abrahamic religions (or more precisely received limited exposure to them) and the experimental group that was exposed to the spread of Abrahamic religions. Since we know what the distribution of ethnic groups in the control group is we can project what the distribution of ethnic groups in the experimental group would be, if the group were not exposed to Abrahamic religions.

Of course, we could go in the other direction too and make a projection about future – what would happen to ethnic groups if all of them adopted an Abrahamic religion.

One of the challenges in this project is absence of a dynamical system, I.e. We don’t have data on how these things changed over time, but I still think that projection about the future and the past would be kinda cool:)

'''Interested''':

==Nationalist vs religious rebel violence (vdzutsati@asu.edu)==

I would like to propose yet another possible project. This time it’s on rebel violence. I have a large dataset on rebel (and government) violence (~1 million observations). The paper that used this data was published couple of months ago (Islamists and Nationalists: Rebel Motivation and Counterinsurgency in Russia’s North Caucasus). The dataset contains event counts, GIS data, demographic and economic characteristics and some other stuff. The primary hunch of the paper was to discover the differences between nationalist and Islamist rebels.

Given all the cool methods we are learning here, my first instinct is to use the methods and tools (TISEAN?) to explore and discover whether nationalist violence is substantively different from religious violence and try to answer why.

One problem that I can see is that the event coding was done by a machine, so this may have “contaminated” the data. But if both religious and nationalist data were contaminated equally then the difference should still be evident.

'''Interested''':

==[[Archived Projects ]]==

Complex Systems Summer School 2015-Projects & Working Groups

2015-07-02T21:06:38Z

Rbarnes: /* Richard's Current Table */

{{Complex Systems Summer School 2015}}

==[[Reservations for Evans Science rm. 215]]==

Click on the title link to reserve Evans Science rm. 215 for group usage.

==Californian Drought Model==
Problem definition:
Water is an ongoing problem in California. Although corrective measures are taken for some years, periods of droughts are depleting water ground levels to not sustainable levels.
Aims:
A Netlogo simulation on how likely Californian agents evolve with regards to drought. The ABM could be used to explore the potential impacts of forms of regulation.
Outcomes:
Create an exploratory agent based model based on real data, enabling the possibility of simulating different methods of regulation, including feedbacks between the economic and hydrologic systems.

Extension 1: We will conduct a network analysis of Twitter data (hashtags #drought #California) to explore the social dynamics and information flows between stakeholders.

Extension 2: Phase-space reconstruction of groundwater level time series data, comparing the dynamics in different parts of the Central Valley.

==City Resilience==
'''Summary:''' This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities. 
'''Contact:''' Richard Barnes (rbarnes@umn.edu) 
'''Participants:''' Alex Tejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang, Martina Steffen, Juan Carlos Castilla, Brent Schneeman 
'''Wiki page:''' [[Resilience of Cities]]

==Complex Adaptive Systems and the Narrative approach: transdisciplinary methodologies for Complexity Science==
The narrative approach and it causality is different from causality in logico-scientific approaches. Making bridges among methodologies transfers knowledge, encourages important questions and switches philosophical paradigms. Formal frameworks of C(A)S can realy be complemented with the narrative, and actually should. The narrative approaches in Complexity Science are an emerging trend for fund-granting, and is really up to date to process documentary and speech-based data, reveal hidden meanings, distinguish causes from effects in overlapping systems of realms - "at the edge" of technology, social, economic, scientific, legal and other domains. The role of time in coding, intuitively generated conditional rules and computational paths. Combinations of C(A)S and the narrative is combination of quantitative and qualitative data and thinking in original - out of convertation and information loss. Synthesis of sciences is what it is about.

'''Questions''':
How to combine CS approaches (in particular CAS) and the narrative ones in a rule-based way? What to start from? What vizualizations there can be designed and used? Other questions at your descrete.

'''Some references''':
- Non-Equilibrium Social Science in ICT and Economics, CORDIS, EU: http://cordis.europa.eu/project/rcn/102344_en.html, http://www.nessnet.eu/

- Combining Complexity Theory with Narrative Research: https://youtu.be/pHjeFFGug1Y

- Haridimos Tsoukas and Mary Jo Hatch (2001), "Complex thinking, complex practice: The case for a narrative approach to organizational complexity: http://www.brown.uk.com/teaching/qualitativepostgrad/tsoukas.pdf

- David Christian, "Big History", Astrophysics, Chemistry, Biology, Information, emergence of life, technology: https://youtu.be/GlmvFjFceD8

'''Contact''': Anna (annza944@gmail.com)

Meet on Monday over lunch

'''Interested''': Marie Pierre, Jeroen, Jim, Melissa

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
 
'Carolina'
 
'Alice'
 
'Federico'
 
'Jean Gab'
 
'Sola'
 
'Ilaria'
 
'Daniel T'
 
'Sahil Garg

==Ebola==
The 2014-15 Ebola virus disease (EVD) outbreak in West Africa presented both unique opportunities and unique challenges to the epidemiological modeling community. For the first time during an emerging infectious disease outbreak, high resolution data--from a variety of sources--were made available to the academic community and many public health decision makers genuinely engaged with mathematical and computational modelers. However, the popular and scientific press were highly critical of most models ability to project the outbreak's course. The following key and open questions seem ripe for investigation using a complex adaptive systems lens:
 
1) What features of EVD transmission are most problematic for reliable, robust forecasting: changing behavior, intervention, viral evolution, complex social networks, etc?
 
2) How/can we use digital data to either improve forecasts or inform model selection?
 
3) Can one quantify the value of additional information in real-time?
 
'''Contact:''' Samuel Scarpino, SFI Omidyar Fellow, Santa Fe Institute - scarpino@santafe.edu
 
 
'''Interested:'''
 
Marie-Pierre Hasne 
Chris Verzijl 
Junming Huang 
'Sola Omoju 
Christine Harvey 
Daniel Citron (dtc65@cornell.edu) 
William Chang (williamkurtischang at gee-mail)

==Effect of landscape topography on vegetation connectivity and navigability==
''Context''
 
Landscape topography influences the dynamics of the processes that take place on it. Evolution of ecosystems networks, river networks, vegetation cover type, microclimate cycles are all interlinked deeply to the local landscape topography.

In addition to the landscape these networks are also influenced by each other conditioning the emergence and stability of ecosystems, and subsequently the behaviour of agents in the ecosystem like migration pathways of animal herds, human settlement patterns etc.

Connectivity patterns emerge from the interaction of these processes, and thus a better understanding and quantification of those patterns is critical to understanding the dynamics of the system.

 
''How do we want to approach the problem''
 
In this project, we will randomly generate landscape topography and subsequent vegetation cover from a set of parameters from known geological and biological processes. The generated data set will then be used to investigate the following questions:
 
(1) What is the connectivity of landscape patterns that emerge at different scales using different techniques such as clustering analysis, percolation theory or network theory, and can we quantify them ?
 
(2) What is the navigability of biological agents (e.g animals, humans, robots!) under such landscape patterns. We can compare mobility trails in existing landscapes to validate our hypothesis. We propose to use the tools like ABMs to simulate and characterise mobility success.
 
(3) We further aim to compare the measures of navigability with the metrics of connectivity, establishing a framework of comparison.
 

''Contact:''
 tirtha.bandy@csiro.au
 alej.tejedor@gmail.com

''Meetings''
(1) Wednesday 1pm Coffee shop

People Interested 
Martina

==[[Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe]]==
Click on the link to go to the project page for meetings, project details, and progress.
 
contact: mveitzel@ucsc.edu
 

==Mapping Complexity/Human Knowledge as a Complex Adaptive System‏==
''Context''
 
Ants leave pheromone trail patterns which they are aware of only in a local sense. They do not have the cognitive faculties to step back and look at the trails and grasp the ant-trail network as a totality. Also, the artifacts they leave behind are physical entities which then provides the aggregate feedback to the aggregate ant body to then feed the evolution of the ant body as a CAS system. In contrast, humans do have the requisite cognitive abilities. The "pheromone trails" we leave behind are the knowledge trails coded in symbolic knowledge artifacts. In contrast to the physical artifacts that ants leave behind, the knowledge artifacts that we leave behind are far more flexible and potent, both at the aggregate as well as at the individual levels. But like the ants, until recently, we did not have the means to step back and map the knowledge "pheromone trails" to obtain the big picture and its global/local dynamics. The burgeoning field of scientometrics is making available visualization tools to help us map and study the evolutionary dynamics of the knowledge network structures.
 
''Data and Questions''
 
The goals of this project include
<ol>
<li>Extract the terms from approximately 1600 working papers published by SFI</li>
<li>Map the intra/inter conceptual network structures</li>
<li>Study the evolution of these structures across time</li>
<li>High-light the gap-closure of knowledge reverse-salients (if any)</li>
<li>Capture any of the network patterns that repeat</li>
<li>Study the diffusion of concepts across the network</li>
<li>Provide visualization tools for navigating the complexity corpus, etc</li>
</ol>
 
''Possible methods''
 
Latent Semantic Analysis (LSA) and Latent Document Analysis (LDA)
 
References:
<ol>
<li> SFI Working Papers: http://www.santafe.edu/research/working-papers/</li>
<li> Atlas-Science-Visualizing: http://www.amazon.com/Atlas-Science-Visualizing-What-Know/dp/0262014459</li>
<li> Atlas-Science-Visualizing WebSite: http://scimaps.org/atlas</li>
<li> Mapping-Scientific-Frontiers-Knowledge-Visualization: http://www.amazon.com/Mapping-Scientific-Frontiers-Knowledge-Visualization/dp/1447151275</li>
<li> Katy Börner presents at Science of Science: https://www.youtube.com/watch?v=pzCqGBNzomE</li>
<li> Scholarly Data, Network Science, and (Google) Maps:https://www.youtube.com/watch?v=vos5QBDywMM</li>
<li> LSA Video Lect: http://videolectures.net/slsfs05_hofmann_lsvm/</li>
<li> What is LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf</li>
<li> LSA Wiki:http://en.wikipedia.org/wiki/Latent_semantic_analysis</li>
<li> LDA: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm</li>
<li> Fusari, A. (2014). Methodological Misconceptions in the Social Sciences: Rethinking Social Thought and Social Processes: http://www.springer.com/us/book/9789401786744 </li>
<li> Kirsh, D. (2013). Thinking with external representations. Cognition Beyond the Brain: http://adrenaline.ucsd.edu/kirsh/Articles/Interaction/thinkingexternalrepresentations.pdf </li>
<li> Holland, J. H. (1992). Adaptation in natural and artificial systems: https://mitpress.mit.edu/index.php?q=books/adaptation-natural-and-artificial-systems </li>
<li> Where to start with text mining. http://tedunderwood.com/2012/08/14/where-to-start-with-text-mining/ </li>
<li> Singular Value Decomposition Tutorial https://www.ling.ohio-state.edu/~kbaker/pubs/Singular_Value_Decomposition_Tutorial.pdf </li>
<li> Latent Semantic Analysis (LSA) Tutorial: http://www.puffinwarellc.com/index.php/news-and-articles/articles/33-latent-semantic-analysis-tutorial.html </li>
<li> LSA in Detail: http://www2.denizyuret.com/ref/berry/berry95using.pdf </li>
<li> Web Based LSA: http://webapp1.dlib.indiana.edu/newton/lsa/index.php </li>
<li> Another Technique: t-Distributed Stochastic Neighbor Embedding (t-SNE): http://lvdmaaten.github.io/tsne/ </li>
<li> tf–idf: https://en.wikipedia.org/wiki/Tf%E2%80%93idf </li>

</ol>
 
'''Interested:'''
 
<ul>
<li> John Thomas (radjohn@live.com) </li>
<li> Haitao Shang (hts@mit.edu) </li>
<li> Christopher Verzijl (cjo.verzijl@gmail.com) </li>
<li> Anna Zaytseva (annza944@gmail.com) </li>
<li> Penny Mealy (penny.mealy@gmail.com) </li>
</ul>
 
 

==Navigating Music, Brain and The Edge of Chaos ‏==
''Context'' 
[[Navigating Music, Brain and The Edge of Chaos]]
 
Brain Sciences have revealed that we/it lives "on the edge of chaos" exhibiting "self-organized criticality" that is tentatively balanced between normalcy and madness. Over the course of history, humans have used various agents and activities to shape, influence and control this living-chaos ranging from substances such as caffeine, sugar, drugs etc., to activities such as the arts (including music), social-discourse/therapy, meditation etc. Of these, music has a distinct role in shaping our moods and helping us transition between different mental states, as well as maintain it for extended periods of time. Clearly we have been using music to help us control and shape the internal chaos. But until recently, the quantitative instrumentation of this massively complex system that comprises of close to a 100-billion neurons networked into a 1000-trillion synaptic edifice has not been available to the common man. But of late, affordable, wearable EEG's are available on the market, thus making the quantitative study of the influence of music in brain dynamics feasible on a large-scale/crowd-sourcing sense. To help come to terms with the complexity of our 1000-trillion synaptic edifice, we need to gather data on a vast scale. The proposed research is a proof-of-concept, exploratory foray into making this happen.
 

History of music in 5 min
https://www.youtube.com/watch?v=Gt2zubHcER4

'''Interested:'''
 
<ul>
<li> Sara Lumbreras (sara.lumbreras@gmail.com) </li>
<li> Ilaria b (bertazzi.ilaria@alice.it) </li>
<li> Braun, Urs (urs.braun@zi-mannheim.de) </li>
<li> John Thomas (radjohn@live.com) </li>
<li> Glenn Magerman (glenn.magerman@kuleuven.be) </li>
<li> Daniel Friedman (DanielAriFriedman@gmail.com)</li>
</ul>
 
 

==Powerlaw fitting and alternative distributions - Theory/statistics ==
Clauset, Shalizi and Newman (2009) propose a maximum likelihood method to estimate the powerlaw exponent of a variable of interest. This is a great improvement on earlier methods such as OLS that dominated the literature up to then. However, one can fit a powerlaw to any dataset and the most we can say is that our observations are consistent with the hypothesis that x is drawn from a powerlaw distribution. One easily implementable method to compare the powerlaw fit to other fits is then a likelihood ratio test for both models.

One particular discussion is the distinction between a powerlaw (PL) and a log-normal (LN) fit. For an avid discussion between both fits on city size and Zipf's law, see Eeckhout (2004, 2009) and Levy (2009), where the discussion now settled on city sizes following a log-normal distribution instead of a powerlaw. Similarly for the discussion on firm size distribution: Simon and Bonini, 1958; Ijri and Simon, 1977; Stanley et al., 1995; Sutton, 1997; Axtell, 2001; Okuyama et al., 1999; Cabral and Mata, 2003; Gaffeo et al., 2003; Aoyama et al., 2004; Fujiwara et al., 2004a,b; Kaizoji et al., 2006; Takayasu et al., 2008; Duchin and Levy, 2008; Schwarzkopf and Farmer, 2008, ...

This distinction matters for several reasons:
- PL and LN come from very similar model and differences in initial conditions can lead to very different outcomes. PL exhibits a choice for x_min, below which the unit of observation is not feasible to exist (eg minimum city size, firm size, word length, ...). LN has no minimum size.
- PL and LN that look similar are the difference between infinite (PL) and large but finite variance (LN)
- shock propagation: when unit-level shocks are large enough to show aggregate perturbances if the distribution is powerlaw with infinite variance, while these shocks wash out fast when the distribution is log-normal. (Gabaix, 1999; Gabaix 2009; Acemoglu et al. 2012, ...).

I have encountered some issues which I would like to explore further:
1. The distinction between lognormal and powerlaw in the data is very sensitive to data truncation: in the above discussions, researchers have slightly different datasets, covering more or less of the population at hand. Left-truncation (i.e. observations not in the dataset because they are too small to be reported) can strongly drive the outcome of the fit, even when endogenizing the x_min cutoff. I have data on the universe of Belgian firms, much more complete than e.g. US Census data, where I have done some preliminary tests on this. The question is then: how to formalise this distinction and what are the theoretical and practical caveats to look out for when applying this method.
2. MLE fitting seems to be sensitive to the choice of units as well: rescaling a variable by a factor 1000, 1000000 etc seems to influence the endogenous x_min choice and hence the estimated parameter. This reminds me of some work on scaling invariance in negative binomial estimators. What is going on here?
3. Can we set up a model that generalises both? I've been looking at Levy stable distributions, but did not do anything with it yet.

Interested people:
* Corbain
* Laura
*Binyang
*Sahil
*Tirtha
*Glenn
*Junming

Literature:
Powerlaw fitting in empirical data - Clauset, Shalizi and Newman (2009): http://arxiv.org/abs/0706.1062

==Organ Transplant Analysis==
Over 120,000 people in the United States are currently on the waiting list for an organ transplant. The size of this waiting list relates to over 6,000 deaths a year while waiting for a transplant and tens of billions of dollars in government spending. I have access to the following data sets:
 
* All transplants performed in the US from October 1987 to June 2014 (including follow-up data)
* All living and deceased organ donors in the same time period (with follow-up data on living donors)
* Waiting list data for everyone who signed up for the list
* 2012 National Survey on Attitudes and Behaviors on Organ Donation
* Social media data relating to organ donation since 2008

Open to ideas and suggestions for the topic, there are a lot of interesting questions to investigate including cultural/racial/gender differences in organ donation. I have several preliminary reports and exploratory analysis done on differences in donors.

Second Meeting Thursday, June 11th 4:15 in the coffee shop!

===Matching Game===
Rules for the matching game:

Objective: Create the largest chain possible using blood types in the room.

Follow the blood type guidelines for donation to form a donor chain.
*Assume that self-matches are not possible, for example someone with a donor of type AB and a recipient of type AB can not match themselves and needs to find someone else to complete their chain.
*Create a donor chain to help as many people as possible.
*Post results below for the prize
*There will be several "good samaritan" donors which will donate and need nothing in return, this is a great way to start your chain. Using a good samaritan chain also means you don't need a donor for the end point.

====Game Workspace====
Use this workspace to post for and find matches!

==== Donor Chains ====
Post your chain here. Example is given below:

=====Example Chain 1=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| A
|-
| Laura
| AB
| B
|-
| Matt
| A
| AB
|}

=====Example Chain 2=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| None
|-
| Laura
| A
| B
|-
| Matt
| None
| A
|}

======Richard's Current Table======

4638 Iterations

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Matthew
| O
| X
|-
| Kleber
| O
| A
|-
| Alice
| AB
| O
|-
| Alejandro
| O
| AB
|-
| James
| AB
| O
|-
| Brent
| O
| AB
|-
| DanielF
| AB
| O
|-
| Susanne
| A
| AB
|-
| Chris
| O
| A
|-
| Cobain
| B
| A
|-
| Nilton
| O
| B
|-
| Richard
| A
| A
|-
| Michael
| O
| A
|-
| Martina
| A
| O
|-
| AndySchauf
| A
| A
|-
| Maria
| O
| A
|-
| Jakub
| O
| A
|-
| Masa
| A
| O
|-
| WillChang
| O
| A
|-
| Sam
| O
| B
|-
| MatthIn
| AB
| O
|-
| Vanessa
| A
| AB
|-
| Glenn
| O
| A
|-
| Urs
| B
| A
|-
| Charon
| B
| B
|-
| Melissa
| A
| B
|-
| Jae
| B
| A
|-
| Laurence
| O
| B
|-
| Jeroen
| O
| O
|-
| MariePierre
| A
| O
|-
| Carolina
| O
| A
|-
| Havier
| AB
| O
|-
|}

Unused:

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| X
|-
| Jelle
| AB
| X
|-
| JGab
| O
| X
|-
| Keith
| AB
| O
|-
| LauraCondon
| O
| X
|-
| Maggie
| O
| X
|-
| MatthewO
| AB
| O
|-
|}

==Multi-dimensional social networks in the evolution, development and resilience of informal economies. ==
Informal economies are defined as economic activities that occur outside the purview of corporate public and private institutions. These types of economies proliferate where traditional economic actors are unable to productively exercise their activities, especially due to costly constraints (De Soto 2003). Firms may face adverse incentives to expand production by hiring more workers or incorporating more capital due to the low productivity of their workers or the predatory practices of rapacious elites or corrupt governments. Labor itself, i.e. people, may face hurdles in trying to make the jump towards entrepreneurial activities or jobs in the formal economy which allow the accumulation of experience, retirement savings, access to insurance or precautionary savings to face unexpected events (like disease, disability, etc.) due to lack of capital access (whether human and financial).

'''For this project, we propose a different interpretation: We seek to understand and model how multi-faceted social networks provide robust alternatives to formal economies, which far from being seen as degenerate forms of social organization, in many instances co-exist, challenge and compete with formal employment and economic activities. While some types of informal economies operate under adverse contexts, their resiliency may be understood as a type of adaptive fitness and not the mere result of stubborn cultural path-dependency. '''

For the most part, informal economies have been analysed as counterpoint to an ideal type of a formal economy with low levels of trust, respect of property rights, access to credit and public institutions - like the court systems (see Losby et al. 2002 for a review). However, informal economies have been coupled with their formal counterparts since the development of capitalism in the 19th Century. To name a famous historical example, Old London's East End's informal sector was described harshly by Engels (1844) in his famous tract, "The Condition of the Working Class in England". But almost fifty years later, in a new preface to the book, Engels recognized the progress that took place there under the aegis of working class organizations in the area.

Research has begun to catch on to this idea – developing a literature in the areas of informal risk sharing and remittances, which are in some sense informal counterparts to insurance and banking. Consumption patterns in poor, rural villages are remarkably smooth suggesting that risk-sharing measures are prevalent, although imperfect (Townsend). Field studies of networks underlying village risk sharing systems have found that households primarily receive help from existing social connections, such as friends and relatives, in the form of informal loans or transfers. Theoretical work in network economics, by authors such as Matt Jackson, found that certain empirically prevalent network structures may directly benefit the stability of favor exchange systems. Another line of inquiry focuses on remittance income, generally informal transfers across kinship ties. World Bank researchers have found that remittances from overseas migrants respond dramatically to regional income shocks, replacing upwards of 60% of lost income in households with international migrants. Further studies, cited below, have generalized those results. Furthermore, reductions in the cost of sending remittances, which occurs with the advent of mobile money, further mitigates exposure to risk in receiving households.

Also recently, authors have written on how communities come together in situations where formal economies are unavailable by external shocks (like disasters) or due to lack of access (due to conditions of abject poverty). In the case of the latter, Venkatesh (2008) provided gripping testimonies of how informal economies arose and developed in the South Side of Chicago in low trust environments via cash transactions and informal service contracts enforced by (and for the benefit of) local criminal gangs. These gangs were seen, surprisingly, as alternative coordinating mechanisms to settle claims between neighbors. In the case of the former, Storr and Grube (2013) in a series of papers has argued how "shared histories and perspectives, and the stability of social networks within the community" allows communities who suffered disasters to cope and endogenously resolve immediate and complex problems. Providing an example of these social networks in action, research has found that remittances flow quickly into areas affected by natural disasters when there is a technology in place for it to happen.

Taking these lines of inquiry together, the findings suggest that informal handling of risk takes place on a large scale and that social networks informally connect communities in ways that impact their economies. Recent trends suggest that the interaction of formal and informal economic processes is growing on a nationwide scale. First, the aggregate value of remittance flows is large and growing, nearing the value of foreign direct investment. Second, advancements that formalize previously informal transactions are expanding dramatically in emerging economies through various forms of branchless banking. National economies may be embedded in profoundly influential informal systems that have never been holistically studied.

On a more general note, this project touches lingering questions in economics. For example, some might argue that more stringent labor regulations should have increased informal employment, but in fact the opposite happened. And while the economic rationale for the former statement remains valid, we suggest that unions, as other types of social organizations, as examples of ways whereas people interact via organized networks, provide richer dimensions than those suggested by their interaction as mere economic agents. Hence, unions, as well as other types of faith-based, ethnic, community and other interest groups may provide ways of interaction that escape narrow economic outcomes.

====References====

• De Soto, Hernando (2000/2003) The Mystery of Capital. Basic Books.
• Losby, Jan; Else, John; Kingslow, Marcia; Edgcomb, Elaine; Malm, Erika and Vivian Kao (2002) The Informal Economy: A Literature Review. ISED Consulting and Research and the Aspen Institute Working Paper. http://www.kingslow-assoc.com/images/Informal_Economy_Lit_Review.pdf
• Townsend, Robert M. "Risk and Insurance in Village India." (1994)
• Fafchamps, Marcel, and Susan Lund. "Risk-sharing Networks in Rural Philippines." (2003)
• Weerdt, Joachim De, and Stefan Dercon. "Risk-sharing Networks and Insurance against Illness." (2006)
• Matthew O. Jackson, Tomas Rodriguez-Barraquer and Xu Tan. “Social Capital and Social Quilts: Network Patterns of Favor Exchange.” (2012)
• Yang D and H Choi."Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines"(2007)
• Kurosaki, Takashi. "Consumption vulnerability to risk in rural Pakistan." (2006)
• Jack, W, and T. Suri. "Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution” (2014)
• Engels, Friedrich (1844/1892) The Condition of the Working Class in England. http://www.gutenberg.org/files/17306/17306-h/17306-h.htm
• Venkatesh, Sudhir (2008) Gang Leader for a Day: A Rogue Sociologist Takes to the Streets. Penguin Books.
• Storr, Virgil and Laura Grube (2013) The Capacity for Self-Governance and Post-Disaster Resiliency. George Mason University, Department of Economics Working Paper 13-37. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2350830##
• Blumenstock, Joshua Evan and Fafchamps, Marcel and Eagle, Nathan. “Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda” (2011).
• Ratha, Dilip. "Workers’ remittances: an important and stable source of external development finance." (2005).
• Pénicaud, Claire, and Arunjay Katakam. State of the Industry 2013: Mobile Financial Services for the Unbanked. Rep. N.p.: GSMA MMU, 2013.

If interested, please list your name below.

'''Interested:'''
<li> Eloy Fisher (eloy.fisher@fulbrightmail.org) </li>
<li> Carolina Mattsson (carromattsson@gmail.com) </li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>

<li> Jakub Rojcek (jakub.rojcek@gmail.com) </li>

==Exploring Community Formation through Analysis of Scholarly Corpus (ArXiv)==

The Arxiv [http://arxiv.org/] is a free online repository of scientific preprint articles, mostly from physics and mathematics. It currently contains over 800,000 articles, dating back to 1991. Currently, a lot of the data associated with this repository is completely available: paper submission dates; full texts of papers; author names and coauthorship information. Additionally, there is some citations data available. (If necessary, I can also find papers' subdiscipline labels; submitting authors' email address domain names; other things.)

In the past I have been using these data to try and explore how communities of authors who have shared interests grow over time. For example, we can pinpoint the first ever paper about topological insulators, and search for all subsequent papers on that topic. As more papers are written, authors begin to join the field of research and to form strong ties by collaborating with one another. We can use the ArXiv data to visualize and analyze exactly how these communities form and grow.

===Brainstorming notes===
Isolation between topics, crossing interdisciplinary boundaries, finding (topological) separation distance between disciplines or research groups

Tipping points - can we look for separation of or mergers of two groups or disciplines?

Can we identify key papers (or groups of papers) that initiate ties between fields.

Sentiment analysis: can we identify when a paper's citation is an endorsement or a refutation?

Tools for Analysis: Change point detection; relational event models

Can we find more comprehensive/better citation data?

Lucene and indexing tools:
- Can we re-index our text database after removing stop words?
- Can we index the titles and abstracts?
- Elastic Search - tool for interacting with Lucene

This set of text-processing [http://alias-i.com/lingpipe/demos/tutorial/read-me.html tutorials] is pretty handy for background info and inspiration. The software package doesn't simply plug-into a Lucene/Solr/Elastic Search index, but it could be done. This package from [http://nlp.stanford.edu/software/ Standford] seems very capable.

===Next meeting===
Thursday @ 9AM in Coffee Shop

== PolComplex - Epistemic communities and policy dynamics in the UK Parliament ==

Over the last twenty years, there has been extensive debate about what the core topics in public policy are, and if they change according to type and number. Furthermore, it is still not clear how policy topics mutate and diffuse over time. Human-based text analysis of governmental documents has shed only some light on these research questions: 21 general topics have been proposed, while other accounts tend to restrict it to about five main clusters. Yet, this approach has not been able to devise a reliable and systemic method to capture topic dynamics, diffusion, and their relation to the human actors that talk about them.

This project is an extension of an exploratory research that intends to overcome such limitations, through the application of natural language processing (more specifically, dynamic topic modeling and sentiment analysis), topological and network analysis on a dataset containing UK House of Commons debates ranging from 1975 to 2014. The aim is manyfold. We hope to identify the structure and dynamics of epistemic policy communities - i.e. of political actors revolving around similar policy interest -, and how these relate to factors such as party membership, seniority, relevant historical events.

Some references:

[http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.734168 Taddy (2013), "Multinomial Inverse Regression for Text Analysis"] 

[https://www.cs.princeton.edu/~blei/papers/BleiLafferty2007.pdf Blei and Lafferty (2007), "A Correlated Topic Model of Science"] 

== Genomic variations from a chaotic mapping ==

Following Dabby CHAOS 6 (2), 1996 Musical variations from a chaotic mapping, this project will explore genomic variations in key metabolic genes that are known to be widely spread across bacterial phylogeny (e.g. the Nif cluster which is used in nitrogen fixation). There are several ways that genomic data can be treated as "music":

1) Nucleotide: 4 notes - A, G, C, T 
2) Amino Acids: 20 notes 
3) codon triplets: 64 notes 
4) tetra nucleotides: 256 notes. 

One possible goal is to take a seed sequence from some random organism and generate new variations. Then using homology search (e.g. BLAST) or phylogenetics, determine if that variation exists in nature? If not, perhaps model the potential protein folding (if possible?) to determine if that variation could exist in nature. This is VERY preliminary and can go in many directions.

If interested please list name below or contact Jarrod at jscott@bigelow.org

Reading
http://digitalcommons.olin.edu/cgi/viewcontent.cgi?article=1000&context=ahse_pub&sei-redir=1&referer=https%3A%2F%2Fscholar.google.com%2Fscholar%3Fhl%3Den%26q%3Ddabby%2Bchaos%26btnG%3D%26as_sdt%3D1%252C32%26as_sdtp%3D#search=%22dabby%20chaos%22

== Free Energy Theory ==
The Free Energy (FE) minimisation framework tries to explain how biological systems (such as a cell or a brain) self-organise in order to occupy the (often very limited number of) non-equilibrium states that minimise free energy. This is also known as active inference. A simple corollary of active inference is that agents behave as to minimise their prediction error, or the difference between prediction and reality. Thermodynamic free energy is a measure of energy available in a system to do useful work. This can be framed in an information theoretic setting, as the difference between how the world is being represented and how it actually is. A better fit means a lower information-theoretic free energy, as more resources are being put to ‘good use’ in representing the world. The overarching logic of FE theory is that a better model of the world help maintain structure and organisation, which ultimately helps the system resist increases in entropy.
 
 
This is not a set-in-stone project with any concrete aim (yet). We are a few people interested in exploring the theoretical and practical implications of these ideas, and you're more than welcome to join in!
 
 
'''Interested:'''
 
<ul>
<li> Jelle Bruineberg (j.bruineberg@gmail.com ) </li>
<li> Tobias Morville (tobiasmorville@gmail.com) </li>
<li> Susanne Pettersson (guspsusa85@student.edu.se) </li>
<li> Maggie Simon (margaret.w.simon@gmail.com) </li>
<li> Martina Steffen (martinasemail@gmail.com) </li>
<li> Sahil (sahilgar@usc.edu) </li>
<li> Tirtha (Tirtha.Bandy@csiro.au) </li>
<li> Anna (annza944@gmail.com) </li>
<li> Matt O (mmosmond@gmail.com) </li>
<li> Will Chang (williamkurtischang at gmail dot com) </li>
<li> Cobain (mrdc1g10@soton.ac.uk) </li>
</ul>
Reading:
 
http://rsif.royalsocietypublishing.org/content/10/86/20130475 
http://arxiv.org/abs/1503.04187 
http://www.mdpi.com/1099-4300/15/1/311 

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
'Carolina'
'Alice'
'Federico'
'Jean Gab'
'Sola'
'Ilaria'
'Daniel T'

==Comparison of Network vs Scaling Theory based Models in Ecology (Cobain - mrdc1g10@soton.ac.uk)==

One of the biggest difficulties ecologists face is trying to understand the ecosystem dynamics based on very little biological information (compared to the size of the system) - observational data is logistically difficult and can be very expensive to acquire, as well as often being very time consuming. In terms of modelling, to overcome this problem, two approaches have been utilised. 

The first is a network based approach whereby the nodes represent biomass density of a particular species (or a higher level taxanomic group and/or resources) and edges as the trophic interactions (who eats who). This method depends on what we know about the trophic behaviour of the species involved (which is often very limited and there can be many species to parameterise), and represents only the central tendency of what could potentially be very diverse behaviour within and between populations. 

The second approach is to use scaling theory to describe average trophic interactions and other biological processes based on individual organism body size along the size continuum, viewing the community as a very size structured dynamical system. This requires less specific knowledge about the organisms in the community ''per se'', however this again represents only the central tendency of a given size class of what could be very diverse behaviour. Functional differences can be potentially introduced (coupled benthic-pelagic systems for example), but to the detriment of braking down the predictability of the well known allometric scaling laws with size as specificity increases. 

There has been little to no comparison of these two methods of modelling ecological systems (at least to my knowledge) and the question arises that, given the same starting information, how well do both approaches model the dynamics of an ecosystem, given the limited biological information we have? Is one approach better at capturing energy flow through the system / community structure and stability etc. compared to the other? If the models vary then such information will better inform ecologists on which to use depending on what type of questions they are trying to answer. 

Therefore, the project proposed here aims to investigate these two methods. The community that is used to seed an experimental mesocosm setup (mesocosms are large tank set-ups designed to reflect the complexity of natural ecosystems but still being able to be artificially controlled and are still relatively simple), will be input into two models based on the separate approaches and then run to steady state. The model outputs will then be compared and contrasted with eachother as well as the mesocosm community at steady state. Further work may include examining perturbation dynamics, dependent on what data we have available.

'''Interested: Name / Email''' 
Martina Steffen (martinasemail@gmail.com)

==Dynamics of homicide (Matthew Ingram - mingram@albany.edu)==

Integrating temporal, spatial, and multi-level concepts

1:30pm in the coffee shop

Am interested in the discussion on this project...Sola Omoju

Interested - Nilton Cardoso

==Multiplex Adaptive Networks (Daniel - dtc65@cornell.edu)==

7 pm in the coffee shop

==Modeling brain diseases (or cancerous bio pathways) (Sahil - sahilgar@usc.edu)==
Interested people can put a meeting time as per their convenience here.
 
No meeting time indicated
 
One potential meeting time can be 3pm in coffee shop ?
 
Information theoretic algorithms can also be explored for the problem.
 
Discovering Structure in High-Dimensional Data Through Correlation Explanation. http://papers.nips.cc/paper/5580-positive-curvature-and-hamiltonian-monte-carlo
 
Maximally Informative Hierarchical Representations
of High-Dimensional Data. http://jmlr.org/proceedings/papers/v38/versteeg15.pdf

 
'''Interested:'''
 
<ul>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> Laura Condon (lcondon@mymail.mines.edu)</li>
</ul>
 
 

==Analysis of UK parliament speeches 1935-2014 (Stefano - etstefano@gmail.com)==

No meeting time indicated

==Mapping Complexity/Human Knowledge as a Complex Adaptive System (radjohn@live.com)==
2pm/Wed 6/10 in Conference Room (Tentative)

==Navigating Music, Brain and The Edge of Chaos (radjohn@live.com)==
10:45am/Wed 6/10 in Conference Room
9:00am/Thu 7/10 at the Coffee Shop

'''Interested:'''
Sahil Garg

==Analysis of rule-based modeling for dopamine-dependent synaptic plasticity (emilia.m.wysocka@gmail.com)==

----------------------------------------------------------------------------------
Modeling and analysis of phenomenons and evolution of stochastic, combinatorially complex signalling systems in a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system).

----------------------------------------------------------------------------------
Rule-based modeling features:

*biological systems as concurrent processes
*dynamics of post-translational modifications
*domain availability
*competitive binding
*causality and intrinsic structure
*binding sites
*interaction rules replace reaction equations
*infinite number of reactions with a small and finite number of rules
*reduction of parameter space
*“don't care don't write” - adjustable rule contextualization
*single reaction rule and parameters generalize classes of multiple rules
*modular and extensible language
*specification language & simulation/integration environment
*static and causal analysis
*Kappa/KaSim & BioNetGen/NFsim -- specification language/network-free simulator

----------------------------------------------------------------------------------
----------------------------------------------------------------------------------
Meetings:
----------------------------------------------------------------------------------
* WEDNESDAY 11/06/2015

Addressing problems in terms of the other aspects of the projects apart from the biological questions and verification (matching results to some published experiments or known behaviour). So my plan is as follows:

* divide the group (roughly) into people who look in to biological meaning and validation and the one which tries to do the analysis of system phenomenons and evolution of stochastic, combinatorially complex signalling systems in both a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system) - all possible states, scenarios of the system abstracted from the biological meaning.

Things that could be modelled (look in dropbox folder: https://www.dropbox.com/sh/g080w60pt2vgi7v/AACHlMf2JwpP2EZqKq7Di6N2a/possible%20models?dl=0):

* to make things easier and have the modeling part done quickly- base the model on the dopamine-related synaptic plasticity (SBML ODE model): http://www.ebi.ac.uk/biomodels-main/MODEL1101170000;

* I found a series of papers suitable to the subject of the Complex Systems course, e.g.: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC344803/. I was thinking to even try to compare higher level brain data (networks, spiking ..) with purely molecular model

* noise and low copy number (http://www.princeton.edu/~wbialek/our_papers/bialek+setayeshgar_08.pdf); this is quite interesting as for sufficiency of binding events -> http://www.princeton.edu/~wbialek/our_papers/bialek+ranganathan_07.pdf

* spontaneous flipping of interactions (phosphorylations and others) between proteins, described by the god of non-linear dynamics (S. Strogatz)

----------------------------------------------------------------------------------
* THURSDAY 11/06/2015 : http://doodle.com/se23ibwtkrkccs4s

----------------------------------------------------------------------------------
Some refs:
*https://www.dropbox.com/sh/g080w60pt2vgi7v/AADUy7Ge-J-oSYkyTeYOo7V8a?dl=0)
*http://www.pps.univ-paris-diderot.fr/~jkrivine/homepage/Teaching.html

==Ecology non-working group (williamkurtischang at gee-mail dot com)==
[[Informal ecology interest group]].

==Making Supply Chains Resilient to Disasters (mh2905 att columbia dott edu)==
'''Key words''': resilience, natural hazards, supply chains, interdependency, interconnected risks, cascading failures

'''Rational''': I am interested in examining what kinds of network structures and features contribute to increasing resilience of supply chains to natural disasters. I believe this area of work is important because regional disasters negatively impact the global economy through disruptions in supply chain networks. The pioneer study published in Nature urges the need for making supply chains climate-smart (Levermann 2014). Also in the industry, the World Economic Forum published a report to address this issue in 2013. Few researches, however, assess and model the impacts of adverse weather on supply chains. I would like to evaluate the impacts based on modeling.

'''Data set''': Supply chains data of a multinational manufacturing company.Global climate data, disaster data, etc.

'''Possible techniques''': Agent-Based Model, Complexity science, network theory and evolution, complex adaptive systems, GIS, operations research, manufacturing engineering, and I am open to any techniques.

'''Myself''': As I joined the program yesterday, please find my background here.
http://tuvalu.santafe.edu/events/workshops/index.php/Masahiko_Haraguchi

'''Please put your names below if you want to be informed about this project by email'''

'''Interested''':
Masa Haraguchi

==From Ethnic Diversity to Religious Zeal: Retrospective/Predictive Construction of the World Ethnic Map (vdzutsati@asu.edu)==

There is a new dataset on ethnic groups (2014), which claims to include all ethnic groups of the world http://www.cidcm.umd.edu/mar/amar_project.asp. There are also several cross national datasets on religion and other variables of interest, normally, socio-economic http://www.thearda.com/Archive/CrossNational.asp. Coming from social sciences I thought it would be really interesting to apply methods and perspectives from other disciplines to social science data.

One idea that I have in mind is to explore how ethnicity and religion interact.

Quick empirical checks indicate that Subsaharan Africa is home to about 1,500 ethnic and subethnic groups, in comparison to about 90 ethnic groups in the Middle East and North Africa. India alone accounts for nearly 2,000 ethnic and subethnic groups, while Europe, including all the countries of the former USSR and large immigrant groups from other parts of the world, has only about 260 ethnic groups. The picture that emerges from this simple comparison is that the spread of Abrahamic religions appears to be associated with the high depletion rate of ethnic groups. The exception of China, which has little more than 50 ethnic groups, can be explained by the country’s long history of a centralized state.

The idea then is to assume that we have had the control group of nations that did not experience the influx of Abrahamic religions (or more precisely received limited exposure to them) and the experimental group that was exposed to the spread of Abrahamic religions. Since we know what the distribution of ethnic groups in the control group is we can project what the distribution of ethnic groups in the experimental group would be, if the group were not exposed to Abrahamic religions.

Of course, we could go in the other direction too and make a projection about future – what would happen to ethnic groups if all of them adopted an Abrahamic religion.

One of the challenges in this project is absence of a dynamical system, I.e. We don’t have data on how these things changed over time, but I still think that projection about the future and the past would be kinda cool:)

'''Interested''':

==Nationalist vs religious rebel violence (vdzutsati@asu.edu)==

I would like to propose yet another possible project. This time it’s on rebel violence. I have a large dataset on rebel (and government) violence (~1 million observations). The paper that used this data was published couple of months ago (Islamists and Nationalists: Rebel Motivation and Counterinsurgency in Russia’s North Caucasus). The dataset contains event counts, GIS data, demographic and economic characteristics and some other stuff. The primary hunch of the paper was to discover the differences between nationalist and Islamist rebels.

Given all the cool methods we are learning here, my first instinct is to use the methods and tools (TISEAN?) to explore and discover whether nationalist violence is substantively different from religious violence and try to answer why.

One problem that I can see is that the event coding was done by a machine, so this may have “contaminated” the data. But if both religious and nationalist data were contaminated equally then the difference should still be evident.

'''Interested''':

==[[Archived Projects ]]==

Complex Systems Summer School 2015-Projects & Working Groups

2015-07-02T19:59:31Z

Rbarnes: /* Richard's Current Table */

{{Complex Systems Summer School 2015}}

==[[Reservations for Evans Science rm. 215]]==

Click on the title link to reserve Evans Science rm. 215 for group usage.

==Californian Drought Model==
Problem definition:
Water is an ongoing problem in California. Although corrective measures are taken for some years, periods of droughts are depleting water ground levels to not sustainable levels.
Aims:
A Netlogo simulation on how likely Californian agents evolve with regards to drought. The ABM could be used to explore the potential impacts of forms of regulation.
Outcomes:
Create an exploratory agent based model based on real data, enabling the possibility of simulating different methods of regulation, including feedbacks between the economic and hydrologic systems.

Extension 1: We will conduct a network analysis of Twitter data (hashtags #drought #California) to explore the social dynamics and information flows between stakeholders.

Extension 2: Phase-space reconstruction of groundwater level time series data, comparing the dynamics in different parts of the Central Valley.

==City Resilience==
'''Summary:''' This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities. 
'''Contact:''' Richard Barnes (rbarnes@umn.edu) 
'''Participants:''' Alex Tejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang, Martina Steffen, Juan Carlos Castilla, Brent Schneeman 
'''Wiki page:''' [[Resilience of Cities]]

==Complex Adaptive Systems and the Narrative approach: transdisciplinary methodologies for Complexity Science==
The narrative approach and it causality is different from causality in logico-scientific approaches. Making bridges among methodologies transfers knowledge, encourages important questions and switches philosophical paradigms. Formal frameworks of C(A)S can realy be complemented with the narrative, and actually should. The narrative approaches in Complexity Science are an emerging trend for fund-granting, and is really up to date to process documentary and speech-based data, reveal hidden meanings, distinguish causes from effects in overlapping systems of realms - "at the edge" of technology, social, economic, scientific, legal and other domains. The role of time in coding, intuitively generated conditional rules and computational paths. Combinations of C(A)S and the narrative is combination of quantitative and qualitative data and thinking in original - out of convertation and information loss. Synthesis of sciences is what it is about.

'''Questions''':
How to combine CS approaches (in particular CAS) and the narrative ones in a rule-based way? What to start from? What vizualizations there can be designed and used? Other questions at your descrete.

'''Some references''':
- Non-Equilibrium Social Science in ICT and Economics, CORDIS, EU: http://cordis.europa.eu/project/rcn/102344_en.html, http://www.nessnet.eu/

- Combining Complexity Theory with Narrative Research: https://youtu.be/pHjeFFGug1Y

- Haridimos Tsoukas and Mary Jo Hatch (2001), "Complex thinking, complex practice: The case for a narrative approach to organizational complexity: http://www.brown.uk.com/teaching/qualitativepostgrad/tsoukas.pdf

- David Christian, "Big History", Astrophysics, Chemistry, Biology, Information, emergence of life, technology: https://youtu.be/GlmvFjFceD8

'''Contact''': Anna (annza944@gmail.com)

Meet on Monday over lunch

'''Interested''': Marie Pierre, Jeroen, Jim, Melissa

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
 
'Carolina'
 
'Alice'
 
'Federico'
 
'Jean Gab'
 
'Sola'
 
'Ilaria'
 
'Daniel T'
 
'Sahil Garg

==Ebola==
The 2014-15 Ebola virus disease (EVD) outbreak in West Africa presented both unique opportunities and unique challenges to the epidemiological modeling community. For the first time during an emerging infectious disease outbreak, high resolution data--from a variety of sources--were made available to the academic community and many public health decision makers genuinely engaged with mathematical and computational modelers. However, the popular and scientific press were highly critical of most models ability to project the outbreak's course. The following key and open questions seem ripe for investigation using a complex adaptive systems lens:
 
1) What features of EVD transmission are most problematic for reliable, robust forecasting: changing behavior, intervention, viral evolution, complex social networks, etc?
 
2) How/can we use digital data to either improve forecasts or inform model selection?
 
3) Can one quantify the value of additional information in real-time?
 
'''Contact:''' Samuel Scarpino, SFI Omidyar Fellow, Santa Fe Institute - scarpino@santafe.edu
 
 
'''Interested:'''
 
Marie-Pierre Hasne 
Chris Verzijl 
Junming Huang 
'Sola Omoju 
Christine Harvey 
Daniel Citron (dtc65@cornell.edu) 
William Chang (williamkurtischang at gee-mail)

==Effect of landscape topography on vegetation connectivity and navigability==
''Context''
 
Landscape topography influences the dynamics of the processes that take place on it. Evolution of ecosystems networks, river networks, vegetation cover type, microclimate cycles are all interlinked deeply to the local landscape topography.

In addition to the landscape these networks are also influenced by each other conditioning the emergence and stability of ecosystems, and subsequently the behaviour of agents in the ecosystem like migration pathways of animal herds, human settlement patterns etc.

Connectivity patterns emerge from the interaction of these processes, and thus a better understanding and quantification of those patterns is critical to understanding the dynamics of the system.

 
''How do we want to approach the problem''
 
In this project, we will randomly generate landscape topography and subsequent vegetation cover from a set of parameters from known geological and biological processes. The generated data set will then be used to investigate the following questions:
 
(1) What is the connectivity of landscape patterns that emerge at different scales using different techniques such as clustering analysis, percolation theory or network theory, and can we quantify them ?
 
(2) What is the navigability of biological agents (e.g animals, humans, robots!) under such landscape patterns. We can compare mobility trails in existing landscapes to validate our hypothesis. We propose to use the tools like ABMs to simulate and characterise mobility success.
 
(3) We further aim to compare the measures of navigability with the metrics of connectivity, establishing a framework of comparison.
 

''Contact:''
 tirtha.bandy@csiro.au
 alej.tejedor@gmail.com

''Meetings''
(1) Wednesday 1pm Coffee shop

People Interested 
Martina

==[[Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe]]==
Click on the link to go to the project page for meetings, project details, and progress.
 
contact: mveitzel@ucsc.edu
 

==Mapping Complexity/Human Knowledge as a Complex Adaptive System‏==
''Context''
 
Ants leave pheromone trail patterns which they are aware of only in a local sense. They do not have the cognitive faculties to step back and look at the trails and grasp the ant-trail network as a totality. Also, the artifacts they leave behind are physical entities which then provides the aggregate feedback to the aggregate ant body to then feed the evolution of the ant body as a CAS system. In contrast, humans do have the requisite cognitive abilities. The "pheromone trails" we leave behind are the knowledge trails coded in symbolic knowledge artifacts. In contrast to the physical artifacts that ants leave behind, the knowledge artifacts that we leave behind are far more flexible and potent, both at the aggregate as well as at the individual levels. But like the ants, until recently, we did not have the means to step back and map the knowledge "pheromone trails" to obtain the big picture and its global/local dynamics. The burgeoning field of scientometrics is making available visualization tools to help us map and study the evolutionary dynamics of the knowledge network structures.
 
''Data and Questions''
 
The goals of this project include
<ol>
<li>Extract the terms from approximately 1600 working papers published by SFI</li>
<li>Map the intra/inter conceptual network structures</li>
<li>Study the evolution of these structures across time</li>
<li>High-light the gap-closure of knowledge reverse-salients (if any)</li>
<li>Capture any of the network patterns that repeat</li>
<li>Study the diffusion of concepts across the network</li>
<li>Provide visualization tools for navigating the complexity corpus, etc</li>
</ol>
 
''Possible methods''
 
Latent Semantic Analysis (LSA) and Latent Document Analysis (LDA)
 
References:
<ol>
<li> SFI Working Papers: http://www.santafe.edu/research/working-papers/</li>
<li> Atlas-Science-Visualizing: http://www.amazon.com/Atlas-Science-Visualizing-What-Know/dp/0262014459</li>
<li> Atlas-Science-Visualizing WebSite: http://scimaps.org/atlas</li>
<li> Mapping-Scientific-Frontiers-Knowledge-Visualization: http://www.amazon.com/Mapping-Scientific-Frontiers-Knowledge-Visualization/dp/1447151275</li>
<li> Katy Börner presents at Science of Science: https://www.youtube.com/watch?v=pzCqGBNzomE</li>
<li> Scholarly Data, Network Science, and (Google) Maps:https://www.youtube.com/watch?v=vos5QBDywMM</li>
<li> LSA Video Lect: http://videolectures.net/slsfs05_hofmann_lsvm/</li>
<li> What is LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf</li>
<li> LSA Wiki:http://en.wikipedia.org/wiki/Latent_semantic_analysis</li>
<li> LDA: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm</li>
<li> Fusari, A. (2014). Methodological Misconceptions in the Social Sciences: Rethinking Social Thought and Social Processes: http://www.springer.com/us/book/9789401786744 </li>
<li> Kirsh, D. (2013). Thinking with external representations. Cognition Beyond the Brain: http://adrenaline.ucsd.edu/kirsh/Articles/Interaction/thinkingexternalrepresentations.pdf </li>
<li> Holland, J. H. (1992). Adaptation in natural and artificial systems: https://mitpress.mit.edu/index.php?q=books/adaptation-natural-and-artificial-systems </li>
<li> Where to start with text mining. http://tedunderwood.com/2012/08/14/where-to-start-with-text-mining/ </li>
<li> Singular Value Decomposition Tutorial https://www.ling.ohio-state.edu/~kbaker/pubs/Singular_Value_Decomposition_Tutorial.pdf </li>
<li> Latent Semantic Analysis (LSA) Tutorial: http://www.puffinwarellc.com/index.php/news-and-articles/articles/33-latent-semantic-analysis-tutorial.html </li>
<li> LSA in Detail: http://www2.denizyuret.com/ref/berry/berry95using.pdf </li>
<li> Web Based LSA: http://webapp1.dlib.indiana.edu/newton/lsa/index.php </li>
<li> Another Technique: t-Distributed Stochastic Neighbor Embedding (t-SNE): http://lvdmaaten.github.io/tsne/ </li>
<li> tf–idf: https://en.wikipedia.org/wiki/Tf%E2%80%93idf </li>

</ol>
 
'''Interested:'''
 
<ul>
<li> John Thomas (radjohn@live.com) </li>
<li> Haitao Shang (hts@mit.edu) </li>
<li> Christopher Verzijl (cjo.verzijl@gmail.com) </li>
<li> Anna Zaytseva (annza944@gmail.com) </li>
<li> Penny Mealy (penny.mealy@gmail.com) </li>
</ul>
 
 

==Navigating Music, Brain and The Edge of Chaos ‏==
''Context'' 
[[Navigating Music, Brain and The Edge of Chaos]]
 
Brain Sciences have revealed that we/it lives "on the edge of chaos" exhibiting "self-organized criticality" that is tentatively balanced between normalcy and madness. Over the course of history, humans have used various agents and activities to shape, influence and control this living-chaos ranging from substances such as caffeine, sugar, drugs etc., to activities such as the arts (including music), social-discourse/therapy, meditation etc. Of these, music has a distinct role in shaping our moods and helping us transition between different mental states, as well as maintain it for extended periods of time. Clearly we have been using music to help us control and shape the internal chaos. But until recently, the quantitative instrumentation of this massively complex system that comprises of close to a 100-billion neurons networked into a 1000-trillion synaptic edifice has not been available to the common man. But of late, affordable, wearable EEG's are available on the market, thus making the quantitative study of the influence of music in brain dynamics feasible on a large-scale/crowd-sourcing sense. To help come to terms with the complexity of our 1000-trillion synaptic edifice, we need to gather data on a vast scale. The proposed research is a proof-of-concept, exploratory foray into making this happen.
 

History of music in 5 min
https://www.youtube.com/watch?v=Gt2zubHcER4

'''Interested:'''
 
<ul>
<li> Sara Lumbreras (sara.lumbreras@gmail.com) </li>
<li> Ilaria b (bertazzi.ilaria@alice.it) </li>
<li> Braun, Urs (urs.braun@zi-mannheim.de) </li>
<li> John Thomas (radjohn@live.com) </li>
<li> Glenn Magerman (glenn.magerman@kuleuven.be) </li>
<li> Daniel Friedman (DanielAriFriedman@gmail.com)</li>
</ul>
 
 

==Powerlaw fitting and alternative distributions - Theory/statistics ==
Clauset, Shalizi and Newman (2009) propose a maximum likelihood method to estimate the powerlaw exponent of a variable of interest. This is a great improvement on earlier methods such as OLS that dominated the literature up to then. However, one can fit a powerlaw to any dataset and the most we can say is that our observations are consistent with the hypothesis that x is drawn from a powerlaw distribution. One easily implementable method to compare the powerlaw fit to other fits is then a likelihood ratio test for both models.

One particular discussion is the distinction between a powerlaw (PL) and a log-normal (LN) fit. For an avid discussion between both fits on city size and Zipf's law, see Eeckhout (2004, 2009) and Levy (2009), where the discussion now settled on city sizes following a log-normal distribution instead of a powerlaw. Similarly for the discussion on firm size distribution: Simon and Bonini, 1958; Ijri and Simon, 1977; Stanley et al., 1995; Sutton, 1997; Axtell, 2001; Okuyama et al., 1999; Cabral and Mata, 2003; Gaffeo et al., 2003; Aoyama et al., 2004; Fujiwara et al., 2004a,b; Kaizoji et al., 2006; Takayasu et al., 2008; Duchin and Levy, 2008; Schwarzkopf and Farmer, 2008, ...

This distinction matters for several reasons:
- PL and LN come from very similar model and differences in initial conditions can lead to very different outcomes. PL exhibits a choice for x_min, below which the unit of observation is not feasible to exist (eg minimum city size, firm size, word length, ...). LN has no minimum size.
- PL and LN that look similar are the difference between infinite (PL) and large but finite variance (LN)
- shock propagation: when unit-level shocks are large enough to show aggregate perturbances if the distribution is powerlaw with infinite variance, while these shocks wash out fast when the distribution is log-normal. (Gabaix, 1999; Gabaix 2009; Acemoglu et al. 2012, ...).

I have encountered some issues which I would like to explore further:
1. The distinction between lognormal and powerlaw in the data is very sensitive to data truncation: in the above discussions, researchers have slightly different datasets, covering more or less of the population at hand. Left-truncation (i.e. observations not in the dataset because they are too small to be reported) can strongly drive the outcome of the fit, even when endogenizing the x_min cutoff. I have data on the universe of Belgian firms, much more complete than e.g. US Census data, where I have done some preliminary tests on this. The question is then: how to formalise this distinction and what are the theoretical and practical caveats to look out for when applying this method.
2. MLE fitting seems to be sensitive to the choice of units as well: rescaling a variable by a factor 1000, 1000000 etc seems to influence the endogenous x_min choice and hence the estimated parameter. This reminds me of some work on scaling invariance in negative binomial estimators. What is going on here?
3. Can we set up a model that generalises both? I've been looking at Levy stable distributions, but did not do anything with it yet.

Interested people:
* Corbain
* Laura
*Binyang
*Sahil
*Tirtha
*Glenn
*Junming

Literature:
Powerlaw fitting in empirical data - Clauset, Shalizi and Newman (2009): http://arxiv.org/abs/0706.1062

==Organ Transplant Analysis==
Over 120,000 people in the United States are currently on the waiting list for an organ transplant. The size of this waiting list relates to over 6,000 deaths a year while waiting for a transplant and tens of billions of dollars in government spending. I have access to the following data sets:
 
* All transplants performed in the US from October 1987 to June 2014 (including follow-up data)
* All living and deceased organ donors in the same time period (with follow-up data on living donors)
* Waiting list data for everyone who signed up for the list
* 2012 National Survey on Attitudes and Behaviors on Organ Donation
* Social media data relating to organ donation since 2008

Open to ideas and suggestions for the topic, there are a lot of interesting questions to investigate including cultural/racial/gender differences in organ donation. I have several preliminary reports and exploratory analysis done on differences in donors.

Second Meeting Thursday, June 11th 4:15 in the coffee shop!

===Matching Game===
Rules for the matching game:

Objective: Create the largest chain possible using blood types in the room.

Follow the blood type guidelines for donation to form a donor chain.
*Assume that self-matches are not possible, for example someone with a donor of type AB and a recipient of type AB can not match themselves and needs to find someone else to complete their chain.
*Create a donor chain to help as many people as possible.
*Post results below for the prize
*There will be several "good samaritan" donors which will donate and need nothing in return, this is a great way to start your chain. Using a good samaritan chain also means you don't need a donor for the end point.

====Game Workspace====
Use this workspace to post for and find matches!

==== Donor Chains ====
Post your chain here. Example is given below:

=====Example Chain 1=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| A
|-
| Laura
| AB
| B
|-
| Matt
| A
| AB
|}

=====Example Chain 2=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| None
|-
| Laura
| A
| B
|-
| Matt
| None
| A
|}

======Richard's Current Table======

3804 Iterations

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Maggie
| O
| X
|-
| Jakub
| O
| A
|-
| MatthIn
| AB
| O
|-
| Alejandro
| O
| AB
|-
| James
| AB
| O
|-
| Susanne
| A
| AB
|-
| Michael
| O
| A
|-
| Alice
| AB
| O
|-
| Vanessa
| A
| AB
|-
| Urs
| B
| A
|-
| Melissa
| A
| B
|-
| Chris
| O
| A
|-
| Carolina
| O
| A
|-
| Havier
| AB
| O
|-
| Brent
| O
| AB
|-
| Masa
| A
| O
|-
| AndySchauf
| A
| A
|-
| Maria
| O
| A
|-
| Kleber
| O
| A
|-
| Jeroen
| O
| O
|-
| Martina
| A
| O
|-
| Richard
| A
| A
|-
| Glenn
| O
| A
|-
| Sam
| O
| B
|-
| Cobain
| B
| A
|-
| Charon
| B
| B
|-
| Nilton
| O
| B
|-
| MariePierre
| A
| O
|-
| Jae
| B
| A
|-
| Laurence
| O
| B
|-
| Keith
| AB
| O
|-
|}

Unused:

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| X
|-
| Jelle
| AB
| X
|-
| JGab
| O
| X
|-
| LauraCondon
| O
| X
|-
| Matthew
| O
| X
|-
| MatthewO
| AB
| O
|-
|}

==Multi-dimensional social networks in the evolution, development and resilience of informal economies. ==
Informal economies are defined as economic activities that occur outside the purview of corporate public and private institutions. These types of economies proliferate where traditional economic actors are unable to productively exercise their activities, especially due to costly constraints (De Soto 2003). Firms may face adverse incentives to expand production by hiring more workers or incorporating more capital due to the low productivity of their workers or the predatory practices of rapacious elites or corrupt governments. Labor itself, i.e. people, may face hurdles in trying to make the jump towards entrepreneurial activities or jobs in the formal economy which allow the accumulation of experience, retirement savings, access to insurance or precautionary savings to face unexpected events (like disease, disability, etc.) due to lack of capital access (whether human and financial).

'''For this project, we propose a different interpretation: We seek to understand and model how multi-faceted social networks provide robust alternatives to formal economies, which far from being seen as degenerate forms of social organization, in many instances co-exist, challenge and compete with formal employment and economic activities. While some types of informal economies operate under adverse contexts, their resiliency may be understood as a type of adaptive fitness and not the mere result of stubborn cultural path-dependency. '''

For the most part, informal economies have been analysed as counterpoint to an ideal type of a formal economy with low levels of trust, respect of property rights, access to credit and public institutions - like the court systems (see Losby et al. 2002 for a review). However, informal economies have been coupled with their formal counterparts since the development of capitalism in the 19th Century. To name a famous historical example, Old London's East End's informal sector was described harshly by Engels (1844) in his famous tract, "The Condition of the Working Class in England". But almost fifty years later, in a new preface to the book, Engels recognized the progress that took place there under the aegis of working class organizations in the area.

Research has begun to catch on to this idea – developing a literature in the areas of informal risk sharing and remittances, which are in some sense informal counterparts to insurance and banking. Consumption patterns in poor, rural villages are remarkably smooth suggesting that risk-sharing measures are prevalent, although imperfect (Townsend). Field studies of networks underlying village risk sharing systems have found that households primarily receive help from existing social connections, such as friends and relatives, in the form of informal loans or transfers. Theoretical work in network economics, by authors such as Matt Jackson, found that certain empirically prevalent network structures may directly benefit the stability of favor exchange systems. Another line of inquiry focuses on remittance income, generally informal transfers across kinship ties. World Bank researchers have found that remittances from overseas migrants respond dramatically to regional income shocks, replacing upwards of 60% of lost income in households with international migrants. Further studies, cited below, have generalized those results. Furthermore, reductions in the cost of sending remittances, which occurs with the advent of mobile money, further mitigates exposure to risk in receiving households.

Also recently, authors have written on how communities come together in situations where formal economies are unavailable by external shocks (like disasters) or due to lack of access (due to conditions of abject poverty). In the case of the latter, Venkatesh (2008) provided gripping testimonies of how informal economies arose and developed in the South Side of Chicago in low trust environments via cash transactions and informal service contracts enforced by (and for the benefit of) local criminal gangs. These gangs were seen, surprisingly, as alternative coordinating mechanisms to settle claims between neighbors. In the case of the former, Storr and Grube (2013) in a series of papers has argued how "shared histories and perspectives, and the stability of social networks within the community" allows communities who suffered disasters to cope and endogenously resolve immediate and complex problems. Providing an example of these social networks in action, research has found that remittances flow quickly into areas affected by natural disasters when there is a technology in place for it to happen.

Taking these lines of inquiry together, the findings suggest that informal handling of risk takes place on a large scale and that social networks informally connect communities in ways that impact their economies. Recent trends suggest that the interaction of formal and informal economic processes is growing on a nationwide scale. First, the aggregate value of remittance flows is large and growing, nearing the value of foreign direct investment. Second, advancements that formalize previously informal transactions are expanding dramatically in emerging economies through various forms of branchless banking. National economies may be embedded in profoundly influential informal systems that have never been holistically studied.

On a more general note, this project touches lingering questions in economics. For example, some might argue that more stringent labor regulations should have increased informal employment, but in fact the opposite happened. And while the economic rationale for the former statement remains valid, we suggest that unions, as other types of social organizations, as examples of ways whereas people interact via organized networks, provide richer dimensions than those suggested by their interaction as mere economic agents. Hence, unions, as well as other types of faith-based, ethnic, community and other interest groups may provide ways of interaction that escape narrow economic outcomes.

====References====

• De Soto, Hernando (2000/2003) The Mystery of Capital. Basic Books.
• Losby, Jan; Else, John; Kingslow, Marcia; Edgcomb, Elaine; Malm, Erika and Vivian Kao (2002) The Informal Economy: A Literature Review. ISED Consulting and Research and the Aspen Institute Working Paper. http://www.kingslow-assoc.com/images/Informal_Economy_Lit_Review.pdf
• Townsend, Robert M. "Risk and Insurance in Village India." (1994)
• Fafchamps, Marcel, and Susan Lund. "Risk-sharing Networks in Rural Philippines." (2003)
• Weerdt, Joachim De, and Stefan Dercon. "Risk-sharing Networks and Insurance against Illness." (2006)
• Matthew O. Jackson, Tomas Rodriguez-Barraquer and Xu Tan. “Social Capital and Social Quilts: Network Patterns of Favor Exchange.” (2012)
• Yang D and H Choi."Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines"(2007)
• Kurosaki, Takashi. "Consumption vulnerability to risk in rural Pakistan." (2006)
• Jack, W, and T. Suri. "Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution” (2014)
• Engels, Friedrich (1844/1892) The Condition of the Working Class in England. http://www.gutenberg.org/files/17306/17306-h/17306-h.htm
• Venkatesh, Sudhir (2008) Gang Leader for a Day: A Rogue Sociologist Takes to the Streets. Penguin Books.
• Storr, Virgil and Laura Grube (2013) The Capacity for Self-Governance and Post-Disaster Resiliency. George Mason University, Department of Economics Working Paper 13-37. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2350830##
• Blumenstock, Joshua Evan and Fafchamps, Marcel and Eagle, Nathan. “Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda” (2011).
• Ratha, Dilip. "Workers’ remittances: an important and stable source of external development finance." (2005).
• Pénicaud, Claire, and Arunjay Katakam. State of the Industry 2013: Mobile Financial Services for the Unbanked. Rep. N.p.: GSMA MMU, 2013.

If interested, please list your name below.

'''Interested:'''
<li> Eloy Fisher (eloy.fisher@fulbrightmail.org) </li>
<li> Carolina Mattsson (carromattsson@gmail.com) </li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>

<li> Jakub Rojcek (jakub.rojcek@gmail.com) </li>

==Exploring Community Formation through Analysis of Scholarly Corpus (ArXiv)==

The Arxiv [http://arxiv.org/] is a free online repository of scientific preprint articles, mostly from physics and mathematics. It currently contains over 800,000 articles, dating back to 1991. Currently, a lot of the data associated with this repository is completely available: paper submission dates; full texts of papers; author names and coauthorship information. Additionally, there is some citations data available. (If necessary, I can also find papers' subdiscipline labels; submitting authors' email address domain names; other things.)

In the past I have been using these data to try and explore how communities of authors who have shared interests grow over time. For example, we can pinpoint the first ever paper about topological insulators, and search for all subsequent papers on that topic. As more papers are written, authors begin to join the field of research and to form strong ties by collaborating with one another. We can use the ArXiv data to visualize and analyze exactly how these communities form and grow.

===Brainstorming notes===
Isolation between topics, crossing interdisciplinary boundaries, finding (topological) separation distance between disciplines or research groups

Tipping points - can we look for separation of or mergers of two groups or disciplines?

Can we identify key papers (or groups of papers) that initiate ties between fields.

Sentiment analysis: can we identify when a paper's citation is an endorsement or a refutation?

Tools for Analysis: Change point detection; relational event models

Can we find more comprehensive/better citation data?

Lucene and indexing tools:
- Can we re-index our text database after removing stop words?
- Can we index the titles and abstracts?
- Elastic Search - tool for interacting with Lucene

This set of text-processing [http://alias-i.com/lingpipe/demos/tutorial/read-me.html tutorials] is pretty handy for background info and inspiration. The software package doesn't simply plug-into a Lucene/Solr/Elastic Search index, but it could be done. This package from [http://nlp.stanford.edu/software/ Standford] seems very capable.

===Next meeting===
Thursday @ 9AM in Coffee Shop

== PolComplex - Epistemic communities and policy dynamics in the UK Parliament ==

Over the last twenty years, there has been extensive debate about what the core topics in public policy are, and if they change according to type and number. Furthermore, it is still not clear how policy topics mutate and diffuse over time. Human-based text analysis of governmental documents has shed only some light on these research questions: 21 general topics have been proposed, while other accounts tend to restrict it to about five main clusters. Yet, this approach has not been able to devise a reliable and systemic method to capture topic dynamics, diffusion, and their relation to the human actors that talk about them.

This project is an extension of an exploratory research that intends to overcome such limitations, through the application of natural language processing (more specifically, dynamic topic modeling and sentiment analysis), topological and network analysis on a dataset containing UK House of Commons debates ranging from 1975 to 2014. The aim is manyfold. We hope to identify the structure and dynamics of epistemic policy communities - i.e. of political actors revolving around similar policy interest -, and how these relate to factors such as party membership, seniority, relevant historical events.

Some references:

[http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.734168 Taddy (2013), "Multinomial Inverse Regression for Text Analysis"] 

[https://www.cs.princeton.edu/~blei/papers/BleiLafferty2007.pdf Blei and Lafferty (2007), "A Correlated Topic Model of Science"] 

== Genomic variations from a chaotic mapping ==

Following Dabby CHAOS 6 (2), 1996 Musical variations from a chaotic mapping, this project will explore genomic variations in key metabolic genes that are known to be widely spread across bacterial phylogeny (e.g. the Nif cluster which is used in nitrogen fixation). There are several ways that genomic data can be treated as "music":

1) Nucleotide: 4 notes - A, G, C, T 
2) Amino Acids: 20 notes 
3) codon triplets: 64 notes 
4) tetra nucleotides: 256 notes. 

One possible goal is to take a seed sequence from some random organism and generate new variations. Then using homology search (e.g. BLAST) or phylogenetics, determine if that variation exists in nature? If not, perhaps model the potential protein folding (if possible?) to determine if that variation could exist in nature. This is VERY preliminary and can go in many directions.

If interested please list name below or contact Jarrod at jscott@bigelow.org

Reading
http://digitalcommons.olin.edu/cgi/viewcontent.cgi?article=1000&context=ahse_pub&sei-redir=1&referer=https%3A%2F%2Fscholar.google.com%2Fscholar%3Fhl%3Den%26q%3Ddabby%2Bchaos%26btnG%3D%26as_sdt%3D1%252C32%26as_sdtp%3D#search=%22dabby%20chaos%22

== Free Energy Theory ==
The Free Energy (FE) minimisation framework tries to explain how biological systems (such as a cell or a brain) self-organise in order to occupy the (often very limited number of) non-equilibrium states that minimise free energy. This is also known as active inference. A simple corollary of active inference is that agents behave as to minimise their prediction error, or the difference between prediction and reality. Thermodynamic free energy is a measure of energy available in a system to do useful work. This can be framed in an information theoretic setting, as the difference between how the world is being represented and how it actually is. A better fit means a lower information-theoretic free energy, as more resources are being put to ‘good use’ in representing the world. The overarching logic of FE theory is that a better model of the world help maintain structure and organisation, which ultimately helps the system resist increases in entropy.
 
 
This is not a set-in-stone project with any concrete aim (yet). We are a few people interested in exploring the theoretical and practical implications of these ideas, and you're more than welcome to join in!
 
 
'''Interested:'''
 
<ul>
<li> Jelle Bruineberg (j.bruineberg@gmail.com ) </li>
<li> Tobias Morville (tobiasmorville@gmail.com) </li>
<li> Susanne Pettersson (guspsusa85@student.edu.se) </li>
<li> Maggie Simon (margaret.w.simon@gmail.com) </li>
<li> Martina Steffen (martinasemail@gmail.com) </li>
<li> Sahil (sahilgar@usc.edu) </li>
<li> Tirtha (Tirtha.Bandy@csiro.au) </li>
<li> Anna (annza944@gmail.com) </li>
<li> Matt O (mmosmond@gmail.com) </li>
<li> Will Chang (williamkurtischang at gmail dot com) </li>
<li> Cobain (mrdc1g10@soton.ac.uk) </li>
</ul>
Reading:
 
http://rsif.royalsocietypublishing.org/content/10/86/20130475 
http://arxiv.org/abs/1503.04187 
http://www.mdpi.com/1099-4300/15/1/311 

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
'Carolina'
'Alice'
'Federico'
'Jean Gab'
'Sola'
'Ilaria'
'Daniel T'

==Comparison of Network vs Scaling Theory based Models in Ecology (Cobain - mrdc1g10@soton.ac.uk)==

One of the biggest difficulties ecologists face is trying to understand the ecosystem dynamics based on very little biological information (compared to the size of the system) - observational data is logistically difficult and can be very expensive to acquire, as well as often being very time consuming. In terms of modelling, to overcome this problem, two approaches have been utilised. 

The first is a network based approach whereby the nodes represent biomass density of a particular species (or a higher level taxanomic group and/or resources) and edges as the trophic interactions (who eats who). This method depends on what we know about the trophic behaviour of the species involved (which is often very limited and there can be many species to parameterise), and represents only the central tendency of what could potentially be very diverse behaviour within and between populations. 

The second approach is to use scaling theory to describe average trophic interactions and other biological processes based on individual organism body size along the size continuum, viewing the community as a very size structured dynamical system. This requires less specific knowledge about the organisms in the community ''per se'', however this again represents only the central tendency of a given size class of what could be very diverse behaviour. Functional differences can be potentially introduced (coupled benthic-pelagic systems for example), but to the detriment of braking down the predictability of the well known allometric scaling laws with size as specificity increases. 

There has been little to no comparison of these two methods of modelling ecological systems (at least to my knowledge) and the question arises that, given the same starting information, how well do both approaches model the dynamics of an ecosystem, given the limited biological information we have? Is one approach better at capturing energy flow through the system / community structure and stability etc. compared to the other? If the models vary then such information will better inform ecologists on which to use depending on what type of questions they are trying to answer. 

Therefore, the project proposed here aims to investigate these two methods. The community that is used to seed an experimental mesocosm setup (mesocosms are large tank set-ups designed to reflect the complexity of natural ecosystems but still being able to be artificially controlled and are still relatively simple), will be input into two models based on the separate approaches and then run to steady state. The model outputs will then be compared and contrasted with eachother as well as the mesocosm community at steady state. Further work may include examining perturbation dynamics, dependent on what data we have available.

'''Interested: Name / Email''' 
Martina Steffen (martinasemail@gmail.com)

==Dynamics of homicide (Matthew Ingram - mingram@albany.edu)==

Integrating temporal, spatial, and multi-level concepts

1:30pm in the coffee shop

Am interested in the discussion on this project...Sola Omoju

Interested - Nilton Cardoso

==Multiplex Adaptive Networks (Daniel - dtc65@cornell.edu)==

7 pm in the coffee shop

==Modeling brain diseases (or cancerous bio pathways) (Sahil - sahilgar@usc.edu)==
Interested people can put a meeting time as per their convenience here.
 
No meeting time indicated
 
One potential meeting time can be 3pm in coffee shop ?
 
Information theoretic algorithms can also be explored for the problem.
 
Discovering Structure in High-Dimensional Data Through Correlation Explanation. http://papers.nips.cc/paper/5580-positive-curvature-and-hamiltonian-monte-carlo
 
Maximally Informative Hierarchical Representations
of High-Dimensional Data. http://jmlr.org/proceedings/papers/v38/versteeg15.pdf

 
'''Interested:'''
 
<ul>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> Laura Condon (lcondon@mymail.mines.edu)</li>
</ul>
 
 

==Analysis of UK parliament speeches 1935-2014 (Stefano - etstefano@gmail.com)==

No meeting time indicated

==Mapping Complexity/Human Knowledge as a Complex Adaptive System (radjohn@live.com)==
2pm/Wed 6/10 in Conference Room (Tentative)

==Navigating Music, Brain and The Edge of Chaos (radjohn@live.com)==
10:45am/Wed 6/10 in Conference Room
9:00am/Thu 7/10 at the Coffee Shop

'''Interested:'''
Sahil Garg

==Analysis of rule-based modeling for dopamine-dependent synaptic plasticity (emilia.m.wysocka@gmail.com)==

----------------------------------------------------------------------------------
Modeling and analysis of phenomenons and evolution of stochastic, combinatorially complex signalling systems in a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system).

----------------------------------------------------------------------------------
Rule-based modeling features:

*biological systems as concurrent processes
*dynamics of post-translational modifications
*domain availability
*competitive binding
*causality and intrinsic structure
*binding sites
*interaction rules replace reaction equations
*infinite number of reactions with a small and finite number of rules
*reduction of parameter space
*“don't care don't write” - adjustable rule contextualization
*single reaction rule and parameters generalize classes of multiple rules
*modular and extensible language
*specification language & simulation/integration environment
*static and causal analysis
*Kappa/KaSim & BioNetGen/NFsim -- specification language/network-free simulator

----------------------------------------------------------------------------------
----------------------------------------------------------------------------------
Meetings:
----------------------------------------------------------------------------------
* WEDNESDAY 11/06/2015

Addressing problems in terms of the other aspects of the projects apart from the biological questions and verification (matching results to some published experiments or known behaviour). So my plan is as follows:

* divide the group (roughly) into people who look in to biological meaning and validation and the one which tries to do the analysis of system phenomenons and evolution of stochastic, combinatorially complex signalling systems in both a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system) - all possible states, scenarios of the system abstracted from the biological meaning.

Things that could be modelled (look in dropbox folder: https://www.dropbox.com/sh/g080w60pt2vgi7v/AACHlMf2JwpP2EZqKq7Di6N2a/possible%20models?dl=0):

* to make things easier and have the modeling part done quickly- base the model on the dopamine-related synaptic plasticity (SBML ODE model): http://www.ebi.ac.uk/biomodels-main/MODEL1101170000;

* I found a series of papers suitable to the subject of the Complex Systems course, e.g.: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC344803/. I was thinking to even try to compare higher level brain data (networks, spiking ..) with purely molecular model

* noise and low copy number (http://www.princeton.edu/~wbialek/our_papers/bialek+setayeshgar_08.pdf); this is quite interesting as for sufficiency of binding events -> http://www.princeton.edu/~wbialek/our_papers/bialek+ranganathan_07.pdf

* spontaneous flipping of interactions (phosphorylations and others) between proteins, described by the god of non-linear dynamics (S. Strogatz)

----------------------------------------------------------------------------------
* THURSDAY 11/06/2015 : http://doodle.com/se23ibwtkrkccs4s

----------------------------------------------------------------------------------
Some refs:
*https://www.dropbox.com/sh/g080w60pt2vgi7v/AADUy7Ge-J-oSYkyTeYOo7V8a?dl=0)
*http://www.pps.univ-paris-diderot.fr/~jkrivine/homepage/Teaching.html

==Ecology non-working group (williamkurtischang at gee-mail dot com)==
[[Informal ecology interest group]].

==Making Supply Chains Resilient to Disasters (mh2905 att columbia dott edu)==
'''Key words''': resilience, natural hazards, supply chains, interdependency, interconnected risks, cascading failures

'''Rational''': I am interested in examining what kinds of network structures and features contribute to increasing resilience of supply chains to natural disasters. I believe this area of work is important because regional disasters negatively impact the global economy through disruptions in supply chain networks. The pioneer study published in Nature urges the need for making supply chains climate-smart (Levermann 2014). Also in the industry, the World Economic Forum published a report to address this issue in 2013. Few researches, however, assess and model the impacts of adverse weather on supply chains. I would like to evaluate the impacts based on modeling.

'''Data set''': Supply chains data of a multinational manufacturing company.Global climate data, disaster data, etc.

'''Possible techniques''': Agent-Based Model, Complexity science, network theory and evolution, complex adaptive systems, GIS, operations research, manufacturing engineering, and I am open to any techniques.

'''Myself''': As I joined the program yesterday, please find my background here.
http://tuvalu.santafe.edu/events/workshops/index.php/Masahiko_Haraguchi

'''Please put your names below if you want to be informed about this project by email'''

'''Interested''':
Masa Haraguchi

==From Ethnic Diversity to Religious Zeal: Retrospective/Predictive Construction of the World Ethnic Map (vdzutsati@asu.edu)==

There is a new dataset on ethnic groups (2014), which claims to include all ethnic groups of the world http://www.cidcm.umd.edu/mar/amar_project.asp. There are also several cross national datasets on religion and other variables of interest, normally, socio-economic http://www.thearda.com/Archive/CrossNational.asp. Coming from social sciences I thought it would be really interesting to apply methods and perspectives from other disciplines to social science data.

One idea that I have in mind is to explore how ethnicity and religion interact.

Quick empirical checks indicate that Subsaharan Africa is home to about 1,500 ethnic and subethnic groups, in comparison to about 90 ethnic groups in the Middle East and North Africa. India alone accounts for nearly 2,000 ethnic and subethnic groups, while Europe, including all the countries of the former USSR and large immigrant groups from other parts of the world, has only about 260 ethnic groups. The picture that emerges from this simple comparison is that the spread of Abrahamic religions appears to be associated with the high depletion rate of ethnic groups. The exception of China, which has little more than 50 ethnic groups, can be explained by the country’s long history of a centralized state.

The idea then is to assume that we have had the control group of nations that did not experience the influx of Abrahamic religions (or more precisely received limited exposure to them) and the experimental group that was exposed to the spread of Abrahamic religions. Since we know what the distribution of ethnic groups in the control group is we can project what the distribution of ethnic groups in the experimental group would be, if the group were not exposed to Abrahamic religions.

Of course, we could go in the other direction too and make a projection about future – what would happen to ethnic groups if all of them adopted an Abrahamic religion.

One of the challenges in this project is absence of a dynamical system, I.e. We don’t have data on how these things changed over time, but I still think that projection about the future and the past would be kinda cool:)

'''Interested''':

==Nationalist vs religious rebel violence (vdzutsati@asu.edu)==

I would like to propose yet another possible project. This time it’s on rebel violence. I have a large dataset on rebel (and government) violence (~1 million observations). The paper that used this data was published couple of months ago (Islamists and Nationalists: Rebel Motivation and Counterinsurgency in Russia’s North Caucasus). The dataset contains event counts, GIS data, demographic and economic characteristics and some other stuff. The primary hunch of the paper was to discover the differences between nationalist and Islamist rebels.

Given all the cool methods we are learning here, my first instinct is to use the methods and tools (TISEAN?) to explore and discover whether nationalist violence is substantively different from religious violence and try to answer why.

One problem that I can see is that the event coding was done by a machine, so this may have “contaminated” the data. But if both religious and nationalist data were contaminated equally then the difference should still be evident.

'''Interested''':

==[[Archived Projects ]]==

Complex Systems Summer School 2015-Projects & Working Groups

2015-07-02T19:57:35Z

Rbarnes: /* Richard's Current Table */

{{Complex Systems Summer School 2015}}

==[[Reservations for Evans Science rm. 215]]==

Click on the title link to reserve Evans Science rm. 215 for group usage.

==Californian Drought Model==
Problem definition:
Water is an ongoing problem in California. Although corrective measures are taken for some years, periods of droughts are depleting water ground levels to not sustainable levels.
Aims:
A Netlogo simulation on how likely Californian agents evolve with regards to drought. The ABM could be used to explore the potential impacts of forms of regulation.
Outcomes:
Create an exploratory agent based model based on real data, enabling the possibility of simulating different methods of regulation, including feedbacks between the economic and hydrologic systems.

Extension 1: We will conduct a network analysis of Twitter data (hashtags #drought #California) to explore the social dynamics and information flows between stakeholders.

Extension 2: Phase-space reconstruction of groundwater level time series data, comparing the dynamics in different parts of the Central Valley.

==City Resilience==
'''Summary:''' This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities. 
'''Contact:''' Richard Barnes (rbarnes@umn.edu) 
'''Participants:''' Alex Tejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang, Martina Steffen, Juan Carlos Castilla, Brent Schneeman 
'''Wiki page:''' [[Resilience of Cities]]

==Complex Adaptive Systems and the Narrative approach: transdisciplinary methodologies for Complexity Science==
The narrative approach and it causality is different from causality in logico-scientific approaches. Making bridges among methodologies transfers knowledge, encourages important questions and switches philosophical paradigms. Formal frameworks of C(A)S can realy be complemented with the narrative, and actually should. The narrative approaches in Complexity Science are an emerging trend for fund-granting, and is really up to date to process documentary and speech-based data, reveal hidden meanings, distinguish causes from effects in overlapping systems of realms - "at the edge" of technology, social, economic, scientific, legal and other domains. The role of time in coding, intuitively generated conditional rules and computational paths. Combinations of C(A)S and the narrative is combination of quantitative and qualitative data and thinking in original - out of convertation and information loss. Synthesis of sciences is what it is about.

'''Questions''':
How to combine CS approaches (in particular CAS) and the narrative ones in a rule-based way? What to start from? What vizualizations there can be designed and used? Other questions at your descrete.

'''Some references''':
- Non-Equilibrium Social Science in ICT and Economics, CORDIS, EU: http://cordis.europa.eu/project/rcn/102344_en.html, http://www.nessnet.eu/

- Combining Complexity Theory with Narrative Research: https://youtu.be/pHjeFFGug1Y

- Haridimos Tsoukas and Mary Jo Hatch (2001), "Complex thinking, complex practice: The case for a narrative approach to organizational complexity: http://www.brown.uk.com/teaching/qualitativepostgrad/tsoukas.pdf

- David Christian, "Big History", Astrophysics, Chemistry, Biology, Information, emergence of life, technology: https://youtu.be/GlmvFjFceD8

'''Contact''': Anna (annza944@gmail.com)

Meet on Monday over lunch

'''Interested''': Marie Pierre, Jeroen, Jim, Melissa

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
 
'Carolina'
 
'Alice'
 
'Federico'
 
'Jean Gab'
 
'Sola'
 
'Ilaria'
 
'Daniel T'
 
'Sahil Garg

==Ebola==
The 2014-15 Ebola virus disease (EVD) outbreak in West Africa presented both unique opportunities and unique challenges to the epidemiological modeling community. For the first time during an emerging infectious disease outbreak, high resolution data--from a variety of sources--were made available to the academic community and many public health decision makers genuinely engaged with mathematical and computational modelers. However, the popular and scientific press were highly critical of most models ability to project the outbreak's course. The following key and open questions seem ripe for investigation using a complex adaptive systems lens:
 
1) What features of EVD transmission are most problematic for reliable, robust forecasting: changing behavior, intervention, viral evolution, complex social networks, etc?
 
2) How/can we use digital data to either improve forecasts or inform model selection?
 
3) Can one quantify the value of additional information in real-time?
 
'''Contact:''' Samuel Scarpino, SFI Omidyar Fellow, Santa Fe Institute - scarpino@santafe.edu
 
 
'''Interested:'''
 
Marie-Pierre Hasne 
Chris Verzijl 
Junming Huang 
'Sola Omoju 
Christine Harvey 
Daniel Citron (dtc65@cornell.edu) 
William Chang (williamkurtischang at gee-mail)

==Effect of landscape topography on vegetation connectivity and navigability==
''Context''
 
Landscape topography influences the dynamics of the processes that take place on it. Evolution of ecosystems networks, river networks, vegetation cover type, microclimate cycles are all interlinked deeply to the local landscape topography.

In addition to the landscape these networks are also influenced by each other conditioning the emergence and stability of ecosystems, and subsequently the behaviour of agents in the ecosystem like migration pathways of animal herds, human settlement patterns etc.

Connectivity patterns emerge from the interaction of these processes, and thus a better understanding and quantification of those patterns is critical to understanding the dynamics of the system.

 
''How do we want to approach the problem''
 
In this project, we will randomly generate landscape topography and subsequent vegetation cover from a set of parameters from known geological and biological processes. The generated data set will then be used to investigate the following questions:
 
(1) What is the connectivity of landscape patterns that emerge at different scales using different techniques such as clustering analysis, percolation theory or network theory, and can we quantify them ?
 
(2) What is the navigability of biological agents (e.g animals, humans, robots!) under such landscape patterns. We can compare mobility trails in existing landscapes to validate our hypothesis. We propose to use the tools like ABMs to simulate and characterise mobility success.
 
(3) We further aim to compare the measures of navigability with the metrics of connectivity, establishing a framework of comparison.
 

''Contact:''
 tirtha.bandy@csiro.au
 alej.tejedor@gmail.com

''Meetings''
(1) Wednesday 1pm Coffee shop

People Interested 
Martina

==[[Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe]]==
Click on the link to go to the project page for meetings, project details, and progress.
 
contact: mveitzel@ucsc.edu
 

==Mapping Complexity/Human Knowledge as a Complex Adaptive System‏==
''Context''
 
Ants leave pheromone trail patterns which they are aware of only in a local sense. They do not have the cognitive faculties to step back and look at the trails and grasp the ant-trail network as a totality. Also, the artifacts they leave behind are physical entities which then provides the aggregate feedback to the aggregate ant body to then feed the evolution of the ant body as a CAS system. In contrast, humans do have the requisite cognitive abilities. The "pheromone trails" we leave behind are the knowledge trails coded in symbolic knowledge artifacts. In contrast to the physical artifacts that ants leave behind, the knowledge artifacts that we leave behind are far more flexible and potent, both at the aggregate as well as at the individual levels. But like the ants, until recently, we did not have the means to step back and map the knowledge "pheromone trails" to obtain the big picture and its global/local dynamics. The burgeoning field of scientometrics is making available visualization tools to help us map and study the evolutionary dynamics of the knowledge network structures.
 
''Data and Questions''
 
The goals of this project include
<ol>
<li>Extract the terms from approximately 1600 working papers published by SFI</li>
<li>Map the intra/inter conceptual network structures</li>
<li>Study the evolution of these structures across time</li>
<li>High-light the gap-closure of knowledge reverse-salients (if any)</li>
<li>Capture any of the network patterns that repeat</li>
<li>Study the diffusion of concepts across the network</li>
<li>Provide visualization tools for navigating the complexity corpus, etc</li>
</ol>
 
''Possible methods''
 
Latent Semantic Analysis (LSA) and Latent Document Analysis (LDA)
 
References:
<ol>
<li> SFI Working Papers: http://www.santafe.edu/research/working-papers/</li>
<li> Atlas-Science-Visualizing: http://www.amazon.com/Atlas-Science-Visualizing-What-Know/dp/0262014459</li>
<li> Atlas-Science-Visualizing WebSite: http://scimaps.org/atlas</li>
<li> Mapping-Scientific-Frontiers-Knowledge-Visualization: http://www.amazon.com/Mapping-Scientific-Frontiers-Knowledge-Visualization/dp/1447151275</li>
<li> Katy Börner presents at Science of Science: https://www.youtube.com/watch?v=pzCqGBNzomE</li>
<li> Scholarly Data, Network Science, and (Google) Maps:https://www.youtube.com/watch?v=vos5QBDywMM</li>
<li> LSA Video Lect: http://videolectures.net/slsfs05_hofmann_lsvm/</li>
<li> What is LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf</li>
<li> LSA Wiki:http://en.wikipedia.org/wiki/Latent_semantic_analysis</li>
<li> LDA: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm</li>
<li> Fusari, A. (2014). Methodological Misconceptions in the Social Sciences: Rethinking Social Thought and Social Processes: http://www.springer.com/us/book/9789401786744 </li>
<li> Kirsh, D. (2013). Thinking with external representations. Cognition Beyond the Brain: http://adrenaline.ucsd.edu/kirsh/Articles/Interaction/thinkingexternalrepresentations.pdf </li>
<li> Holland, J. H. (1992). Adaptation in natural and artificial systems: https://mitpress.mit.edu/index.php?q=books/adaptation-natural-and-artificial-systems </li>
<li> Where to start with text mining. http://tedunderwood.com/2012/08/14/where-to-start-with-text-mining/ </li>
<li> Singular Value Decomposition Tutorial https://www.ling.ohio-state.edu/~kbaker/pubs/Singular_Value_Decomposition_Tutorial.pdf </li>
<li> Latent Semantic Analysis (LSA) Tutorial: http://www.puffinwarellc.com/index.php/news-and-articles/articles/33-latent-semantic-analysis-tutorial.html </li>
<li> LSA in Detail: http://www2.denizyuret.com/ref/berry/berry95using.pdf </li>
<li> Web Based LSA: http://webapp1.dlib.indiana.edu/newton/lsa/index.php </li>
<li> Another Technique: t-Distributed Stochastic Neighbor Embedding (t-SNE): http://lvdmaaten.github.io/tsne/ </li>
<li> tf–idf: https://en.wikipedia.org/wiki/Tf%E2%80%93idf </li>

</ol>
 
'''Interested:'''
 
<ul>
<li> John Thomas (radjohn@live.com) </li>
<li> Haitao Shang (hts@mit.edu) </li>
<li> Christopher Verzijl (cjo.verzijl@gmail.com) </li>
<li> Anna Zaytseva (annza944@gmail.com) </li>
<li> Penny Mealy (penny.mealy@gmail.com) </li>
</ul>
 
 

==Navigating Music, Brain and The Edge of Chaos ‏==
''Context'' 
[[Navigating Music, Brain and The Edge of Chaos]]
 
Brain Sciences have revealed that we/it lives "on the edge of chaos" exhibiting "self-organized criticality" that is tentatively balanced between normalcy and madness. Over the course of history, humans have used various agents and activities to shape, influence and control this living-chaos ranging from substances such as caffeine, sugar, drugs etc., to activities such as the arts (including music), social-discourse/therapy, meditation etc. Of these, music has a distinct role in shaping our moods and helping us transition between different mental states, as well as maintain it for extended periods of time. Clearly we have been using music to help us control and shape the internal chaos. But until recently, the quantitative instrumentation of this massively complex system that comprises of close to a 100-billion neurons networked into a 1000-trillion synaptic edifice has not been available to the common man. But of late, affordable, wearable EEG's are available on the market, thus making the quantitative study of the influence of music in brain dynamics feasible on a large-scale/crowd-sourcing sense. To help come to terms with the complexity of our 1000-trillion synaptic edifice, we need to gather data on a vast scale. The proposed research is a proof-of-concept, exploratory foray into making this happen.
 

History of music in 5 min
https://www.youtube.com/watch?v=Gt2zubHcER4

'''Interested:'''
 
<ul>
<li> Sara Lumbreras (sara.lumbreras@gmail.com) </li>
<li> Ilaria b (bertazzi.ilaria@alice.it) </li>
<li> Braun, Urs (urs.braun@zi-mannheim.de) </li>
<li> John Thomas (radjohn@live.com) </li>
<li> Glenn Magerman (glenn.magerman@kuleuven.be) </li>
<li> Daniel Friedman (DanielAriFriedman@gmail.com)</li>
</ul>
 
 

==Powerlaw fitting and alternative distributions - Theory/statistics ==
Clauset, Shalizi and Newman (2009) propose a maximum likelihood method to estimate the powerlaw exponent of a variable of interest. This is a great improvement on earlier methods such as OLS that dominated the literature up to then. However, one can fit a powerlaw to any dataset and the most we can say is that our observations are consistent with the hypothesis that x is drawn from a powerlaw distribution. One easily implementable method to compare the powerlaw fit to other fits is then a likelihood ratio test for both models.

One particular discussion is the distinction between a powerlaw (PL) and a log-normal (LN) fit. For an avid discussion between both fits on city size and Zipf's law, see Eeckhout (2004, 2009) and Levy (2009), where the discussion now settled on city sizes following a log-normal distribution instead of a powerlaw. Similarly for the discussion on firm size distribution: Simon and Bonini, 1958; Ijri and Simon, 1977; Stanley et al., 1995; Sutton, 1997; Axtell, 2001; Okuyama et al., 1999; Cabral and Mata, 2003; Gaffeo et al., 2003; Aoyama et al., 2004; Fujiwara et al., 2004a,b; Kaizoji et al., 2006; Takayasu et al., 2008; Duchin and Levy, 2008; Schwarzkopf and Farmer, 2008, ...

This distinction matters for several reasons:
- PL and LN come from very similar model and differences in initial conditions can lead to very different outcomes. PL exhibits a choice for x_min, below which the unit of observation is not feasible to exist (eg minimum city size, firm size, word length, ...). LN has no minimum size.
- PL and LN that look similar are the difference between infinite (PL) and large but finite variance (LN)
- shock propagation: when unit-level shocks are large enough to show aggregate perturbances if the distribution is powerlaw with infinite variance, while these shocks wash out fast when the distribution is log-normal. (Gabaix, 1999; Gabaix 2009; Acemoglu et al. 2012, ...).

I have encountered some issues which I would like to explore further:
1. The distinction between lognormal and powerlaw in the data is very sensitive to data truncation: in the above discussions, researchers have slightly different datasets, covering more or less of the population at hand. Left-truncation (i.e. observations not in the dataset because they are too small to be reported) can strongly drive the outcome of the fit, even when endogenizing the x_min cutoff. I have data on the universe of Belgian firms, much more complete than e.g. US Census data, where I have done some preliminary tests on this. The question is then: how to formalise this distinction and what are the theoretical and practical caveats to look out for when applying this method.
2. MLE fitting seems to be sensitive to the choice of units as well: rescaling a variable by a factor 1000, 1000000 etc seems to influence the endogenous x_min choice and hence the estimated parameter. This reminds me of some work on scaling invariance in negative binomial estimators. What is going on here?
3. Can we set up a model that generalises both? I've been looking at Levy stable distributions, but did not do anything with it yet.

Interested people:
* Corbain
* Laura
*Binyang
*Sahil
*Tirtha
*Glenn
*Junming

Literature:
Powerlaw fitting in empirical data - Clauset, Shalizi and Newman (2009): http://arxiv.org/abs/0706.1062

==Organ Transplant Analysis==
Over 120,000 people in the United States are currently on the waiting list for an organ transplant. The size of this waiting list relates to over 6,000 deaths a year while waiting for a transplant and tens of billions of dollars in government spending. I have access to the following data sets:
 
* All transplants performed in the US from October 1987 to June 2014 (including follow-up data)
* All living and deceased organ donors in the same time period (with follow-up data on living donors)
* Waiting list data for everyone who signed up for the list
* 2012 National Survey on Attitudes and Behaviors on Organ Donation
* Social media data relating to organ donation since 2008

Open to ideas and suggestions for the topic, there are a lot of interesting questions to investigate including cultural/racial/gender differences in organ donation. I have several preliminary reports and exploratory analysis done on differences in donors.

Second Meeting Thursday, June 11th 4:15 in the coffee shop!

===Matching Game===
Rules for the matching game:

Objective: Create the largest chain possible using blood types in the room.

Follow the blood type guidelines for donation to form a donor chain.
*Assume that self-matches are not possible, for example someone with a donor of type AB and a recipient of type AB can not match themselves and needs to find someone else to complete their chain.
*Create a donor chain to help as many people as possible.
*Post results below for the prize
*There will be several "good samaritan" donors which will donate and need nothing in return, this is a great way to start your chain. Using a good samaritan chain also means you don't need a donor for the end point.

====Game Workspace====
Use this workspace to post for and find matches!

==== Donor Chains ====
Post your chain here. Example is given below:

=====Example Chain 1=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| A
|-
| Laura
| AB
| B
|-
| Matt
| A
| AB
|}

=====Example Chain 2=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| None
|-
| Laura
| A
| B
|-
| Matt
| None
| A
|}

======Richard's Current Table======

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Maggie
| O
| X
|-
| Jakub
| O
| A
|-
| MatthIn
| AB
| O
|-
| Alejandro
| O
| AB
|-
| James
| AB
| O
|-
| Susanne
| A
| AB
|-
| Michael
| O
| A
|-
| Alice
| AB
| O
|-
| Vanessa
| A
| AB
|-
| Urs
| B
| A
|-
| Melissa
| A
| B
|-
| Chris
| O
| A
|-
| Carolina
| O
| A
|-
| Havier
| AB
| O
|-
| Brent
| O
| AB
|-
| Masa
| A
| O
|-
| AndySchauf
| A
| A
|-
| Maria
| O
| A
|-
| Kleber
| O
| A
|-
| Jeroen
| O
| O
|-
| Martina
| A
| O
|-
| Richard
| A
| A
|-
| Glenn
| O
| A
|-
| Sam
| O
| B
|-
| Cobain
| B
| A
|-
| Charon
| B
| B
|-
| Nilton
| O
| B
|-
| MariePierre
| A
| O
|-
| Jae
| B
| A
|-
| Laurence
| O
| B
|-
| Keith
| AB
| O
|-
|}

Unused:

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| X
|-
| Jelle
| AB
| X
|-
| JGab
| O
| X
|-
| LauraCondon
| O
| X
|-
| Matthew
| O
| X
|-
| MatthewO
| AB
| O
|-
|}

==Multi-dimensional social networks in the evolution, development and resilience of informal economies. ==
Informal economies are defined as economic activities that occur outside the purview of corporate public and private institutions. These types of economies proliferate where traditional economic actors are unable to productively exercise their activities, especially due to costly constraints (De Soto 2003). Firms may face adverse incentives to expand production by hiring more workers or incorporating more capital due to the low productivity of their workers or the predatory practices of rapacious elites or corrupt governments. Labor itself, i.e. people, may face hurdles in trying to make the jump towards entrepreneurial activities or jobs in the formal economy which allow the accumulation of experience, retirement savings, access to insurance or precautionary savings to face unexpected events (like disease, disability, etc.) due to lack of capital access (whether human and financial).

'''For this project, we propose a different interpretation: We seek to understand and model how multi-faceted social networks provide robust alternatives to formal economies, which far from being seen as degenerate forms of social organization, in many instances co-exist, challenge and compete with formal employment and economic activities. While some types of informal economies operate under adverse contexts, their resiliency may be understood as a type of adaptive fitness and not the mere result of stubborn cultural path-dependency. '''

For the most part, informal economies have been analysed as counterpoint to an ideal type of a formal economy with low levels of trust, respect of property rights, access to credit and public institutions - like the court systems (see Losby et al. 2002 for a review). However, informal economies have been coupled with their formal counterparts since the development of capitalism in the 19th Century. To name a famous historical example, Old London's East End's informal sector was described harshly by Engels (1844) in his famous tract, "The Condition of the Working Class in England". But almost fifty years later, in a new preface to the book, Engels recognized the progress that took place there under the aegis of working class organizations in the area.

Research has begun to catch on to this idea – developing a literature in the areas of informal risk sharing and remittances, which are in some sense informal counterparts to insurance and banking. Consumption patterns in poor, rural villages are remarkably smooth suggesting that risk-sharing measures are prevalent, although imperfect (Townsend). Field studies of networks underlying village risk sharing systems have found that households primarily receive help from existing social connections, such as friends and relatives, in the form of informal loans or transfers. Theoretical work in network economics, by authors such as Matt Jackson, found that certain empirically prevalent network structures may directly benefit the stability of favor exchange systems. Another line of inquiry focuses on remittance income, generally informal transfers across kinship ties. World Bank researchers have found that remittances from overseas migrants respond dramatically to regional income shocks, replacing upwards of 60% of lost income in households with international migrants. Further studies, cited below, have generalized those results. Furthermore, reductions in the cost of sending remittances, which occurs with the advent of mobile money, further mitigates exposure to risk in receiving households.

Also recently, authors have written on how communities come together in situations where formal economies are unavailable by external shocks (like disasters) or due to lack of access (due to conditions of abject poverty). In the case of the latter, Venkatesh (2008) provided gripping testimonies of how informal economies arose and developed in the South Side of Chicago in low trust environments via cash transactions and informal service contracts enforced by (and for the benefit of) local criminal gangs. These gangs were seen, surprisingly, as alternative coordinating mechanisms to settle claims between neighbors. In the case of the former, Storr and Grube (2013) in a series of papers has argued how "shared histories and perspectives, and the stability of social networks within the community" allows communities who suffered disasters to cope and endogenously resolve immediate and complex problems. Providing an example of these social networks in action, research has found that remittances flow quickly into areas affected by natural disasters when there is a technology in place for it to happen.

Taking these lines of inquiry together, the findings suggest that informal handling of risk takes place on a large scale and that social networks informally connect communities in ways that impact their economies. Recent trends suggest that the interaction of formal and informal economic processes is growing on a nationwide scale. First, the aggregate value of remittance flows is large and growing, nearing the value of foreign direct investment. Second, advancements that formalize previously informal transactions are expanding dramatically in emerging economies through various forms of branchless banking. National economies may be embedded in profoundly influential informal systems that have never been holistically studied.

On a more general note, this project touches lingering questions in economics. For example, some might argue that more stringent labor regulations should have increased informal employment, but in fact the opposite happened. And while the economic rationale for the former statement remains valid, we suggest that unions, as other types of social organizations, as examples of ways whereas people interact via organized networks, provide richer dimensions than those suggested by their interaction as mere economic agents. Hence, unions, as well as other types of faith-based, ethnic, community and other interest groups may provide ways of interaction that escape narrow economic outcomes.

====References====

• De Soto, Hernando (2000/2003) The Mystery of Capital. Basic Books.
• Losby, Jan; Else, John; Kingslow, Marcia; Edgcomb, Elaine; Malm, Erika and Vivian Kao (2002) The Informal Economy: A Literature Review. ISED Consulting and Research and the Aspen Institute Working Paper. http://www.kingslow-assoc.com/images/Informal_Economy_Lit_Review.pdf
• Townsend, Robert M. "Risk and Insurance in Village India." (1994)
• Fafchamps, Marcel, and Susan Lund. "Risk-sharing Networks in Rural Philippines." (2003)
• Weerdt, Joachim De, and Stefan Dercon. "Risk-sharing Networks and Insurance against Illness." (2006)
• Matthew O. Jackson, Tomas Rodriguez-Barraquer and Xu Tan. “Social Capital and Social Quilts: Network Patterns of Favor Exchange.” (2012)
• Yang D and H Choi."Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines"(2007)
• Kurosaki, Takashi. "Consumption vulnerability to risk in rural Pakistan." (2006)
• Jack, W, and T. Suri. "Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution” (2014)
• Engels, Friedrich (1844/1892) The Condition of the Working Class in England. http://www.gutenberg.org/files/17306/17306-h/17306-h.htm
• Venkatesh, Sudhir (2008) Gang Leader for a Day: A Rogue Sociologist Takes to the Streets. Penguin Books.
• Storr, Virgil and Laura Grube (2013) The Capacity for Self-Governance and Post-Disaster Resiliency. George Mason University, Department of Economics Working Paper 13-37. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2350830##
• Blumenstock, Joshua Evan and Fafchamps, Marcel and Eagle, Nathan. “Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda” (2011).
• Ratha, Dilip. "Workers’ remittances: an important and stable source of external development finance." (2005).
• Pénicaud, Claire, and Arunjay Katakam. State of the Industry 2013: Mobile Financial Services for the Unbanked. Rep. N.p.: GSMA MMU, 2013.

If interested, please list your name below.

'''Interested:'''
<li> Eloy Fisher (eloy.fisher@fulbrightmail.org) </li>
<li> Carolina Mattsson (carromattsson@gmail.com) </li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>

<li> Jakub Rojcek (jakub.rojcek@gmail.com) </li>

==Exploring Community Formation through Analysis of Scholarly Corpus (ArXiv)==

The Arxiv [http://arxiv.org/] is a free online repository of scientific preprint articles, mostly from physics and mathematics. It currently contains over 800,000 articles, dating back to 1991. Currently, a lot of the data associated with this repository is completely available: paper submission dates; full texts of papers; author names and coauthorship information. Additionally, there is some citations data available. (If necessary, I can also find papers' subdiscipline labels; submitting authors' email address domain names; other things.)

In the past I have been using these data to try and explore how communities of authors who have shared interests grow over time. For example, we can pinpoint the first ever paper about topological insulators, and search for all subsequent papers on that topic. As more papers are written, authors begin to join the field of research and to form strong ties by collaborating with one another. We can use the ArXiv data to visualize and analyze exactly how these communities form and grow.

===Brainstorming notes===
Isolation between topics, crossing interdisciplinary boundaries, finding (topological) separation distance between disciplines or research groups

Tipping points - can we look for separation of or mergers of two groups or disciplines?

Can we identify key papers (or groups of papers) that initiate ties between fields.

Sentiment analysis: can we identify when a paper's citation is an endorsement or a refutation?

Tools for Analysis: Change point detection; relational event models

Can we find more comprehensive/better citation data?

Lucene and indexing tools:
- Can we re-index our text database after removing stop words?
- Can we index the titles and abstracts?
- Elastic Search - tool for interacting with Lucene

This set of text-processing [http://alias-i.com/lingpipe/demos/tutorial/read-me.html tutorials] is pretty handy for background info and inspiration. The software package doesn't simply plug-into a Lucene/Solr/Elastic Search index, but it could be done. This package from [http://nlp.stanford.edu/software/ Standford] seems very capable.

===Next meeting===
Thursday @ 9AM in Coffee Shop

== PolComplex - Epistemic communities and policy dynamics in the UK Parliament ==

Over the last twenty years, there has been extensive debate about what the core topics in public policy are, and if they change according to type and number. Furthermore, it is still not clear how policy topics mutate and diffuse over time. Human-based text analysis of governmental documents has shed only some light on these research questions: 21 general topics have been proposed, while other accounts tend to restrict it to about five main clusters. Yet, this approach has not been able to devise a reliable and systemic method to capture topic dynamics, diffusion, and their relation to the human actors that talk about them.

This project is an extension of an exploratory research that intends to overcome such limitations, through the application of natural language processing (more specifically, dynamic topic modeling and sentiment analysis), topological and network analysis on a dataset containing UK House of Commons debates ranging from 1975 to 2014. The aim is manyfold. We hope to identify the structure and dynamics of epistemic policy communities - i.e. of political actors revolving around similar policy interest -, and how these relate to factors such as party membership, seniority, relevant historical events.

Some references:

[http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.734168 Taddy (2013), "Multinomial Inverse Regression for Text Analysis"] 

[https://www.cs.princeton.edu/~blei/papers/BleiLafferty2007.pdf Blei and Lafferty (2007), "A Correlated Topic Model of Science"] 

== Genomic variations from a chaotic mapping ==

Following Dabby CHAOS 6 (2), 1996 Musical variations from a chaotic mapping, this project will explore genomic variations in key metabolic genes that are known to be widely spread across bacterial phylogeny (e.g. the Nif cluster which is used in nitrogen fixation). There are several ways that genomic data can be treated as "music":

1) Nucleotide: 4 notes - A, G, C, T 
2) Amino Acids: 20 notes 
3) codon triplets: 64 notes 
4) tetra nucleotides: 256 notes. 

One possible goal is to take a seed sequence from some random organism and generate new variations. Then using homology search (e.g. BLAST) or phylogenetics, determine if that variation exists in nature? If not, perhaps model the potential protein folding (if possible?) to determine if that variation could exist in nature. This is VERY preliminary and can go in many directions.

If interested please list name below or contact Jarrod at jscott@bigelow.org

Reading
http://digitalcommons.olin.edu/cgi/viewcontent.cgi?article=1000&context=ahse_pub&sei-redir=1&referer=https%3A%2F%2Fscholar.google.com%2Fscholar%3Fhl%3Den%26q%3Ddabby%2Bchaos%26btnG%3D%26as_sdt%3D1%252C32%26as_sdtp%3D#search=%22dabby%20chaos%22

== Free Energy Theory ==
The Free Energy (FE) minimisation framework tries to explain how biological systems (such as a cell or a brain) self-organise in order to occupy the (often very limited number of) non-equilibrium states that minimise free energy. This is also known as active inference. A simple corollary of active inference is that agents behave as to minimise their prediction error, or the difference between prediction and reality. Thermodynamic free energy is a measure of energy available in a system to do useful work. This can be framed in an information theoretic setting, as the difference between how the world is being represented and how it actually is. A better fit means a lower information-theoretic free energy, as more resources are being put to ‘good use’ in representing the world. The overarching logic of FE theory is that a better model of the world help maintain structure and organisation, which ultimately helps the system resist increases in entropy.
 
 
This is not a set-in-stone project with any concrete aim (yet). We are a few people interested in exploring the theoretical and practical implications of these ideas, and you're more than welcome to join in!
 
 
'''Interested:'''
 
<ul>
<li> Jelle Bruineberg (j.bruineberg@gmail.com ) </li>
<li> Tobias Morville (tobiasmorville@gmail.com) </li>
<li> Susanne Pettersson (guspsusa85@student.edu.se) </li>
<li> Maggie Simon (margaret.w.simon@gmail.com) </li>
<li> Martina Steffen (martinasemail@gmail.com) </li>
<li> Sahil (sahilgar@usc.edu) </li>
<li> Tirtha (Tirtha.Bandy@csiro.au) </li>
<li> Anna (annza944@gmail.com) </li>
<li> Matt O (mmosmond@gmail.com) </li>
<li> Will Chang (williamkurtischang at gmail dot com) </li>
<li> Cobain (mrdc1g10@soton.ac.uk) </li>
</ul>
Reading:
 
http://rsif.royalsocietypublishing.org/content/10/86/20130475 
http://arxiv.org/abs/1503.04187 
http://www.mdpi.com/1099-4300/15/1/311 

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
'Carolina'
'Alice'
'Federico'
'Jean Gab'
'Sola'
'Ilaria'
'Daniel T'

==Comparison of Network vs Scaling Theory based Models in Ecology (Cobain - mrdc1g10@soton.ac.uk)==

One of the biggest difficulties ecologists face is trying to understand the ecosystem dynamics based on very little biological information (compared to the size of the system) - observational data is logistically difficult and can be very expensive to acquire, as well as often being very time consuming. In terms of modelling, to overcome this problem, two approaches have been utilised. 

The first is a network based approach whereby the nodes represent biomass density of a particular species (or a higher level taxanomic group and/or resources) and edges as the trophic interactions (who eats who). This method depends on what we know about the trophic behaviour of the species involved (which is often very limited and there can be many species to parameterise), and represents only the central tendency of what could potentially be very diverse behaviour within and between populations. 

The second approach is to use scaling theory to describe average trophic interactions and other biological processes based on individual organism body size along the size continuum, viewing the community as a very size structured dynamical system. This requires less specific knowledge about the organisms in the community ''per se'', however this again represents only the central tendency of a given size class of what could be very diverse behaviour. Functional differences can be potentially introduced (coupled benthic-pelagic systems for example), but to the detriment of braking down the predictability of the well known allometric scaling laws with size as specificity increases. 

There has been little to no comparison of these two methods of modelling ecological systems (at least to my knowledge) and the question arises that, given the same starting information, how well do both approaches model the dynamics of an ecosystem, given the limited biological information we have? Is one approach better at capturing energy flow through the system / community structure and stability etc. compared to the other? If the models vary then such information will better inform ecologists on which to use depending on what type of questions they are trying to answer. 

Therefore, the project proposed here aims to investigate these two methods. The community that is used to seed an experimental mesocosm setup (mesocosms are large tank set-ups designed to reflect the complexity of natural ecosystems but still being able to be artificially controlled and are still relatively simple), will be input into two models based on the separate approaches and then run to steady state. The model outputs will then be compared and contrasted with eachother as well as the mesocosm community at steady state. Further work may include examining perturbation dynamics, dependent on what data we have available.

'''Interested: Name / Email''' 
Martina Steffen (martinasemail@gmail.com)

==Dynamics of homicide (Matthew Ingram - mingram@albany.edu)==

Integrating temporal, spatial, and multi-level concepts

1:30pm in the coffee shop

Am interested in the discussion on this project...Sola Omoju

Interested - Nilton Cardoso

==Multiplex Adaptive Networks (Daniel - dtc65@cornell.edu)==

7 pm in the coffee shop

==Modeling brain diseases (or cancerous bio pathways) (Sahil - sahilgar@usc.edu)==
Interested people can put a meeting time as per their convenience here.
 
No meeting time indicated
 
One potential meeting time can be 3pm in coffee shop ?
 
Information theoretic algorithms can also be explored for the problem.
 
Discovering Structure in High-Dimensional Data Through Correlation Explanation. http://papers.nips.cc/paper/5580-positive-curvature-and-hamiltonian-monte-carlo
 
Maximally Informative Hierarchical Representations
of High-Dimensional Data. http://jmlr.org/proceedings/papers/v38/versteeg15.pdf

 
'''Interested:'''
 
<ul>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> Laura Condon (lcondon@mymail.mines.edu)</li>
</ul>
 
 

==Analysis of UK parliament speeches 1935-2014 (Stefano - etstefano@gmail.com)==

No meeting time indicated

==Mapping Complexity/Human Knowledge as a Complex Adaptive System (radjohn@live.com)==
2pm/Wed 6/10 in Conference Room (Tentative)

==Navigating Music, Brain and The Edge of Chaos (radjohn@live.com)==
10:45am/Wed 6/10 in Conference Room
9:00am/Thu 7/10 at the Coffee Shop

'''Interested:'''
Sahil Garg

==Analysis of rule-based modeling for dopamine-dependent synaptic plasticity (emilia.m.wysocka@gmail.com)==

----------------------------------------------------------------------------------
Modeling and analysis of phenomenons and evolution of stochastic, combinatorially complex signalling systems in a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system).

----------------------------------------------------------------------------------
Rule-based modeling features:

*biological systems as concurrent processes
*dynamics of post-translational modifications
*domain availability
*competitive binding
*causality and intrinsic structure
*binding sites
*interaction rules replace reaction equations
*infinite number of reactions with a small and finite number of rules
*reduction of parameter space
*“don't care don't write” - adjustable rule contextualization
*single reaction rule and parameters generalize classes of multiple rules
*modular and extensible language
*specification language & simulation/integration environment
*static and causal analysis
*Kappa/KaSim & BioNetGen/NFsim -- specification language/network-free simulator

----------------------------------------------------------------------------------
----------------------------------------------------------------------------------
Meetings:
----------------------------------------------------------------------------------
* WEDNESDAY 11/06/2015

Addressing problems in terms of the other aspects of the projects apart from the biological questions and verification (matching results to some published experiments or known behaviour). So my plan is as follows:

* divide the group (roughly) into people who look in to biological meaning and validation and the one which tries to do the analysis of system phenomenons and evolution of stochastic, combinatorially complex signalling systems in both a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system) - all possible states, scenarios of the system abstracted from the biological meaning.

Things that could be modelled (look in dropbox folder: https://www.dropbox.com/sh/g080w60pt2vgi7v/AACHlMf2JwpP2EZqKq7Di6N2a/possible%20models?dl=0):

* to make things easier and have the modeling part done quickly- base the model on the dopamine-related synaptic plasticity (SBML ODE model): http://www.ebi.ac.uk/biomodels-main/MODEL1101170000;

* I found a series of papers suitable to the subject of the Complex Systems course, e.g.: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC344803/. I was thinking to even try to compare higher level brain data (networks, spiking ..) with purely molecular model

* noise and low copy number (http://www.princeton.edu/~wbialek/our_papers/bialek+setayeshgar_08.pdf); this is quite interesting as for sufficiency of binding events -> http://www.princeton.edu/~wbialek/our_papers/bialek+ranganathan_07.pdf

* spontaneous flipping of interactions (phosphorylations and others) between proteins, described by the god of non-linear dynamics (S. Strogatz)

----------------------------------------------------------------------------------
* THURSDAY 11/06/2015 : http://doodle.com/se23ibwtkrkccs4s

----------------------------------------------------------------------------------
Some refs:
*https://www.dropbox.com/sh/g080w60pt2vgi7v/AADUy7Ge-J-oSYkyTeYOo7V8a?dl=0)
*http://www.pps.univ-paris-diderot.fr/~jkrivine/homepage/Teaching.html

==Ecology non-working group (williamkurtischang at gee-mail dot com)==
[[Informal ecology interest group]].

==Making Supply Chains Resilient to Disasters (mh2905 att columbia dott edu)==
'''Key words''': resilience, natural hazards, supply chains, interdependency, interconnected risks, cascading failures

'''Rational''': I am interested in examining what kinds of network structures and features contribute to increasing resilience of supply chains to natural disasters. I believe this area of work is important because regional disasters negatively impact the global economy through disruptions in supply chain networks. The pioneer study published in Nature urges the need for making supply chains climate-smart (Levermann 2014). Also in the industry, the World Economic Forum published a report to address this issue in 2013. Few researches, however, assess and model the impacts of adverse weather on supply chains. I would like to evaluate the impacts based on modeling.

'''Data set''': Supply chains data of a multinational manufacturing company.Global climate data, disaster data, etc.

'''Possible techniques''': Agent-Based Model, Complexity science, network theory and evolution, complex adaptive systems, GIS, operations research, manufacturing engineering, and I am open to any techniques.

'''Myself''': As I joined the program yesterday, please find my background here.
http://tuvalu.santafe.edu/events/workshops/index.php/Masahiko_Haraguchi

'''Please put your names below if you want to be informed about this project by email'''

'''Interested''':
Masa Haraguchi

==From Ethnic Diversity to Religious Zeal: Retrospective/Predictive Construction of the World Ethnic Map (vdzutsati@asu.edu)==

There is a new dataset on ethnic groups (2014), which claims to include all ethnic groups of the world http://www.cidcm.umd.edu/mar/amar_project.asp. There are also several cross national datasets on religion and other variables of interest, normally, socio-economic http://www.thearda.com/Archive/CrossNational.asp. Coming from social sciences I thought it would be really interesting to apply methods and perspectives from other disciplines to social science data.

One idea that I have in mind is to explore how ethnicity and religion interact.

Quick empirical checks indicate that Subsaharan Africa is home to about 1,500 ethnic and subethnic groups, in comparison to about 90 ethnic groups in the Middle East and North Africa. India alone accounts for nearly 2,000 ethnic and subethnic groups, while Europe, including all the countries of the former USSR and large immigrant groups from other parts of the world, has only about 260 ethnic groups. The picture that emerges from this simple comparison is that the spread of Abrahamic religions appears to be associated with the high depletion rate of ethnic groups. The exception of China, which has little more than 50 ethnic groups, can be explained by the country’s long history of a centralized state.

The idea then is to assume that we have had the control group of nations that did not experience the influx of Abrahamic religions (or more precisely received limited exposure to them) and the experimental group that was exposed to the spread of Abrahamic religions. Since we know what the distribution of ethnic groups in the control group is we can project what the distribution of ethnic groups in the experimental group would be, if the group were not exposed to Abrahamic religions.

Of course, we could go in the other direction too and make a projection about future – what would happen to ethnic groups if all of them adopted an Abrahamic religion.

One of the challenges in this project is absence of a dynamical system, I.e. We don’t have data on how these things changed over time, but I still think that projection about the future and the past would be kinda cool:)

'''Interested''':

==Nationalist vs religious rebel violence (vdzutsati@asu.edu)==

I would like to propose yet another possible project. This time it’s on rebel violence. I have a large dataset on rebel (and government) violence (~1 million observations). The paper that used this data was published couple of months ago (Islamists and Nationalists: Rebel Motivation and Counterinsurgency in Russia’s North Caucasus). The dataset contains event counts, GIS data, demographic and economic characteristics and some other stuff. The primary hunch of the paper was to discover the differences between nationalist and Islamist rebels.

Given all the cool methods we are learning here, my first instinct is to use the methods and tools (TISEAN?) to explore and discover whether nationalist violence is substantively different from religious violence and try to answer why.

One problem that I can see is that the event coding was done by a machine, so this may have “contaminated” the data. But if both religious and nationalist data were contaminated equally then the difference should still be evident.

'''Interested''':

==[[Archived Projects ]]==

Complex Systems Summer School 2015-Projects & Working Groups

2015-07-02T19:38:37Z

Rbarnes: /* Organ Transplant Analysis */

{{Complex Systems Summer School 2015}}

==[[Reservations for Evans Science rm. 215]]==

Click on the title link to reserve Evans Science rm. 215 for group usage.

==Californian Drought Model==
Problem definition:
Water is an ongoing problem in California. Although corrective measures are taken for some years, periods of droughts are depleting water ground levels to not sustainable levels.
Aims:
A Netlogo simulation on how likely Californian agents evolve with regards to drought. The ABM could be used to explore the potential impacts of forms of regulation.
Outcomes:
Create an exploratory agent based model based on real data, enabling the possibility of simulating different methods of regulation, including feedbacks between the economic and hydrologic systems.

Extension 1: We will conduct a network analysis of Twitter data (hashtags #drought #California) to explore the social dynamics and information flows between stakeholders.

Extension 2: Phase-space reconstruction of groundwater level time series data, comparing the dynamics in different parts of the Central Valley.

==City Resilience==
'''Summary:''' This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities. 
'''Contact:''' Richard Barnes (rbarnes@umn.edu) 
'''Participants:''' Alex Tejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang, Martina Steffen, Juan Carlos Castilla, Brent Schneeman 
'''Wiki page:''' [[Resilience of Cities]]

==Complex Adaptive Systems and the Narrative approach: transdisciplinary methodologies for Complexity Science==
The narrative approach and it causality is different from causality in logico-scientific approaches. Making bridges among methodologies transfers knowledge, encourages important questions and switches philosophical paradigms. Formal frameworks of C(A)S can realy be complemented with the narrative, and actually should. The narrative approaches in Complexity Science are an emerging trend for fund-granting, and is really up to date to process documentary and speech-based data, reveal hidden meanings, distinguish causes from effects in overlapping systems of realms - "at the edge" of technology, social, economic, scientific, legal and other domains. The role of time in coding, intuitively generated conditional rules and computational paths. Combinations of C(A)S and the narrative is combination of quantitative and qualitative data and thinking in original - out of convertation and information loss. Synthesis of sciences is what it is about.

'''Questions''':
How to combine CS approaches (in particular CAS) and the narrative ones in a rule-based way? What to start from? What vizualizations there can be designed and used? Other questions at your descrete.

'''Some references''':
- Non-Equilibrium Social Science in ICT and Economics, CORDIS, EU: http://cordis.europa.eu/project/rcn/102344_en.html, http://www.nessnet.eu/

- Combining Complexity Theory with Narrative Research: https://youtu.be/pHjeFFGug1Y

- Haridimos Tsoukas and Mary Jo Hatch (2001), "Complex thinking, complex practice: The case for a narrative approach to organizational complexity: http://www.brown.uk.com/teaching/qualitativepostgrad/tsoukas.pdf

- David Christian, "Big History", Astrophysics, Chemistry, Biology, Information, emergence of life, technology: https://youtu.be/GlmvFjFceD8

'''Contact''': Anna (annza944@gmail.com)

Meet on Monday over lunch

'''Interested''': Marie Pierre, Jeroen, Jim, Melissa

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
 
'Carolina'
 
'Alice'
 
'Federico'
 
'Jean Gab'
 
'Sola'
 
'Ilaria'
 
'Daniel T'
 
'Sahil Garg

==Ebola==
The 2014-15 Ebola virus disease (EVD) outbreak in West Africa presented both unique opportunities and unique challenges to the epidemiological modeling community. For the first time during an emerging infectious disease outbreak, high resolution data--from a variety of sources--were made available to the academic community and many public health decision makers genuinely engaged with mathematical and computational modelers. However, the popular and scientific press were highly critical of most models ability to project the outbreak's course. The following key and open questions seem ripe for investigation using a complex adaptive systems lens:
 
1) What features of EVD transmission are most problematic for reliable, robust forecasting: changing behavior, intervention, viral evolution, complex social networks, etc?
 
2) How/can we use digital data to either improve forecasts or inform model selection?
 
3) Can one quantify the value of additional information in real-time?
 
'''Contact:''' Samuel Scarpino, SFI Omidyar Fellow, Santa Fe Institute - scarpino@santafe.edu
 
 
'''Interested:'''
 
Marie-Pierre Hasne 
Chris Verzijl 
Junming Huang 
'Sola Omoju 
Christine Harvey 
Daniel Citron (dtc65@cornell.edu) 
William Chang (williamkurtischang at gee-mail)

==Effect of landscape topography on vegetation connectivity and navigability==
''Context''
 
Landscape topography influences the dynamics of the processes that take place on it. Evolution of ecosystems networks, river networks, vegetation cover type, microclimate cycles are all interlinked deeply to the local landscape topography.

In addition to the landscape these networks are also influenced by each other conditioning the emergence and stability of ecosystems, and subsequently the behaviour of agents in the ecosystem like migration pathways of animal herds, human settlement patterns etc.

Connectivity patterns emerge from the interaction of these processes, and thus a better understanding and quantification of those patterns is critical to understanding the dynamics of the system.

 
''How do we want to approach the problem''
 
In this project, we will randomly generate landscape topography and subsequent vegetation cover from a set of parameters from known geological and biological processes. The generated data set will then be used to investigate the following questions:
 
(1) What is the connectivity of landscape patterns that emerge at different scales using different techniques such as clustering analysis, percolation theory or network theory, and can we quantify them ?
 
(2) What is the navigability of biological agents (e.g animals, humans, robots!) under such landscape patterns. We can compare mobility trails in existing landscapes to validate our hypothesis. We propose to use the tools like ABMs to simulate and characterise mobility success.
 
(3) We further aim to compare the measures of navigability with the metrics of connectivity, establishing a framework of comparison.
 

''Contact:''
 tirtha.bandy@csiro.au
 alej.tejedor@gmail.com

''Meetings''
(1) Wednesday 1pm Coffee shop

People Interested 
Martina

==[[Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe]]==
Click on the link to go to the project page for meetings, project details, and progress.
 
contact: mveitzel@ucsc.edu
 

==Mapping Complexity/Human Knowledge as a Complex Adaptive System‏==
''Context''
 
Ants leave pheromone trail patterns which they are aware of only in a local sense. They do not have the cognitive faculties to step back and look at the trails and grasp the ant-trail network as a totality. Also, the artifacts they leave behind are physical entities which then provides the aggregate feedback to the aggregate ant body to then feed the evolution of the ant body as a CAS system. In contrast, humans do have the requisite cognitive abilities. The "pheromone trails" we leave behind are the knowledge trails coded in symbolic knowledge artifacts. In contrast to the physical artifacts that ants leave behind, the knowledge artifacts that we leave behind are far more flexible and potent, both at the aggregate as well as at the individual levels. But like the ants, until recently, we did not have the means to step back and map the knowledge "pheromone trails" to obtain the big picture and its global/local dynamics. The burgeoning field of scientometrics is making available visualization tools to help us map and study the evolutionary dynamics of the knowledge network structures.
 
''Data and Questions''
 
The goals of this project include
<ol>
<li>Extract the terms from approximately 1600 working papers published by SFI</li>
<li>Map the intra/inter conceptual network structures</li>
<li>Study the evolution of these structures across time</li>
<li>High-light the gap-closure of knowledge reverse-salients (if any)</li>
<li>Capture any of the network patterns that repeat</li>
<li>Study the diffusion of concepts across the network</li>
<li>Provide visualization tools for navigating the complexity corpus, etc</li>
</ol>
 
''Possible methods''
 
Latent Semantic Analysis (LSA) and Latent Document Analysis (LDA)
 
References:
<ol>
<li> SFI Working Papers: http://www.santafe.edu/research/working-papers/</li>
<li> Atlas-Science-Visualizing: http://www.amazon.com/Atlas-Science-Visualizing-What-Know/dp/0262014459</li>
<li> Atlas-Science-Visualizing WebSite: http://scimaps.org/atlas</li>
<li> Mapping-Scientific-Frontiers-Knowledge-Visualization: http://www.amazon.com/Mapping-Scientific-Frontiers-Knowledge-Visualization/dp/1447151275</li>
<li> Katy Börner presents at Science of Science: https://www.youtube.com/watch?v=pzCqGBNzomE</li>
<li> Scholarly Data, Network Science, and (Google) Maps:https://www.youtube.com/watch?v=vos5QBDywMM</li>
<li> LSA Video Lect: http://videolectures.net/slsfs05_hofmann_lsvm/</li>
<li> What is LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf</li>
<li> LSA Wiki:http://en.wikipedia.org/wiki/Latent_semantic_analysis</li>
<li> LDA: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm</li>
<li> Fusari, A. (2014). Methodological Misconceptions in the Social Sciences: Rethinking Social Thought and Social Processes: http://www.springer.com/us/book/9789401786744 </li>
<li> Kirsh, D. (2013). Thinking with external representations. Cognition Beyond the Brain: http://adrenaline.ucsd.edu/kirsh/Articles/Interaction/thinkingexternalrepresentations.pdf </li>
<li> Holland, J. H. (1992). Adaptation in natural and artificial systems: https://mitpress.mit.edu/index.php?q=books/adaptation-natural-and-artificial-systems </li>
<li> Where to start with text mining. http://tedunderwood.com/2012/08/14/where-to-start-with-text-mining/ </li>
<li> Singular Value Decomposition Tutorial https://www.ling.ohio-state.edu/~kbaker/pubs/Singular_Value_Decomposition_Tutorial.pdf </li>
<li> Latent Semantic Analysis (LSA) Tutorial: http://www.puffinwarellc.com/index.php/news-and-articles/articles/33-latent-semantic-analysis-tutorial.html </li>
<li> LSA in Detail: http://www2.denizyuret.com/ref/berry/berry95using.pdf </li>
<li> Web Based LSA: http://webapp1.dlib.indiana.edu/newton/lsa/index.php </li>
<li> Another Technique: t-Distributed Stochastic Neighbor Embedding (t-SNE): http://lvdmaaten.github.io/tsne/ </li>
<li> tf–idf: https://en.wikipedia.org/wiki/Tf%E2%80%93idf </li>

</ol>
 
'''Interested:'''
 
<ul>
<li> John Thomas (radjohn@live.com) </li>
<li> Haitao Shang (hts@mit.edu) </li>
<li> Christopher Verzijl (cjo.verzijl@gmail.com) </li>
<li> Anna Zaytseva (annza944@gmail.com) </li>
<li> Penny Mealy (penny.mealy@gmail.com) </li>
</ul>
 
 

==Navigating Music, Brain and The Edge of Chaos ‏==
''Context'' 
[[Navigating Music, Brain and The Edge of Chaos]]
 
Brain Sciences have revealed that we/it lives "on the edge of chaos" exhibiting "self-organized criticality" that is tentatively balanced between normalcy and madness. Over the course of history, humans have used various agents and activities to shape, influence and control this living-chaos ranging from substances such as caffeine, sugar, drugs etc., to activities such as the arts (including music), social-discourse/therapy, meditation etc. Of these, music has a distinct role in shaping our moods and helping us transition between different mental states, as well as maintain it for extended periods of time. Clearly we have been using music to help us control and shape the internal chaos. But until recently, the quantitative instrumentation of this massively complex system that comprises of close to a 100-billion neurons networked into a 1000-trillion synaptic edifice has not been available to the common man. But of late, affordable, wearable EEG's are available on the market, thus making the quantitative study of the influence of music in brain dynamics feasible on a large-scale/crowd-sourcing sense. To help come to terms with the complexity of our 1000-trillion synaptic edifice, we need to gather data on a vast scale. The proposed research is a proof-of-concept, exploratory foray into making this happen.
 

History of music in 5 min
https://www.youtube.com/watch?v=Gt2zubHcER4

'''Interested:'''
 
<ul>
<li> Sara Lumbreras (sara.lumbreras@gmail.com) </li>
<li> Ilaria b (bertazzi.ilaria@alice.it) </li>
<li> Braun, Urs (urs.braun@zi-mannheim.de) </li>
<li> John Thomas (radjohn@live.com) </li>
<li> Glenn Magerman (glenn.magerman@kuleuven.be) </li>
<li> Daniel Friedman (DanielAriFriedman@gmail.com)</li>
</ul>
 
 

==Powerlaw fitting and alternative distributions - Theory/statistics ==
Clauset, Shalizi and Newman (2009) propose a maximum likelihood method to estimate the powerlaw exponent of a variable of interest. This is a great improvement on earlier methods such as OLS that dominated the literature up to then. However, one can fit a powerlaw to any dataset and the most we can say is that our observations are consistent with the hypothesis that x is drawn from a powerlaw distribution. One easily implementable method to compare the powerlaw fit to other fits is then a likelihood ratio test for both models.

One particular discussion is the distinction between a powerlaw (PL) and a log-normal (LN) fit. For an avid discussion between both fits on city size and Zipf's law, see Eeckhout (2004, 2009) and Levy (2009), where the discussion now settled on city sizes following a log-normal distribution instead of a powerlaw. Similarly for the discussion on firm size distribution: Simon and Bonini, 1958; Ijri and Simon, 1977; Stanley et al., 1995; Sutton, 1997; Axtell, 2001; Okuyama et al., 1999; Cabral and Mata, 2003; Gaffeo et al., 2003; Aoyama et al., 2004; Fujiwara et al., 2004a,b; Kaizoji et al., 2006; Takayasu et al., 2008; Duchin and Levy, 2008; Schwarzkopf and Farmer, 2008, ...

This distinction matters for several reasons:
- PL and LN come from very similar model and differences in initial conditions can lead to very different outcomes. PL exhibits a choice for x_min, below which the unit of observation is not feasible to exist (eg minimum city size, firm size, word length, ...). LN has no minimum size.
- PL and LN that look similar are the difference between infinite (PL) and large but finite variance (LN)
- shock propagation: when unit-level shocks are large enough to show aggregate perturbances if the distribution is powerlaw with infinite variance, while these shocks wash out fast when the distribution is log-normal. (Gabaix, 1999; Gabaix 2009; Acemoglu et al. 2012, ...).

I have encountered some issues which I would like to explore further:
1. The distinction between lognormal and powerlaw in the data is very sensitive to data truncation: in the above discussions, researchers have slightly different datasets, covering more or less of the population at hand. Left-truncation (i.e. observations not in the dataset because they are too small to be reported) can strongly drive the outcome of the fit, even when endogenizing the x_min cutoff. I have data on the universe of Belgian firms, much more complete than e.g. US Census data, where I have done some preliminary tests on this. The question is then: how to formalise this distinction and what are the theoretical and practical caveats to look out for when applying this method.
2. MLE fitting seems to be sensitive to the choice of units as well: rescaling a variable by a factor 1000, 1000000 etc seems to influence the endogenous x_min choice and hence the estimated parameter. This reminds me of some work on scaling invariance in negative binomial estimators. What is going on here?
3. Can we set up a model that generalises both? I've been looking at Levy stable distributions, but did not do anything with it yet.

Interested people:
* Corbain
* Laura
*Binyang
*Sahil
*Tirtha
*Glenn
*Junming

Literature:
Powerlaw fitting in empirical data - Clauset, Shalizi and Newman (2009): http://arxiv.org/abs/0706.1062

==Organ Transplant Analysis==
Over 120,000 people in the United States are currently on the waiting list for an organ transplant. The size of this waiting list relates to over 6,000 deaths a year while waiting for a transplant and tens of billions of dollars in government spending. I have access to the following data sets:
 
* All transplants performed in the US from October 1987 to June 2014 (including follow-up data)
* All living and deceased organ donors in the same time period (with follow-up data on living donors)
* Waiting list data for everyone who signed up for the list
* 2012 National Survey on Attitudes and Behaviors on Organ Donation
* Social media data relating to organ donation since 2008

Open to ideas and suggestions for the topic, there are a lot of interesting questions to investigate including cultural/racial/gender differences in organ donation. I have several preliminary reports and exploratory analysis done on differences in donors.

Second Meeting Thursday, June 11th 4:15 in the coffee shop!

===Matching Game===
Rules for the matching game:

Objective: Create the largest chain possible using blood types in the room.

Follow the blood type guidelines for donation to form a donor chain.
*Assume that self-matches are not possible, for example someone with a donor of type AB and a recipient of type AB can not match themselves and needs to find someone else to complete their chain.
*Create a donor chain to help as many people as possible.
*Post results below for the prize
*There will be several "good samaritan" donors which will donate and need nothing in return, this is a great way to start your chain. Using a good samaritan chain also means you don't need a donor for the end point.

====Game Workspace====
Use this workspace to post for and find matches!

==== Donor Chains ====
Post your chain here. Example is given below:

=====Example Chain 1=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| A
|-
| Laura
| AB
| B
|-
| Matt
| A
| AB
|}

=====Example Chain 2=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| None
|-
| Laura
| A
| B
|-
| Matt
| None
| A
|}

======Richard's Current Table======

Database size: 37. Used: 32, Unused: 5. Unused altruistic: 5. OPTIMAL

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| X
|-
| MatthIn
| AB
| O
|-
| Susanne
| A
| AB
|-
| Michael
| O
| A
|-
| MatthewO
| AB
| O
|-
| Brent
| O
| AB
|-
| James
| AB
| O
|-
| Alejandro
| O
| AB
|-
| Glenn
| O
| A
|-
| AndySchauf
| A
| A
|-
| Richard
| A
| A
|-
| Maria
| O
| A
|-
| Keith
| AB
| O
|-
| Vanessa
| A
| AB
|-
| Carolina
| O
| A
|-
| Kleber
| O
| A
|-
| Sam
| O
| B
|-
| Masa
| A
| O
|-
| Cobain
| B
| A
|-
| Melissa
| A
| B
|-
| Jakub
| O
| A
|-
| Martina
| A
| O
|-
| Jae
| B
| A
|-
| Charon
| B
| B
|-
| Nilton
| O
| B
|-
| Alice
| AB
| O
|-
| Jelle
| O
| AB
|-
| MariePierre
| A
| O
|-
| Urs
| B
| A
|-
| Laurence
| O
| B
|-
| Chris
| O
| A
|-
| Havier
| AB
| O
|}

Unused:

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Jelle
| AB
| X
|-
| JGab
| O
| X
|-
| LauraCondon
| O
| X
|-
| Maggie
| O
| X
|-
| Matthew
| O
| X
|}

==Multi-dimensional social networks in the evolution, development and resilience of informal economies. ==
Informal economies are defined as economic activities that occur outside the purview of corporate public and private institutions. These types of economies proliferate where traditional economic actors are unable to productively exercise their activities, especially due to costly constraints (De Soto 2003). Firms may face adverse incentives to expand production by hiring more workers or incorporating more capital due to the low productivity of their workers or the predatory practices of rapacious elites or corrupt governments. Labor itself, i.e. people, may face hurdles in trying to make the jump towards entrepreneurial activities or jobs in the formal economy which allow the accumulation of experience, retirement savings, access to insurance or precautionary savings to face unexpected events (like disease, disability, etc.) due to lack of capital access (whether human and financial).

'''For this project, we propose a different interpretation: We seek to understand and model how multi-faceted social networks provide robust alternatives to formal economies, which far from being seen as degenerate forms of social organization, in many instances co-exist, challenge and compete with formal employment and economic activities. While some types of informal economies operate under adverse contexts, their resiliency may be understood as a type of adaptive fitness and not the mere result of stubborn cultural path-dependency. '''

For the most part, informal economies have been analysed as counterpoint to an ideal type of a formal economy with low levels of trust, respect of property rights, access to credit and public institutions - like the court systems (see Losby et al. 2002 for a review). However, informal economies have been coupled with their formal counterparts since the development of capitalism in the 19th Century. To name a famous historical example, Old London's East End's informal sector was described harshly by Engels (1844) in his famous tract, "The Condition of the Working Class in England". But almost fifty years later, in a new preface to the book, Engels recognized the progress that took place there under the aegis of working class organizations in the area.

Research has begun to catch on to this idea – developing a literature in the areas of informal risk sharing and remittances, which are in some sense informal counterparts to insurance and banking. Consumption patterns in poor, rural villages are remarkably smooth suggesting that risk-sharing measures are prevalent, although imperfect (Townsend). Field studies of networks underlying village risk sharing systems have found that households primarily receive help from existing social connections, such as friends and relatives, in the form of informal loans or transfers. Theoretical work in network economics, by authors such as Matt Jackson, found that certain empirically prevalent network structures may directly benefit the stability of favor exchange systems. Another line of inquiry focuses on remittance income, generally informal transfers across kinship ties. World Bank researchers have found that remittances from overseas migrants respond dramatically to regional income shocks, replacing upwards of 60% of lost income in households with international migrants. Further studies, cited below, have generalized those results. Furthermore, reductions in the cost of sending remittances, which occurs with the advent of mobile money, further mitigates exposure to risk in receiving households.

Also recently, authors have written on how communities come together in situations where formal economies are unavailable by external shocks (like disasters) or due to lack of access (due to conditions of abject poverty). In the case of the latter, Venkatesh (2008) provided gripping testimonies of how informal economies arose and developed in the South Side of Chicago in low trust environments via cash transactions and informal service contracts enforced by (and for the benefit of) local criminal gangs. These gangs were seen, surprisingly, as alternative coordinating mechanisms to settle claims between neighbors. In the case of the former, Storr and Grube (2013) in a series of papers has argued how "shared histories and perspectives, and the stability of social networks within the community" allows communities who suffered disasters to cope and endogenously resolve immediate and complex problems. Providing an example of these social networks in action, research has found that remittances flow quickly into areas affected by natural disasters when there is a technology in place for it to happen.

Taking these lines of inquiry together, the findings suggest that informal handling of risk takes place on a large scale and that social networks informally connect communities in ways that impact their economies. Recent trends suggest that the interaction of formal and informal economic processes is growing on a nationwide scale. First, the aggregate value of remittance flows is large and growing, nearing the value of foreign direct investment. Second, advancements that formalize previously informal transactions are expanding dramatically in emerging economies through various forms of branchless banking. National economies may be embedded in profoundly influential informal systems that have never been holistically studied.

On a more general note, this project touches lingering questions in economics. For example, some might argue that more stringent labor regulations should have increased informal employment, but in fact the opposite happened. And while the economic rationale for the former statement remains valid, we suggest that unions, as other types of social organizations, as examples of ways whereas people interact via organized networks, provide richer dimensions than those suggested by their interaction as mere economic agents. Hence, unions, as well as other types of faith-based, ethnic, community and other interest groups may provide ways of interaction that escape narrow economic outcomes.

====References====

• De Soto, Hernando (2000/2003) The Mystery of Capital. Basic Books.
• Losby, Jan; Else, John; Kingslow, Marcia; Edgcomb, Elaine; Malm, Erika and Vivian Kao (2002) The Informal Economy: A Literature Review. ISED Consulting and Research and the Aspen Institute Working Paper. http://www.kingslow-assoc.com/images/Informal_Economy_Lit_Review.pdf
• Townsend, Robert M. "Risk and Insurance in Village India." (1994)
• Fafchamps, Marcel, and Susan Lund. "Risk-sharing Networks in Rural Philippines." (2003)
• Weerdt, Joachim De, and Stefan Dercon. "Risk-sharing Networks and Insurance against Illness." (2006)
• Matthew O. Jackson, Tomas Rodriguez-Barraquer and Xu Tan. “Social Capital and Social Quilts: Network Patterns of Favor Exchange.” (2012)
• Yang D and H Choi."Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines"(2007)
• Kurosaki, Takashi. "Consumption vulnerability to risk in rural Pakistan." (2006)
• Jack, W, and T. Suri. "Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution” (2014)
• Engels, Friedrich (1844/1892) The Condition of the Working Class in England. http://www.gutenberg.org/files/17306/17306-h/17306-h.htm
• Venkatesh, Sudhir (2008) Gang Leader for a Day: A Rogue Sociologist Takes to the Streets. Penguin Books.
• Storr, Virgil and Laura Grube (2013) The Capacity for Self-Governance and Post-Disaster Resiliency. George Mason University, Department of Economics Working Paper 13-37. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2350830##
• Blumenstock, Joshua Evan and Fafchamps, Marcel and Eagle, Nathan. “Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda” (2011).
• Ratha, Dilip. "Workers’ remittances: an important and stable source of external development finance." (2005).
• Pénicaud, Claire, and Arunjay Katakam. State of the Industry 2013: Mobile Financial Services for the Unbanked. Rep. N.p.: GSMA MMU, 2013.

If interested, please list your name below.

'''Interested:'''
<li> Eloy Fisher (eloy.fisher@fulbrightmail.org) </li>
<li> Carolina Mattsson (carromattsson@gmail.com) </li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>

<li> Jakub Rojcek (jakub.rojcek@gmail.com) </li>

==Exploring Community Formation through Analysis of Scholarly Corpus (ArXiv)==

The Arxiv [http://arxiv.org/] is a free online repository of scientific preprint articles, mostly from physics and mathematics. It currently contains over 800,000 articles, dating back to 1991. Currently, a lot of the data associated with this repository is completely available: paper submission dates; full texts of papers; author names and coauthorship information. Additionally, there is some citations data available. (If necessary, I can also find papers' subdiscipline labels; submitting authors' email address domain names; other things.)

In the past I have been using these data to try and explore how communities of authors who have shared interests grow over time. For example, we can pinpoint the first ever paper about topological insulators, and search for all subsequent papers on that topic. As more papers are written, authors begin to join the field of research and to form strong ties by collaborating with one another. We can use the ArXiv data to visualize and analyze exactly how these communities form and grow.

===Brainstorming notes===
Isolation between topics, crossing interdisciplinary boundaries, finding (topological) separation distance between disciplines or research groups

Tipping points - can we look for separation of or mergers of two groups or disciplines?

Can we identify key papers (or groups of papers) that initiate ties between fields.

Sentiment analysis: can we identify when a paper's citation is an endorsement or a refutation?

Tools for Analysis: Change point detection; relational event models

Can we find more comprehensive/better citation data?

Lucene and indexing tools:
- Can we re-index our text database after removing stop words?
- Can we index the titles and abstracts?
- Elastic Search - tool for interacting with Lucene

This set of text-processing [http://alias-i.com/lingpipe/demos/tutorial/read-me.html tutorials] is pretty handy for background info and inspiration. The software package doesn't simply plug-into a Lucene/Solr/Elastic Search index, but it could be done. This package from [http://nlp.stanford.edu/software/ Standford] seems very capable.

===Next meeting===
Thursday @ 9AM in Coffee Shop

== PolComplex - Epistemic communities and policy dynamics in the UK Parliament ==

Over the last twenty years, there has been extensive debate about what the core topics in public policy are, and if they change according to type and number. Furthermore, it is still not clear how policy topics mutate and diffuse over time. Human-based text analysis of governmental documents has shed only some light on these research questions: 21 general topics have been proposed, while other accounts tend to restrict it to about five main clusters. Yet, this approach has not been able to devise a reliable and systemic method to capture topic dynamics, diffusion, and their relation to the human actors that talk about them.

This project is an extension of an exploratory research that intends to overcome such limitations, through the application of natural language processing (more specifically, dynamic topic modeling and sentiment analysis), topological and network analysis on a dataset containing UK House of Commons debates ranging from 1975 to 2014. The aim is manyfold. We hope to identify the structure and dynamics of epistemic policy communities - i.e. of political actors revolving around similar policy interest -, and how these relate to factors such as party membership, seniority, relevant historical events.

Some references:

[http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.734168 Taddy (2013), "Multinomial Inverse Regression for Text Analysis"] 

[https://www.cs.princeton.edu/~blei/papers/BleiLafferty2007.pdf Blei and Lafferty (2007), "A Correlated Topic Model of Science"] 

== Genomic variations from a chaotic mapping ==

Following Dabby CHAOS 6 (2), 1996 Musical variations from a chaotic mapping, this project will explore genomic variations in key metabolic genes that are known to be widely spread across bacterial phylogeny (e.g. the Nif cluster which is used in nitrogen fixation). There are several ways that genomic data can be treated as "music":

1) Nucleotide: 4 notes - A, G, C, T 
2) Amino Acids: 20 notes 
3) codon triplets: 64 notes 
4) tetra nucleotides: 256 notes. 

One possible goal is to take a seed sequence from some random organism and generate new variations. Then using homology search (e.g. BLAST) or phylogenetics, determine if that variation exists in nature? If not, perhaps model the potential protein folding (if possible?) to determine if that variation could exist in nature. This is VERY preliminary and can go in many directions.

If interested please list name below or contact Jarrod at jscott@bigelow.org

Reading
http://digitalcommons.olin.edu/cgi/viewcontent.cgi?article=1000&context=ahse_pub&sei-redir=1&referer=https%3A%2F%2Fscholar.google.com%2Fscholar%3Fhl%3Den%26q%3Ddabby%2Bchaos%26btnG%3D%26as_sdt%3D1%252C32%26as_sdtp%3D#search=%22dabby%20chaos%22

== Free Energy Theory ==
The Free Energy (FE) minimisation framework tries to explain how biological systems (such as a cell or a brain) self-organise in order to occupy the (often very limited number of) non-equilibrium states that minimise free energy. This is also known as active inference. A simple corollary of active inference is that agents behave as to minimise their prediction error, or the difference between prediction and reality. Thermodynamic free energy is a measure of energy available in a system to do useful work. This can be framed in an information theoretic setting, as the difference between how the world is being represented and how it actually is. A better fit means a lower information-theoretic free energy, as more resources are being put to ‘good use’ in representing the world. The overarching logic of FE theory is that a better model of the world help maintain structure and organisation, which ultimately helps the system resist increases in entropy.
 
 
This is not a set-in-stone project with any concrete aim (yet). We are a few people interested in exploring the theoretical and practical implications of these ideas, and you're more than welcome to join in!
 
 
'''Interested:'''
 
<ul>
<li> Jelle Bruineberg (j.bruineberg@gmail.com ) </li>
<li> Tobias Morville (tobiasmorville@gmail.com) </li>
<li> Susanne Pettersson (guspsusa85@student.edu.se) </li>
<li> Maggie Simon (margaret.w.simon@gmail.com) </li>
<li> Martina Steffen (martinasemail@gmail.com) </li>
<li> Sahil (sahilgar@usc.edu) </li>
<li> Tirtha (Tirtha.Bandy@csiro.au) </li>
<li> Anna (annza944@gmail.com) </li>
<li> Matt O (mmosmond@gmail.com) </li>
<li> Will Chang (williamkurtischang at gmail dot com) </li>
<li> Cobain (mrdc1g10@soton.ac.uk) </li>
</ul>
Reading:
 
http://rsif.royalsocietypublishing.org/content/10/86/20130475 
http://arxiv.org/abs/1503.04187 
http://www.mdpi.com/1099-4300/15/1/311 

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
'Carolina'
'Alice'
'Federico'
'Jean Gab'
'Sola'
'Ilaria'
'Daniel T'

==Comparison of Network vs Scaling Theory based Models in Ecology (Cobain - mrdc1g10@soton.ac.uk)==

One of the biggest difficulties ecologists face is trying to understand the ecosystem dynamics based on very little biological information (compared to the size of the system) - observational data is logistically difficult and can be very expensive to acquire, as well as often being very time consuming. In terms of modelling, to overcome this problem, two approaches have been utilised. 

The first is a network based approach whereby the nodes represent biomass density of a particular species (or a higher level taxanomic group and/or resources) and edges as the trophic interactions (who eats who). This method depends on what we know about the trophic behaviour of the species involved (which is often very limited and there can be many species to parameterise), and represents only the central tendency of what could potentially be very diverse behaviour within and between populations. 

The second approach is to use scaling theory to describe average trophic interactions and other biological processes based on individual organism body size along the size continuum, viewing the community as a very size structured dynamical system. This requires less specific knowledge about the organisms in the community ''per se'', however this again represents only the central tendency of a given size class of what could be very diverse behaviour. Functional differences can be potentially introduced (coupled benthic-pelagic systems for example), but to the detriment of braking down the predictability of the well known allometric scaling laws with size as specificity increases. 

There has been little to no comparison of these two methods of modelling ecological systems (at least to my knowledge) and the question arises that, given the same starting information, how well do both approaches model the dynamics of an ecosystem, given the limited biological information we have? Is one approach better at capturing energy flow through the system / community structure and stability etc. compared to the other? If the models vary then such information will better inform ecologists on which to use depending on what type of questions they are trying to answer. 

Therefore, the project proposed here aims to investigate these two methods. The community that is used to seed an experimental mesocosm setup (mesocosms are large tank set-ups designed to reflect the complexity of natural ecosystems but still being able to be artificially controlled and are still relatively simple), will be input into two models based on the separate approaches and then run to steady state. The model outputs will then be compared and contrasted with eachother as well as the mesocosm community at steady state. Further work may include examining perturbation dynamics, dependent on what data we have available.

'''Interested: Name / Email''' 
Martina Steffen (martinasemail@gmail.com)

==Dynamics of homicide (Matthew Ingram - mingram@albany.edu)==

Integrating temporal, spatial, and multi-level concepts

1:30pm in the coffee shop

Am interested in the discussion on this project...Sola Omoju

Interested - Nilton Cardoso

==Multiplex Adaptive Networks (Daniel - dtc65@cornell.edu)==

7 pm in the coffee shop

==Modeling brain diseases (or cancerous bio pathways) (Sahil - sahilgar@usc.edu)==
Interested people can put a meeting time as per their convenience here.
 
No meeting time indicated
 
One potential meeting time can be 3pm in coffee shop ?
 
Information theoretic algorithms can also be explored for the problem.
 
Discovering Structure in High-Dimensional Data Through Correlation Explanation. http://papers.nips.cc/paper/5580-positive-curvature-and-hamiltonian-monte-carlo
 
Maximally Informative Hierarchical Representations
of High-Dimensional Data. http://jmlr.org/proceedings/papers/v38/versteeg15.pdf

 
'''Interested:'''
 
<ul>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> Laura Condon (lcondon@mymail.mines.edu)</li>
</ul>
 
 

==Analysis of UK parliament speeches 1935-2014 (Stefano - etstefano@gmail.com)==

No meeting time indicated

==Mapping Complexity/Human Knowledge as a Complex Adaptive System (radjohn@live.com)==
2pm/Wed 6/10 in Conference Room (Tentative)

==Navigating Music, Brain and The Edge of Chaos (radjohn@live.com)==
10:45am/Wed 6/10 in Conference Room
9:00am/Thu 7/10 at the Coffee Shop

'''Interested:'''
Sahil Garg

==Analysis of rule-based modeling for dopamine-dependent synaptic plasticity (emilia.m.wysocka@gmail.com)==

----------------------------------------------------------------------------------
Modeling and analysis of phenomenons and evolution of stochastic, combinatorially complex signalling systems in a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system).

----------------------------------------------------------------------------------
Rule-based modeling features:

*biological systems as concurrent processes
*dynamics of post-translational modifications
*domain availability
*competitive binding
*causality and intrinsic structure
*binding sites
*interaction rules replace reaction equations
*infinite number of reactions with a small and finite number of rules
*reduction of parameter space
*“don't care don't write” - adjustable rule contextualization
*single reaction rule and parameters generalize classes of multiple rules
*modular and extensible language
*specification language & simulation/integration environment
*static and causal analysis
*Kappa/KaSim & BioNetGen/NFsim -- specification language/network-free simulator

----------------------------------------------------------------------------------
----------------------------------------------------------------------------------
Meetings:
----------------------------------------------------------------------------------
* WEDNESDAY 11/06/2015

Addressing problems in terms of the other aspects of the projects apart from the biological questions and verification (matching results to some published experiments or known behaviour). So my plan is as follows:

* divide the group (roughly) into people who look in to biological meaning and validation and the one which tries to do the analysis of system phenomenons and evolution of stochastic, combinatorially complex signalling systems in both a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system) - all possible states, scenarios of the system abstracted from the biological meaning.

Things that could be modelled (look in dropbox folder: https://www.dropbox.com/sh/g080w60pt2vgi7v/AACHlMf2JwpP2EZqKq7Di6N2a/possible%20models?dl=0):

* to make things easier and have the modeling part done quickly- base the model on the dopamine-related synaptic plasticity (SBML ODE model): http://www.ebi.ac.uk/biomodels-main/MODEL1101170000;

* I found a series of papers suitable to the subject of the Complex Systems course, e.g.: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC344803/. I was thinking to even try to compare higher level brain data (networks, spiking ..) with purely molecular model

* noise and low copy number (http://www.princeton.edu/~wbialek/our_papers/bialek+setayeshgar_08.pdf); this is quite interesting as for sufficiency of binding events -> http://www.princeton.edu/~wbialek/our_papers/bialek+ranganathan_07.pdf

* spontaneous flipping of interactions (phosphorylations and others) between proteins, described by the god of non-linear dynamics (S. Strogatz)

----------------------------------------------------------------------------------
* THURSDAY 11/06/2015 : http://doodle.com/se23ibwtkrkccs4s

----------------------------------------------------------------------------------
Some refs:
*https://www.dropbox.com/sh/g080w60pt2vgi7v/AADUy7Ge-J-oSYkyTeYOo7V8a?dl=0)
*http://www.pps.univ-paris-diderot.fr/~jkrivine/homepage/Teaching.html

==Ecology non-working group (williamkurtischang at gee-mail dot com)==
[[Informal ecology interest group]].

==Making Supply Chains Resilient to Disasters (mh2905 att columbia dott edu)==
'''Key words''': resilience, natural hazards, supply chains, interdependency, interconnected risks, cascading failures

'''Rational''': I am interested in examining what kinds of network structures and features contribute to increasing resilience of supply chains to natural disasters. I believe this area of work is important because regional disasters negatively impact the global economy through disruptions in supply chain networks. The pioneer study published in Nature urges the need for making supply chains climate-smart (Levermann 2014). Also in the industry, the World Economic Forum published a report to address this issue in 2013. Few researches, however, assess and model the impacts of adverse weather on supply chains. I would like to evaluate the impacts based on modeling.

'''Data set''': Supply chains data of a multinational manufacturing company.Global climate data, disaster data, etc.

'''Possible techniques''': Agent-Based Model, Complexity science, network theory and evolution, complex adaptive systems, GIS, operations research, manufacturing engineering, and I am open to any techniques.

'''Myself''': As I joined the program yesterday, please find my background here.
http://tuvalu.santafe.edu/events/workshops/index.php/Masahiko_Haraguchi

'''Please put your names below if you want to be informed about this project by email'''

'''Interested''':
Masa Haraguchi

==From Ethnic Diversity to Religious Zeal: Retrospective/Predictive Construction of the World Ethnic Map (vdzutsati@asu.edu)==

There is a new dataset on ethnic groups (2014), which claims to include all ethnic groups of the world http://www.cidcm.umd.edu/mar/amar_project.asp. There are also several cross national datasets on religion and other variables of interest, normally, socio-economic http://www.thearda.com/Archive/CrossNational.asp. Coming from social sciences I thought it would be really interesting to apply methods and perspectives from other disciplines to social science data.

One idea that I have in mind is to explore how ethnicity and religion interact.

Quick empirical checks indicate that Subsaharan Africa is home to about 1,500 ethnic and subethnic groups, in comparison to about 90 ethnic groups in the Middle East and North Africa. India alone accounts for nearly 2,000 ethnic and subethnic groups, while Europe, including all the countries of the former USSR and large immigrant groups from other parts of the world, has only about 260 ethnic groups. The picture that emerges from this simple comparison is that the spread of Abrahamic religions appears to be associated with the high depletion rate of ethnic groups. The exception of China, which has little more than 50 ethnic groups, can be explained by the country’s long history of a centralized state.

The idea then is to assume that we have had the control group of nations that did not experience the influx of Abrahamic religions (or more precisely received limited exposure to them) and the experimental group that was exposed to the spread of Abrahamic religions. Since we know what the distribution of ethnic groups in the control group is we can project what the distribution of ethnic groups in the experimental group would be, if the group were not exposed to Abrahamic religions.

Of course, we could go in the other direction too and make a projection about future – what would happen to ethnic groups if all of them adopted an Abrahamic religion.

One of the challenges in this project is absence of a dynamical system, I.e. We don’t have data on how these things changed over time, but I still think that projection about the future and the past would be kinda cool:)

'''Interested''':

==Nationalist vs religious rebel violence (vdzutsati@asu.edu)==

I would like to propose yet another possible project. This time it’s on rebel violence. I have a large dataset on rebel (and government) violence (~1 million observations). The paper that used this data was published couple of months ago (Islamists and Nationalists: Rebel Motivation and Counterinsurgency in Russia’s North Caucasus). The dataset contains event counts, GIS data, demographic and economic characteristics and some other stuff. The primary hunch of the paper was to discover the differences between nationalist and Islamist rebels.

Given all the cool methods we are learning here, my first instinct is to use the methods and tools (TISEAN?) to explore and discover whether nationalist violence is substantively different from religious violence and try to answer why.

One problem that I can see is that the event coding was done by a machine, so this may have “contaminated” the data. But if both religious and nationalist data were contaminated equally then the difference should still be evident.

'''Interested''':

==[[Archived Projects ]]==

Complex Systems Summer School 2015-Projects & Working Groups

2015-07-02T19:38:16Z

Rbarnes: /* Richard's Current Table */

{{Complex Systems Summer School 2015}}

==[[Reservations for Evans Science rm. 215]]==

Click on the title link to reserve Evans Science rm. 215 for group usage.

==Californian Drought Model==
Problem definition:
Water is an ongoing problem in California. Although corrective measures are taken for some years, periods of droughts are depleting water ground levels to not sustainable levels.
Aims:
A Netlogo simulation on how likely Californian agents evolve with regards to drought. The ABM could be used to explore the potential impacts of forms of regulation.
Outcomes:
Create an exploratory agent based model based on real data, enabling the possibility of simulating different methods of regulation, including feedbacks between the economic and hydrologic systems.

Extension 1: We will conduct a network analysis of Twitter data (hashtags #drought #California) to explore the social dynamics and information flows between stakeholders.

Extension 2: Phase-space reconstruction of groundwater level time series data, comparing the dynamics in different parts of the Central Valley.

==City Resilience==
'''Summary:''' This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities. 
'''Contact:''' Richard Barnes (rbarnes@umn.edu) 
'''Participants:''' Alex Tejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang, Martina Steffen, Juan Carlos Castilla, Brent Schneeman 
'''Wiki page:''' [[Resilience of Cities]]

==Complex Adaptive Systems and the Narrative approach: transdisciplinary methodologies for Complexity Science==
The narrative approach and it causality is different from causality in logico-scientific approaches. Making bridges among methodologies transfers knowledge, encourages important questions and switches philosophical paradigms. Formal frameworks of C(A)S can realy be complemented with the narrative, and actually should. The narrative approaches in Complexity Science are an emerging trend for fund-granting, and is really up to date to process documentary and speech-based data, reveal hidden meanings, distinguish causes from effects in overlapping systems of realms - "at the edge" of technology, social, economic, scientific, legal and other domains. The role of time in coding, intuitively generated conditional rules and computational paths. Combinations of C(A)S and the narrative is combination of quantitative and qualitative data and thinking in original - out of convertation and information loss. Synthesis of sciences is what it is about.

'''Questions''':
How to combine CS approaches (in particular CAS) and the narrative ones in a rule-based way? What to start from? What vizualizations there can be designed and used? Other questions at your descrete.

'''Some references''':
- Non-Equilibrium Social Science in ICT and Economics, CORDIS, EU: http://cordis.europa.eu/project/rcn/102344_en.html, http://www.nessnet.eu/

- Combining Complexity Theory with Narrative Research: https://youtu.be/pHjeFFGug1Y

- Haridimos Tsoukas and Mary Jo Hatch (2001), "Complex thinking, complex practice: The case for a narrative approach to organizational complexity: http://www.brown.uk.com/teaching/qualitativepostgrad/tsoukas.pdf

- David Christian, "Big History", Astrophysics, Chemistry, Biology, Information, emergence of life, technology: https://youtu.be/GlmvFjFceD8

'''Contact''': Anna (annza944@gmail.com)

Meet on Monday over lunch

'''Interested''': Marie Pierre, Jeroen, Jim, Melissa

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
 
'Carolina'
 
'Alice'
 
'Federico'
 
'Jean Gab'
 
'Sola'
 
'Ilaria'
 
'Daniel T'
 
'Sahil Garg

==Ebola==
The 2014-15 Ebola virus disease (EVD) outbreak in West Africa presented both unique opportunities and unique challenges to the epidemiological modeling community. For the first time during an emerging infectious disease outbreak, high resolution data--from a variety of sources--were made available to the academic community and many public health decision makers genuinely engaged with mathematical and computational modelers. However, the popular and scientific press were highly critical of most models ability to project the outbreak's course. The following key and open questions seem ripe for investigation using a complex adaptive systems lens:
 
1) What features of EVD transmission are most problematic for reliable, robust forecasting: changing behavior, intervention, viral evolution, complex social networks, etc?
 
2) How/can we use digital data to either improve forecasts or inform model selection?
 
3) Can one quantify the value of additional information in real-time?
 
'''Contact:''' Samuel Scarpino, SFI Omidyar Fellow, Santa Fe Institute - scarpino@santafe.edu
 
 
'''Interested:'''
 
Marie-Pierre Hasne 
Chris Verzijl 
Junming Huang 
'Sola Omoju 
Christine Harvey 
Daniel Citron (dtc65@cornell.edu) 
William Chang (williamkurtischang at gee-mail)

==Effect of landscape topography on vegetation connectivity and navigability==
''Context''
 
Landscape topography influences the dynamics of the processes that take place on it. Evolution of ecosystems networks, river networks, vegetation cover type, microclimate cycles are all interlinked deeply to the local landscape topography.

In addition to the landscape these networks are also influenced by each other conditioning the emergence and stability of ecosystems, and subsequently the behaviour of agents in the ecosystem like migration pathways of animal herds, human settlement patterns etc.

Connectivity patterns emerge from the interaction of these processes, and thus a better understanding and quantification of those patterns is critical to understanding the dynamics of the system.

 
''How do we want to approach the problem''
 
In this project, we will randomly generate landscape topography and subsequent vegetation cover from a set of parameters from known geological and biological processes. The generated data set will then be used to investigate the following questions:
 
(1) What is the connectivity of landscape patterns that emerge at different scales using different techniques such as clustering analysis, percolation theory or network theory, and can we quantify them ?
 
(2) What is the navigability of biological agents (e.g animals, humans, robots!) under such landscape patterns. We can compare mobility trails in existing landscapes to validate our hypothesis. We propose to use the tools like ABMs to simulate and characterise mobility success.
 
(3) We further aim to compare the measures of navigability with the metrics of connectivity, establishing a framework of comparison.
 

''Contact:''
 tirtha.bandy@csiro.au
 alej.tejedor@gmail.com

''Meetings''
(1) Wednesday 1pm Coffee shop

People Interested 
Martina

==[[Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe]]==
Click on the link to go to the project page for meetings, project details, and progress.
 
contact: mveitzel@ucsc.edu
 

==Mapping Complexity/Human Knowledge as a Complex Adaptive System‏==
''Context''
 
Ants leave pheromone trail patterns which they are aware of only in a local sense. They do not have the cognitive faculties to step back and look at the trails and grasp the ant-trail network as a totality. Also, the artifacts they leave behind are physical entities which then provides the aggregate feedback to the aggregate ant body to then feed the evolution of the ant body as a CAS system. In contrast, humans do have the requisite cognitive abilities. The "pheromone trails" we leave behind are the knowledge trails coded in symbolic knowledge artifacts. In contrast to the physical artifacts that ants leave behind, the knowledge artifacts that we leave behind are far more flexible and potent, both at the aggregate as well as at the individual levels. But like the ants, until recently, we did not have the means to step back and map the knowledge "pheromone trails" to obtain the big picture and its global/local dynamics. The burgeoning field of scientometrics is making available visualization tools to help us map and study the evolutionary dynamics of the knowledge network structures.
 
''Data and Questions''
 
The goals of this project include
<ol>
<li>Extract the terms from approximately 1600 working papers published by SFI</li>
<li>Map the intra/inter conceptual network structures</li>
<li>Study the evolution of these structures across time</li>
<li>High-light the gap-closure of knowledge reverse-salients (if any)</li>
<li>Capture any of the network patterns that repeat</li>
<li>Study the diffusion of concepts across the network</li>
<li>Provide visualization tools for navigating the complexity corpus, etc</li>
</ol>
 
''Possible methods''
 
Latent Semantic Analysis (LSA) and Latent Document Analysis (LDA)
 
References:
<ol>
<li> SFI Working Papers: http://www.santafe.edu/research/working-papers/</li>
<li> Atlas-Science-Visualizing: http://www.amazon.com/Atlas-Science-Visualizing-What-Know/dp/0262014459</li>
<li> Atlas-Science-Visualizing WebSite: http://scimaps.org/atlas</li>
<li> Mapping-Scientific-Frontiers-Knowledge-Visualization: http://www.amazon.com/Mapping-Scientific-Frontiers-Knowledge-Visualization/dp/1447151275</li>
<li> Katy Börner presents at Science of Science: https://www.youtube.com/watch?v=pzCqGBNzomE</li>
<li> Scholarly Data, Network Science, and (Google) Maps:https://www.youtube.com/watch?v=vos5QBDywMM</li>
<li> LSA Video Lect: http://videolectures.net/slsfs05_hofmann_lsvm/</li>
<li> What is LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf</li>
<li> LSA Wiki:http://en.wikipedia.org/wiki/Latent_semantic_analysis</li>
<li> LDA: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm</li>
<li> Fusari, A. (2014). Methodological Misconceptions in the Social Sciences: Rethinking Social Thought and Social Processes: http://www.springer.com/us/book/9789401786744 </li>
<li> Kirsh, D. (2013). Thinking with external representations. Cognition Beyond the Brain: http://adrenaline.ucsd.edu/kirsh/Articles/Interaction/thinkingexternalrepresentations.pdf </li>
<li> Holland, J. H. (1992). Adaptation in natural and artificial systems: https://mitpress.mit.edu/index.php?q=books/adaptation-natural-and-artificial-systems </li>
<li> Where to start with text mining. http://tedunderwood.com/2012/08/14/where-to-start-with-text-mining/ </li>
<li> Singular Value Decomposition Tutorial https://www.ling.ohio-state.edu/~kbaker/pubs/Singular_Value_Decomposition_Tutorial.pdf </li>
<li> Latent Semantic Analysis (LSA) Tutorial: http://www.puffinwarellc.com/index.php/news-and-articles/articles/33-latent-semantic-analysis-tutorial.html </li>
<li> LSA in Detail: http://www2.denizyuret.com/ref/berry/berry95using.pdf </li>
<li> Web Based LSA: http://webapp1.dlib.indiana.edu/newton/lsa/index.php </li>
<li> Another Technique: t-Distributed Stochastic Neighbor Embedding (t-SNE): http://lvdmaaten.github.io/tsne/ </li>
<li> tf–idf: https://en.wikipedia.org/wiki/Tf%E2%80%93idf </li>

</ol>
 
'''Interested:'''
 
<ul>
<li> John Thomas (radjohn@live.com) </li>
<li> Haitao Shang (hts@mit.edu) </li>
<li> Christopher Verzijl (cjo.verzijl@gmail.com) </li>
<li> Anna Zaytseva (annza944@gmail.com) </li>
<li> Penny Mealy (penny.mealy@gmail.com) </li>
</ul>
 
 

==Navigating Music, Brain and The Edge of Chaos ‏==
''Context'' 
[[Navigating Music, Brain and The Edge of Chaos]]
 
Brain Sciences have revealed that we/it lives "on the edge of chaos" exhibiting "self-organized criticality" that is tentatively balanced between normalcy and madness. Over the course of history, humans have used various agents and activities to shape, influence and control this living-chaos ranging from substances such as caffeine, sugar, drugs etc., to activities such as the arts (including music), social-discourse/therapy, meditation etc. Of these, music has a distinct role in shaping our moods and helping us transition between different mental states, as well as maintain it for extended periods of time. Clearly we have been using music to help us control and shape the internal chaos. But until recently, the quantitative instrumentation of this massively complex system that comprises of close to a 100-billion neurons networked into a 1000-trillion synaptic edifice has not been available to the common man. But of late, affordable, wearable EEG's are available on the market, thus making the quantitative study of the influence of music in brain dynamics feasible on a large-scale/crowd-sourcing sense. To help come to terms with the complexity of our 1000-trillion synaptic edifice, we need to gather data on a vast scale. The proposed research is a proof-of-concept, exploratory foray into making this happen.
 

History of music in 5 min
https://www.youtube.com/watch?v=Gt2zubHcER4

'''Interested:'''
 
<ul>
<li> Sara Lumbreras (sara.lumbreras@gmail.com) </li>
<li> Ilaria b (bertazzi.ilaria@alice.it) </li>
<li> Braun, Urs (urs.braun@zi-mannheim.de) </li>
<li> John Thomas (radjohn@live.com) </li>
<li> Glenn Magerman (glenn.magerman@kuleuven.be) </li>
<li> Daniel Friedman (DanielAriFriedman@gmail.com)</li>
</ul>
 
 

==Powerlaw fitting and alternative distributions - Theory/statistics ==
Clauset, Shalizi and Newman (2009) propose a maximum likelihood method to estimate the powerlaw exponent of a variable of interest. This is a great improvement on earlier methods such as OLS that dominated the literature up to then. However, one can fit a powerlaw to any dataset and the most we can say is that our observations are consistent with the hypothesis that x is drawn from a powerlaw distribution. One easily implementable method to compare the powerlaw fit to other fits is then a likelihood ratio test for both models.

One particular discussion is the distinction between a powerlaw (PL) and a log-normal (LN) fit. For an avid discussion between both fits on city size and Zipf's law, see Eeckhout (2004, 2009) and Levy (2009), where the discussion now settled on city sizes following a log-normal distribution instead of a powerlaw. Similarly for the discussion on firm size distribution: Simon and Bonini, 1958; Ijri and Simon, 1977; Stanley et al., 1995; Sutton, 1997; Axtell, 2001; Okuyama et al., 1999; Cabral and Mata, 2003; Gaffeo et al., 2003; Aoyama et al., 2004; Fujiwara et al., 2004a,b; Kaizoji et al., 2006; Takayasu et al., 2008; Duchin and Levy, 2008; Schwarzkopf and Farmer, 2008, ...

This distinction matters for several reasons:
- PL and LN come from very similar model and differences in initial conditions can lead to very different outcomes. PL exhibits a choice for x_min, below which the unit of observation is not feasible to exist (eg minimum city size, firm size, word length, ...). LN has no minimum size.
- PL and LN that look similar are the difference between infinite (PL) and large but finite variance (LN)
- shock propagation: when unit-level shocks are large enough to show aggregate perturbances if the distribution is powerlaw with infinite variance, while these shocks wash out fast when the distribution is log-normal. (Gabaix, 1999; Gabaix 2009; Acemoglu et al. 2012, ...).

I have encountered some issues which I would like to explore further:
1. The distinction between lognormal and powerlaw in the data is very sensitive to data truncation: in the above discussions, researchers have slightly different datasets, covering more or less of the population at hand. Left-truncation (i.e. observations not in the dataset because they are too small to be reported) can strongly drive the outcome of the fit, even when endogenizing the x_min cutoff. I have data on the universe of Belgian firms, much more complete than e.g. US Census data, where I have done some preliminary tests on this. The question is then: how to formalise this distinction and what are the theoretical and practical caveats to look out for when applying this method.
2. MLE fitting seems to be sensitive to the choice of units as well: rescaling a variable by a factor 1000, 1000000 etc seems to influence the endogenous x_min choice and hence the estimated parameter. This reminds me of some work on scaling invariance in negative binomial estimators. What is going on here?
3. Can we set up a model that generalises both? I've been looking at Levy stable distributions, but did not do anything with it yet.

Interested people:
* Corbain
* Laura
*Binyang
*Sahil
*Tirtha
*Glenn
*Junming

Literature:
Powerlaw fitting in empirical data - Clauset, Shalizi and Newman (2009): http://arxiv.org/abs/0706.1062

==Organ Transplant Analysis==
Over 120,000 people in the United States are currently on the waiting list for an organ transplant. The size of this waiting list relates to over 6,000 deaths a year while waiting for a transplant and tens of billions of dollars in government spending. I have access to the following data sets:
 
* All transplants performed in the US from October 1987 to June 2014 (including follow-up data)
* All living and deceased organ donors in the same time period (with follow-up data on living donors)
* Waiting list data for everyone who signed up for the list
* 2012 National Survey on Attitudes and Behaviors on Organ Donation
* Social media data relating to organ donation since 2008

Open to ideas and suggestions for the topic, there are a lot of interesting questions to investigate including cultural/racial/gender differences in organ donation. I have several preliminary reports and exploratory analysis done on differences in donors.

Second Meeting Thursday, June 11th 4:15 in the coffee shop!

===Matching Game===
Rules for the matching game:

Objective: Create the largest chain possible using blood types in the room.

Follow the blood type guidelines for donation to form a donor chain.
*Assume that self-matches are not possible, for example someone with a donor of type AB and a recipient of type AB can not match themselves and needs to find someone else to complete their chain.
*Create a donor chain to help as many people as possible.
*Post results below for the prize
*There will be several "good samaritan" donors which will donate and need nothing in return, this is a great way to start your chain. Using a good samaritan chain also means you don't need a donor for the end point.

====Game Workspace====
Use this workspace to post for and find matches!

==== Donor Chains ====
Post your chain here. Example is given below:

=====Example Chain 1=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| A
|-
| Laura
| AB
| B
|-
| Matt
| A
| AB
|}

=====Example Chain 2=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| None
|-
| Laura
| A
| B
|-
| Matt
| None
| A
|}

Database size: 37. Used: 32, Unused: 5. Unused altruistic: 5. OPTIMAL

======Richard's Current Table======
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| X
|-
| MatthIn
| AB
| O
|-
| Susanne
| A
| AB
|-
| Michael
| O
| A
|-
| MatthewO
| AB
| O
|-
| Brent
| O
| AB
|-
| James
| AB
| O
|-
| Alejandro
| O
| AB
|-
| Glenn
| O
| A
|-
| AndySchauf
| A
| A
|-
| Richard
| A
| A
|-
| Maria
| O
| A
|-
| Keith
| AB
| O
|-
| Vanessa
| A
| AB
|-
| Carolina
| O
| A
|-
| Kleber
| O
| A
|-
| Sam
| O
| B
|-
| Masa
| A
| O
|-
| Cobain
| B
| A
|-
| Melissa
| A
| B
|-
| Jakub
| O
| A
|-
| Martina
| A
| O
|-
| Jae
| B
| A
|-
| Charon
| B
| B
|-
| Nilton
| O
| B
|-
| Alice
| AB
| O
|-
| Jelle
| O
| AB
|-
| MariePierre
| A
| O
|-
| Urs
| B
| A
|-
| Laurence
| O
| B
|-
| Chris
| O
| A
|-
| Havier
| AB
| O
|}

Unused:

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Jelle
| AB
| X
|-
| JGab
| O
| X
|-
| LauraCondon
| O
| X
|-
| Maggie
| O
| X
|-
| Matthew
| O
| X
|}

==Multi-dimensional social networks in the evolution, development and resilience of informal economies. ==
Informal economies are defined as economic activities that occur outside the purview of corporate public and private institutions. These types of economies proliferate where traditional economic actors are unable to productively exercise their activities, especially due to costly constraints (De Soto 2003). Firms may face adverse incentives to expand production by hiring more workers or incorporating more capital due to the low productivity of their workers or the predatory practices of rapacious elites or corrupt governments. Labor itself, i.e. people, may face hurdles in trying to make the jump towards entrepreneurial activities or jobs in the formal economy which allow the accumulation of experience, retirement savings, access to insurance or precautionary savings to face unexpected events (like disease, disability, etc.) due to lack of capital access (whether human and financial).

'''For this project, we propose a different interpretation: We seek to understand and model how multi-faceted social networks provide robust alternatives to formal economies, which far from being seen as degenerate forms of social organization, in many instances co-exist, challenge and compete with formal employment and economic activities. While some types of informal economies operate under adverse contexts, their resiliency may be understood as a type of adaptive fitness and not the mere result of stubborn cultural path-dependency. '''

For the most part, informal economies have been analysed as counterpoint to an ideal type of a formal economy with low levels of trust, respect of property rights, access to credit and public institutions - like the court systems (see Losby et al. 2002 for a review). However, informal economies have been coupled with their formal counterparts since the development of capitalism in the 19th Century. To name a famous historical example, Old London's East End's informal sector was described harshly by Engels (1844) in his famous tract, "The Condition of the Working Class in England". But almost fifty years later, in a new preface to the book, Engels recognized the progress that took place there under the aegis of working class organizations in the area.

Research has begun to catch on to this idea – developing a literature in the areas of informal risk sharing and remittances, which are in some sense informal counterparts to insurance and banking. Consumption patterns in poor, rural villages are remarkably smooth suggesting that risk-sharing measures are prevalent, although imperfect (Townsend). Field studies of networks underlying village risk sharing systems have found that households primarily receive help from existing social connections, such as friends and relatives, in the form of informal loans or transfers. Theoretical work in network economics, by authors such as Matt Jackson, found that certain empirically prevalent network structures may directly benefit the stability of favor exchange systems. Another line of inquiry focuses on remittance income, generally informal transfers across kinship ties. World Bank researchers have found that remittances from overseas migrants respond dramatically to regional income shocks, replacing upwards of 60% of lost income in households with international migrants. Further studies, cited below, have generalized those results. Furthermore, reductions in the cost of sending remittances, which occurs with the advent of mobile money, further mitigates exposure to risk in receiving households.

Also recently, authors have written on how communities come together in situations where formal economies are unavailable by external shocks (like disasters) or due to lack of access (due to conditions of abject poverty). In the case of the latter, Venkatesh (2008) provided gripping testimonies of how informal economies arose and developed in the South Side of Chicago in low trust environments via cash transactions and informal service contracts enforced by (and for the benefit of) local criminal gangs. These gangs were seen, surprisingly, as alternative coordinating mechanisms to settle claims between neighbors. In the case of the former, Storr and Grube (2013) in a series of papers has argued how "shared histories and perspectives, and the stability of social networks within the community" allows communities who suffered disasters to cope and endogenously resolve immediate and complex problems. Providing an example of these social networks in action, research has found that remittances flow quickly into areas affected by natural disasters when there is a technology in place for it to happen.

Taking these lines of inquiry together, the findings suggest that informal handling of risk takes place on a large scale and that social networks informally connect communities in ways that impact their economies. Recent trends suggest that the interaction of formal and informal economic processes is growing on a nationwide scale. First, the aggregate value of remittance flows is large and growing, nearing the value of foreign direct investment. Second, advancements that formalize previously informal transactions are expanding dramatically in emerging economies through various forms of branchless banking. National economies may be embedded in profoundly influential informal systems that have never been holistically studied.

On a more general note, this project touches lingering questions in economics. For example, some might argue that more stringent labor regulations should have increased informal employment, but in fact the opposite happened. And while the economic rationale for the former statement remains valid, we suggest that unions, as other types of social organizations, as examples of ways whereas people interact via organized networks, provide richer dimensions than those suggested by their interaction as mere economic agents. Hence, unions, as well as other types of faith-based, ethnic, community and other interest groups may provide ways of interaction that escape narrow economic outcomes.

====References====

• De Soto, Hernando (2000/2003) The Mystery of Capital. Basic Books.
• Losby, Jan; Else, John; Kingslow, Marcia; Edgcomb, Elaine; Malm, Erika and Vivian Kao (2002) The Informal Economy: A Literature Review. ISED Consulting and Research and the Aspen Institute Working Paper. http://www.kingslow-assoc.com/images/Informal_Economy_Lit_Review.pdf
• Townsend, Robert M. "Risk and Insurance in Village India." (1994)
• Fafchamps, Marcel, and Susan Lund. "Risk-sharing Networks in Rural Philippines." (2003)
• Weerdt, Joachim De, and Stefan Dercon. "Risk-sharing Networks and Insurance against Illness." (2006)
• Matthew O. Jackson, Tomas Rodriguez-Barraquer and Xu Tan. “Social Capital and Social Quilts: Network Patterns of Favor Exchange.” (2012)
• Yang D and H Choi."Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines"(2007)
• Kurosaki, Takashi. "Consumption vulnerability to risk in rural Pakistan." (2006)
• Jack, W, and T. Suri. "Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution” (2014)
• Engels, Friedrich (1844/1892) The Condition of the Working Class in England. http://www.gutenberg.org/files/17306/17306-h/17306-h.htm
• Venkatesh, Sudhir (2008) Gang Leader for a Day: A Rogue Sociologist Takes to the Streets. Penguin Books.
• Storr, Virgil and Laura Grube (2013) The Capacity for Self-Governance and Post-Disaster Resiliency. George Mason University, Department of Economics Working Paper 13-37. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2350830##
• Blumenstock, Joshua Evan and Fafchamps, Marcel and Eagle, Nathan. “Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda” (2011).
• Ratha, Dilip. "Workers’ remittances: an important and stable source of external development finance." (2005).
• Pénicaud, Claire, and Arunjay Katakam. State of the Industry 2013: Mobile Financial Services for the Unbanked. Rep. N.p.: GSMA MMU, 2013.

If interested, please list your name below.

'''Interested:'''
<li> Eloy Fisher (eloy.fisher@fulbrightmail.org) </li>
<li> Carolina Mattsson (carromattsson@gmail.com) </li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>

<li> Jakub Rojcek (jakub.rojcek@gmail.com) </li>

==Exploring Community Formation through Analysis of Scholarly Corpus (ArXiv)==

The Arxiv [http://arxiv.org/] is a free online repository of scientific preprint articles, mostly from physics and mathematics. It currently contains over 800,000 articles, dating back to 1991. Currently, a lot of the data associated with this repository is completely available: paper submission dates; full texts of papers; author names and coauthorship information. Additionally, there is some citations data available. (If necessary, I can also find papers' subdiscipline labels; submitting authors' email address domain names; other things.)

In the past I have been using these data to try and explore how communities of authors who have shared interests grow over time. For example, we can pinpoint the first ever paper about topological insulators, and search for all subsequent papers on that topic. As more papers are written, authors begin to join the field of research and to form strong ties by collaborating with one another. We can use the ArXiv data to visualize and analyze exactly how these communities form and grow.

===Brainstorming notes===
Isolation between topics, crossing interdisciplinary boundaries, finding (topological) separation distance between disciplines or research groups

Tipping points - can we look for separation of or mergers of two groups or disciplines?

Can we identify key papers (or groups of papers) that initiate ties between fields.

Sentiment analysis: can we identify when a paper's citation is an endorsement or a refutation?

Tools for Analysis: Change point detection; relational event models

Can we find more comprehensive/better citation data?

Lucene and indexing tools:
- Can we re-index our text database after removing stop words?
- Can we index the titles and abstracts?
- Elastic Search - tool for interacting with Lucene

This set of text-processing [http://alias-i.com/lingpipe/demos/tutorial/read-me.html tutorials] is pretty handy for background info and inspiration. The software package doesn't simply plug-into a Lucene/Solr/Elastic Search index, but it could be done. This package from [http://nlp.stanford.edu/software/ Standford] seems very capable.

===Next meeting===
Thursday @ 9AM in Coffee Shop

== PolComplex - Epistemic communities and policy dynamics in the UK Parliament ==

Over the last twenty years, there has been extensive debate about what the core topics in public policy are, and if they change according to type and number. Furthermore, it is still not clear how policy topics mutate and diffuse over time. Human-based text analysis of governmental documents has shed only some light on these research questions: 21 general topics have been proposed, while other accounts tend to restrict it to about five main clusters. Yet, this approach has not been able to devise a reliable and systemic method to capture topic dynamics, diffusion, and their relation to the human actors that talk about them.

This project is an extension of an exploratory research that intends to overcome such limitations, through the application of natural language processing (more specifically, dynamic topic modeling and sentiment analysis), topological and network analysis on a dataset containing UK House of Commons debates ranging from 1975 to 2014. The aim is manyfold. We hope to identify the structure and dynamics of epistemic policy communities - i.e. of political actors revolving around similar policy interest -, and how these relate to factors such as party membership, seniority, relevant historical events.

Some references:

[http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.734168 Taddy (2013), "Multinomial Inverse Regression for Text Analysis"] 

[https://www.cs.princeton.edu/~blei/papers/BleiLafferty2007.pdf Blei and Lafferty (2007), "A Correlated Topic Model of Science"] 

== Genomic variations from a chaotic mapping ==

Following Dabby CHAOS 6 (2), 1996 Musical variations from a chaotic mapping, this project will explore genomic variations in key metabolic genes that are known to be widely spread across bacterial phylogeny (e.g. the Nif cluster which is used in nitrogen fixation). There are several ways that genomic data can be treated as "music":

1) Nucleotide: 4 notes - A, G, C, T 
2) Amino Acids: 20 notes 
3) codon triplets: 64 notes 
4) tetra nucleotides: 256 notes. 

One possible goal is to take a seed sequence from some random organism and generate new variations. Then using homology search (e.g. BLAST) or phylogenetics, determine if that variation exists in nature? If not, perhaps model the potential protein folding (if possible?) to determine if that variation could exist in nature. This is VERY preliminary and can go in many directions.

If interested please list name below or contact Jarrod at jscott@bigelow.org

Reading
http://digitalcommons.olin.edu/cgi/viewcontent.cgi?article=1000&context=ahse_pub&sei-redir=1&referer=https%3A%2F%2Fscholar.google.com%2Fscholar%3Fhl%3Den%26q%3Ddabby%2Bchaos%26btnG%3D%26as_sdt%3D1%252C32%26as_sdtp%3D#search=%22dabby%20chaos%22

== Free Energy Theory ==
The Free Energy (FE) minimisation framework tries to explain how biological systems (such as a cell or a brain) self-organise in order to occupy the (often very limited number of) non-equilibrium states that minimise free energy. This is also known as active inference. A simple corollary of active inference is that agents behave as to minimise their prediction error, or the difference between prediction and reality. Thermodynamic free energy is a measure of energy available in a system to do useful work. This can be framed in an information theoretic setting, as the difference between how the world is being represented and how it actually is. A better fit means a lower information-theoretic free energy, as more resources are being put to ‘good use’ in representing the world. The overarching logic of FE theory is that a better model of the world help maintain structure and organisation, which ultimately helps the system resist increases in entropy.
 
 
This is not a set-in-stone project with any concrete aim (yet). We are a few people interested in exploring the theoretical and practical implications of these ideas, and you're more than welcome to join in!
 
 
'''Interested:'''
 
<ul>
<li> Jelle Bruineberg (j.bruineberg@gmail.com ) </li>
<li> Tobias Morville (tobiasmorville@gmail.com) </li>
<li> Susanne Pettersson (guspsusa85@student.edu.se) </li>
<li> Maggie Simon (margaret.w.simon@gmail.com) </li>
<li> Martina Steffen (martinasemail@gmail.com) </li>
<li> Sahil (sahilgar@usc.edu) </li>
<li> Tirtha (Tirtha.Bandy@csiro.au) </li>
<li> Anna (annza944@gmail.com) </li>
<li> Matt O (mmosmond@gmail.com) </li>
<li> Will Chang (williamkurtischang at gmail dot com) </li>
<li> Cobain (mrdc1g10@soton.ac.uk) </li>
</ul>
Reading:
 
http://rsif.royalsocietypublishing.org/content/10/86/20130475 
http://arxiv.org/abs/1503.04187 
http://www.mdpi.com/1099-4300/15/1/311 

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
'Carolina'
'Alice'
'Federico'
'Jean Gab'
'Sola'
'Ilaria'
'Daniel T'

==Comparison of Network vs Scaling Theory based Models in Ecology (Cobain - mrdc1g10@soton.ac.uk)==

One of the biggest difficulties ecologists face is trying to understand the ecosystem dynamics based on very little biological information (compared to the size of the system) - observational data is logistically difficult and can be very expensive to acquire, as well as often being very time consuming. In terms of modelling, to overcome this problem, two approaches have been utilised. 

The first is a network based approach whereby the nodes represent biomass density of a particular species (or a higher level taxanomic group and/or resources) and edges as the trophic interactions (who eats who). This method depends on what we know about the trophic behaviour of the species involved (which is often very limited and there can be many species to parameterise), and represents only the central tendency of what could potentially be very diverse behaviour within and between populations. 

The second approach is to use scaling theory to describe average trophic interactions and other biological processes based on individual organism body size along the size continuum, viewing the community as a very size structured dynamical system. This requires less specific knowledge about the organisms in the community ''per se'', however this again represents only the central tendency of a given size class of what could be very diverse behaviour. Functional differences can be potentially introduced (coupled benthic-pelagic systems for example), but to the detriment of braking down the predictability of the well known allometric scaling laws with size as specificity increases. 

There has been little to no comparison of these two methods of modelling ecological systems (at least to my knowledge) and the question arises that, given the same starting information, how well do both approaches model the dynamics of an ecosystem, given the limited biological information we have? Is one approach better at capturing energy flow through the system / community structure and stability etc. compared to the other? If the models vary then such information will better inform ecologists on which to use depending on what type of questions they are trying to answer. 

Therefore, the project proposed here aims to investigate these two methods. The community that is used to seed an experimental mesocosm setup (mesocosms are large tank set-ups designed to reflect the complexity of natural ecosystems but still being able to be artificially controlled and are still relatively simple), will be input into two models based on the separate approaches and then run to steady state. The model outputs will then be compared and contrasted with eachother as well as the mesocosm community at steady state. Further work may include examining perturbation dynamics, dependent on what data we have available.

'''Interested: Name / Email''' 
Martina Steffen (martinasemail@gmail.com)

==Dynamics of homicide (Matthew Ingram - mingram@albany.edu)==

Integrating temporal, spatial, and multi-level concepts

1:30pm in the coffee shop

Am interested in the discussion on this project...Sola Omoju

Interested - Nilton Cardoso

==Multiplex Adaptive Networks (Daniel - dtc65@cornell.edu)==

7 pm in the coffee shop

==Modeling brain diseases (or cancerous bio pathways) (Sahil - sahilgar@usc.edu)==
Interested people can put a meeting time as per their convenience here.
 
No meeting time indicated
 
One potential meeting time can be 3pm in coffee shop ?
 
Information theoretic algorithms can also be explored for the problem.
 
Discovering Structure in High-Dimensional Data Through Correlation Explanation. http://papers.nips.cc/paper/5580-positive-curvature-and-hamiltonian-monte-carlo
 
Maximally Informative Hierarchical Representations
of High-Dimensional Data. http://jmlr.org/proceedings/papers/v38/versteeg15.pdf

 
'''Interested:'''
 
<ul>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> Laura Condon (lcondon@mymail.mines.edu)</li>
</ul>
 
 

==Analysis of UK parliament speeches 1935-2014 (Stefano - etstefano@gmail.com)==

No meeting time indicated

==Mapping Complexity/Human Knowledge as a Complex Adaptive System (radjohn@live.com)==
2pm/Wed 6/10 in Conference Room (Tentative)

==Navigating Music, Brain and The Edge of Chaos (radjohn@live.com)==
10:45am/Wed 6/10 in Conference Room
9:00am/Thu 7/10 at the Coffee Shop

'''Interested:'''
Sahil Garg

==Analysis of rule-based modeling for dopamine-dependent synaptic plasticity (emilia.m.wysocka@gmail.com)==

----------------------------------------------------------------------------------
Modeling and analysis of phenomenons and evolution of stochastic, combinatorially complex signalling systems in a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system).

----------------------------------------------------------------------------------
Rule-based modeling features:

*biological systems as concurrent processes
*dynamics of post-translational modifications
*domain availability
*competitive binding
*causality and intrinsic structure
*binding sites
*interaction rules replace reaction equations
*infinite number of reactions with a small and finite number of rules
*reduction of parameter space
*“don't care don't write” - adjustable rule contextualization
*single reaction rule and parameters generalize classes of multiple rules
*modular and extensible language
*specification language & simulation/integration environment
*static and causal analysis
*Kappa/KaSim & BioNetGen/NFsim -- specification language/network-free simulator

----------------------------------------------------------------------------------
----------------------------------------------------------------------------------
Meetings:
----------------------------------------------------------------------------------
* WEDNESDAY 11/06/2015

Addressing problems in terms of the other aspects of the projects apart from the biological questions and verification (matching results to some published experiments or known behaviour). So my plan is as follows:

* divide the group (roughly) into people who look in to biological meaning and validation and the one which tries to do the analysis of system phenomenons and evolution of stochastic, combinatorially complex signalling systems in both a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system) - all possible states, scenarios of the system abstracted from the biological meaning.

Things that could be modelled (look in dropbox folder: https://www.dropbox.com/sh/g080w60pt2vgi7v/AACHlMf2JwpP2EZqKq7Di6N2a/possible%20models?dl=0):

* to make things easier and have the modeling part done quickly- base the model on the dopamine-related synaptic plasticity (SBML ODE model): http://www.ebi.ac.uk/biomodels-main/MODEL1101170000;

* I found a series of papers suitable to the subject of the Complex Systems course, e.g.: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC344803/. I was thinking to even try to compare higher level brain data (networks, spiking ..) with purely molecular model

* noise and low copy number (http://www.princeton.edu/~wbialek/our_papers/bialek+setayeshgar_08.pdf); this is quite interesting as for sufficiency of binding events -> http://www.princeton.edu/~wbialek/our_papers/bialek+ranganathan_07.pdf

* spontaneous flipping of interactions (phosphorylations and others) between proteins, described by the god of non-linear dynamics (S. Strogatz)

----------------------------------------------------------------------------------
* THURSDAY 11/06/2015 : http://doodle.com/se23ibwtkrkccs4s

----------------------------------------------------------------------------------
Some refs:
*https://www.dropbox.com/sh/g080w60pt2vgi7v/AADUy7Ge-J-oSYkyTeYOo7V8a?dl=0)
*http://www.pps.univ-paris-diderot.fr/~jkrivine/homepage/Teaching.html

==Ecology non-working group (williamkurtischang at gee-mail dot com)==
[[Informal ecology interest group]].

==Making Supply Chains Resilient to Disasters (mh2905 att columbia dott edu)==
'''Key words''': resilience, natural hazards, supply chains, interdependency, interconnected risks, cascading failures

'''Rational''': I am interested in examining what kinds of network structures and features contribute to increasing resilience of supply chains to natural disasters. I believe this area of work is important because regional disasters negatively impact the global economy through disruptions in supply chain networks. The pioneer study published in Nature urges the need for making supply chains climate-smart (Levermann 2014). Also in the industry, the World Economic Forum published a report to address this issue in 2013. Few researches, however, assess and model the impacts of adverse weather on supply chains. I would like to evaluate the impacts based on modeling.

'''Data set''': Supply chains data of a multinational manufacturing company.Global climate data, disaster data, etc.

'''Possible techniques''': Agent-Based Model, Complexity science, network theory and evolution, complex adaptive systems, GIS, operations research, manufacturing engineering, and I am open to any techniques.

'''Myself''': As I joined the program yesterday, please find my background here.
http://tuvalu.santafe.edu/events/workshops/index.php/Masahiko_Haraguchi

'''Please put your names below if you want to be informed about this project by email'''

'''Interested''':
Masa Haraguchi

==From Ethnic Diversity to Religious Zeal: Retrospective/Predictive Construction of the World Ethnic Map (vdzutsati@asu.edu)==

There is a new dataset on ethnic groups (2014), which claims to include all ethnic groups of the world http://www.cidcm.umd.edu/mar/amar_project.asp. There are also several cross national datasets on religion and other variables of interest, normally, socio-economic http://www.thearda.com/Archive/CrossNational.asp. Coming from social sciences I thought it would be really interesting to apply methods and perspectives from other disciplines to social science data.

One idea that I have in mind is to explore how ethnicity and religion interact.

Quick empirical checks indicate that Subsaharan Africa is home to about 1,500 ethnic and subethnic groups, in comparison to about 90 ethnic groups in the Middle East and North Africa. India alone accounts for nearly 2,000 ethnic and subethnic groups, while Europe, including all the countries of the former USSR and large immigrant groups from other parts of the world, has only about 260 ethnic groups. The picture that emerges from this simple comparison is that the spread of Abrahamic religions appears to be associated with the high depletion rate of ethnic groups. The exception of China, which has little more than 50 ethnic groups, can be explained by the country’s long history of a centralized state.

The idea then is to assume that we have had the control group of nations that did not experience the influx of Abrahamic religions (or more precisely received limited exposure to them) and the experimental group that was exposed to the spread of Abrahamic religions. Since we know what the distribution of ethnic groups in the control group is we can project what the distribution of ethnic groups in the experimental group would be, if the group were not exposed to Abrahamic religions.

Of course, we could go in the other direction too and make a projection about future – what would happen to ethnic groups if all of them adopted an Abrahamic religion.

One of the challenges in this project is absence of a dynamical system, I.e. We don’t have data on how these things changed over time, but I still think that projection about the future and the past would be kinda cool:)

'''Interested''':

==Nationalist vs religious rebel violence (vdzutsati@asu.edu)==

I would like to propose yet another possible project. This time it’s on rebel violence. I have a large dataset on rebel (and government) violence (~1 million observations). The paper that used this data was published couple of months ago (Islamists and Nationalists: Rebel Motivation and Counterinsurgency in Russia’s North Caucasus). The dataset contains event counts, GIS data, demographic and economic characteristics and some other stuff. The primary hunch of the paper was to discover the differences between nationalist and Islamist rebels.

Given all the cool methods we are learning here, my first instinct is to use the methods and tools (TISEAN?) to explore and discover whether nationalist violence is substantively different from religious violence and try to answer why.

One problem that I can see is that the event coding was done by a machine, so this may have “contaminated” the data. But if both religious and nationalist data were contaminated equally then the difference should still be evident.

'''Interested''':

==[[Archived Projects ]]==

Complex Systems Summer School 2015-Projects & Working Groups

2015-07-02T19:37:26Z

Rbarnes: /* Richard's Current Table */

{{Complex Systems Summer School 2015}}

==[[Reservations for Evans Science rm. 215]]==

Click on the title link to reserve Evans Science rm. 215 for group usage.

==Californian Drought Model==
Problem definition:
Water is an ongoing problem in California. Although corrective measures are taken for some years, periods of droughts are depleting water ground levels to not sustainable levels.
Aims:
A Netlogo simulation on how likely Californian agents evolve with regards to drought. The ABM could be used to explore the potential impacts of forms of regulation.
Outcomes:
Create an exploratory agent based model based on real data, enabling the possibility of simulating different methods of regulation, including feedbacks between the economic and hydrologic systems.

Extension 1: We will conduct a network analysis of Twitter data (hashtags #drought #California) to explore the social dynamics and information flows between stakeholders.

Extension 2: Phase-space reconstruction of groundwater level time series data, comparing the dynamics in different parts of the Central Valley.

==City Resilience==
'''Summary:''' This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities. 
'''Contact:''' Richard Barnes (rbarnes@umn.edu) 
'''Participants:''' Alex Tejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang, Martina Steffen, Juan Carlos Castilla, Brent Schneeman 
'''Wiki page:''' [[Resilience of Cities]]

==Complex Adaptive Systems and the Narrative approach: transdisciplinary methodologies for Complexity Science==
The narrative approach and it causality is different from causality in logico-scientific approaches. Making bridges among methodologies transfers knowledge, encourages important questions and switches philosophical paradigms. Formal frameworks of C(A)S can realy be complemented with the narrative, and actually should. The narrative approaches in Complexity Science are an emerging trend for fund-granting, and is really up to date to process documentary and speech-based data, reveal hidden meanings, distinguish causes from effects in overlapping systems of realms - "at the edge" of technology, social, economic, scientific, legal and other domains. The role of time in coding, intuitively generated conditional rules and computational paths. Combinations of C(A)S and the narrative is combination of quantitative and qualitative data and thinking in original - out of convertation and information loss. Synthesis of sciences is what it is about.

'''Questions''':
How to combine CS approaches (in particular CAS) and the narrative ones in a rule-based way? What to start from? What vizualizations there can be designed and used? Other questions at your descrete.

'''Some references''':
- Non-Equilibrium Social Science in ICT and Economics, CORDIS, EU: http://cordis.europa.eu/project/rcn/102344_en.html, http://www.nessnet.eu/

- Combining Complexity Theory with Narrative Research: https://youtu.be/pHjeFFGug1Y

- Haridimos Tsoukas and Mary Jo Hatch (2001), "Complex thinking, complex practice: The case for a narrative approach to organizational complexity: http://www.brown.uk.com/teaching/qualitativepostgrad/tsoukas.pdf

- David Christian, "Big History", Astrophysics, Chemistry, Biology, Information, emergence of life, technology: https://youtu.be/GlmvFjFceD8

'''Contact''': Anna (annza944@gmail.com)

Meet on Monday over lunch

'''Interested''': Marie Pierre, Jeroen, Jim, Melissa

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
 
'Carolina'
 
'Alice'
 
'Federico'
 
'Jean Gab'
 
'Sola'
 
'Ilaria'
 
'Daniel T'
 
'Sahil Garg

==Ebola==
The 2014-15 Ebola virus disease (EVD) outbreak in West Africa presented both unique opportunities and unique challenges to the epidemiological modeling community. For the first time during an emerging infectious disease outbreak, high resolution data--from a variety of sources--were made available to the academic community and many public health decision makers genuinely engaged with mathematical and computational modelers. However, the popular and scientific press were highly critical of most models ability to project the outbreak's course. The following key and open questions seem ripe for investigation using a complex adaptive systems lens:
 
1) What features of EVD transmission are most problematic for reliable, robust forecasting: changing behavior, intervention, viral evolution, complex social networks, etc?
 
2) How/can we use digital data to either improve forecasts or inform model selection?
 
3) Can one quantify the value of additional information in real-time?
 
'''Contact:''' Samuel Scarpino, SFI Omidyar Fellow, Santa Fe Institute - scarpino@santafe.edu
 
 
'''Interested:'''
 
Marie-Pierre Hasne 
Chris Verzijl 
Junming Huang 
'Sola Omoju 
Christine Harvey 
Daniel Citron (dtc65@cornell.edu) 
William Chang (williamkurtischang at gee-mail)

==Effect of landscape topography on vegetation connectivity and navigability==
''Context''
 
Landscape topography influences the dynamics of the processes that take place on it. Evolution of ecosystems networks, river networks, vegetation cover type, microclimate cycles are all interlinked deeply to the local landscape topography.

In addition to the landscape these networks are also influenced by each other conditioning the emergence and stability of ecosystems, and subsequently the behaviour of agents in the ecosystem like migration pathways of animal herds, human settlement patterns etc.

Connectivity patterns emerge from the interaction of these processes, and thus a better understanding and quantification of those patterns is critical to understanding the dynamics of the system.

 
''How do we want to approach the problem''
 
In this project, we will randomly generate landscape topography and subsequent vegetation cover from a set of parameters from known geological and biological processes. The generated data set will then be used to investigate the following questions:
 
(1) What is the connectivity of landscape patterns that emerge at different scales using different techniques such as clustering analysis, percolation theory or network theory, and can we quantify them ?
 
(2) What is the navigability of biological agents (e.g animals, humans, robots!) under such landscape patterns. We can compare mobility trails in existing landscapes to validate our hypothesis. We propose to use the tools like ABMs to simulate and characterise mobility success.
 
(3) We further aim to compare the measures of navigability with the metrics of connectivity, establishing a framework of comparison.
 

''Contact:''
 tirtha.bandy@csiro.au
 alej.tejedor@gmail.com

''Meetings''
(1) Wednesday 1pm Coffee shop

People Interested 
Martina

==[[Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe]]==
Click on the link to go to the project page for meetings, project details, and progress.
 
contact: mveitzel@ucsc.edu
 

==Mapping Complexity/Human Knowledge as a Complex Adaptive System‏==
''Context''
 
Ants leave pheromone trail patterns which they are aware of only in a local sense. They do not have the cognitive faculties to step back and look at the trails and grasp the ant-trail network as a totality. Also, the artifacts they leave behind are physical entities which then provides the aggregate feedback to the aggregate ant body to then feed the evolution of the ant body as a CAS system. In contrast, humans do have the requisite cognitive abilities. The "pheromone trails" we leave behind are the knowledge trails coded in symbolic knowledge artifacts. In contrast to the physical artifacts that ants leave behind, the knowledge artifacts that we leave behind are far more flexible and potent, both at the aggregate as well as at the individual levels. But like the ants, until recently, we did not have the means to step back and map the knowledge "pheromone trails" to obtain the big picture and its global/local dynamics. The burgeoning field of scientometrics is making available visualization tools to help us map and study the evolutionary dynamics of the knowledge network structures.
 
''Data and Questions''
 
The goals of this project include
<ol>
<li>Extract the terms from approximately 1600 working papers published by SFI</li>
<li>Map the intra/inter conceptual network structures</li>
<li>Study the evolution of these structures across time</li>
<li>High-light the gap-closure of knowledge reverse-salients (if any)</li>
<li>Capture any of the network patterns that repeat</li>
<li>Study the diffusion of concepts across the network</li>
<li>Provide visualization tools for navigating the complexity corpus, etc</li>
</ol>
 
''Possible methods''
 
Latent Semantic Analysis (LSA) and Latent Document Analysis (LDA)
 
References:
<ol>
<li> SFI Working Papers: http://www.santafe.edu/research/working-papers/</li>
<li> Atlas-Science-Visualizing: http://www.amazon.com/Atlas-Science-Visualizing-What-Know/dp/0262014459</li>
<li> Atlas-Science-Visualizing WebSite: http://scimaps.org/atlas</li>
<li> Mapping-Scientific-Frontiers-Knowledge-Visualization: http://www.amazon.com/Mapping-Scientific-Frontiers-Knowledge-Visualization/dp/1447151275</li>
<li> Katy Börner presents at Science of Science: https://www.youtube.com/watch?v=pzCqGBNzomE</li>
<li> Scholarly Data, Network Science, and (Google) Maps:https://www.youtube.com/watch?v=vos5QBDywMM</li>
<li> LSA Video Lect: http://videolectures.net/slsfs05_hofmann_lsvm/</li>
<li> What is LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf</li>
<li> LSA Wiki:http://en.wikipedia.org/wiki/Latent_semantic_analysis</li>
<li> LDA: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm</li>
<li> Fusari, A. (2014). Methodological Misconceptions in the Social Sciences: Rethinking Social Thought and Social Processes: http://www.springer.com/us/book/9789401786744 </li>
<li> Kirsh, D. (2013). Thinking with external representations. Cognition Beyond the Brain: http://adrenaline.ucsd.edu/kirsh/Articles/Interaction/thinkingexternalrepresentations.pdf </li>
<li> Holland, J. H. (1992). Adaptation in natural and artificial systems: https://mitpress.mit.edu/index.php?q=books/adaptation-natural-and-artificial-systems </li>
<li> Where to start with text mining. http://tedunderwood.com/2012/08/14/where-to-start-with-text-mining/ </li>
<li> Singular Value Decomposition Tutorial https://www.ling.ohio-state.edu/~kbaker/pubs/Singular_Value_Decomposition_Tutorial.pdf </li>
<li> Latent Semantic Analysis (LSA) Tutorial: http://www.puffinwarellc.com/index.php/news-and-articles/articles/33-latent-semantic-analysis-tutorial.html </li>
<li> LSA in Detail: http://www2.denizyuret.com/ref/berry/berry95using.pdf </li>
<li> Web Based LSA: http://webapp1.dlib.indiana.edu/newton/lsa/index.php </li>
<li> Another Technique: t-Distributed Stochastic Neighbor Embedding (t-SNE): http://lvdmaaten.github.io/tsne/ </li>
<li> tf–idf: https://en.wikipedia.org/wiki/Tf%E2%80%93idf </li>

</ol>
 
'''Interested:'''
 
<ul>
<li> John Thomas (radjohn@live.com) </li>
<li> Haitao Shang (hts@mit.edu) </li>
<li> Christopher Verzijl (cjo.verzijl@gmail.com) </li>
<li> Anna Zaytseva (annza944@gmail.com) </li>
<li> Penny Mealy (penny.mealy@gmail.com) </li>
</ul>
 
 

==Navigating Music, Brain and The Edge of Chaos ‏==
''Context'' 
[[Navigating Music, Brain and The Edge of Chaos]]
 
Brain Sciences have revealed that we/it lives "on the edge of chaos" exhibiting "self-organized criticality" that is tentatively balanced between normalcy and madness. Over the course of history, humans have used various agents and activities to shape, influence and control this living-chaos ranging from substances such as caffeine, sugar, drugs etc., to activities such as the arts (including music), social-discourse/therapy, meditation etc. Of these, music has a distinct role in shaping our moods and helping us transition between different mental states, as well as maintain it for extended periods of time. Clearly we have been using music to help us control and shape the internal chaos. But until recently, the quantitative instrumentation of this massively complex system that comprises of close to a 100-billion neurons networked into a 1000-trillion synaptic edifice has not been available to the common man. But of late, affordable, wearable EEG's are available on the market, thus making the quantitative study of the influence of music in brain dynamics feasible on a large-scale/crowd-sourcing sense. To help come to terms with the complexity of our 1000-trillion synaptic edifice, we need to gather data on a vast scale. The proposed research is a proof-of-concept, exploratory foray into making this happen.
 

History of music in 5 min
https://www.youtube.com/watch?v=Gt2zubHcER4

'''Interested:'''
 
<ul>
<li> Sara Lumbreras (sara.lumbreras@gmail.com) </li>
<li> Ilaria b (bertazzi.ilaria@alice.it) </li>
<li> Braun, Urs (urs.braun@zi-mannheim.de) </li>
<li> John Thomas (radjohn@live.com) </li>
<li> Glenn Magerman (glenn.magerman@kuleuven.be) </li>
<li> Daniel Friedman (DanielAriFriedman@gmail.com)</li>
</ul>
 
 

==Powerlaw fitting and alternative distributions - Theory/statistics ==
Clauset, Shalizi and Newman (2009) propose a maximum likelihood method to estimate the powerlaw exponent of a variable of interest. This is a great improvement on earlier methods such as OLS that dominated the literature up to then. However, one can fit a powerlaw to any dataset and the most we can say is that our observations are consistent with the hypothesis that x is drawn from a powerlaw distribution. One easily implementable method to compare the powerlaw fit to other fits is then a likelihood ratio test for both models.

One particular discussion is the distinction between a powerlaw (PL) and a log-normal (LN) fit. For an avid discussion between both fits on city size and Zipf's law, see Eeckhout (2004, 2009) and Levy (2009), where the discussion now settled on city sizes following a log-normal distribution instead of a powerlaw. Similarly for the discussion on firm size distribution: Simon and Bonini, 1958; Ijri and Simon, 1977; Stanley et al., 1995; Sutton, 1997; Axtell, 2001; Okuyama et al., 1999; Cabral and Mata, 2003; Gaffeo et al., 2003; Aoyama et al., 2004; Fujiwara et al., 2004a,b; Kaizoji et al., 2006; Takayasu et al., 2008; Duchin and Levy, 2008; Schwarzkopf and Farmer, 2008, ...

This distinction matters for several reasons:
- PL and LN come from very similar model and differences in initial conditions can lead to very different outcomes. PL exhibits a choice for x_min, below which the unit of observation is not feasible to exist (eg minimum city size, firm size, word length, ...). LN has no minimum size.
- PL and LN that look similar are the difference between infinite (PL) and large but finite variance (LN)
- shock propagation: when unit-level shocks are large enough to show aggregate perturbances if the distribution is powerlaw with infinite variance, while these shocks wash out fast when the distribution is log-normal. (Gabaix, 1999; Gabaix 2009; Acemoglu et al. 2012, ...).

I have encountered some issues which I would like to explore further:
1. The distinction between lognormal and powerlaw in the data is very sensitive to data truncation: in the above discussions, researchers have slightly different datasets, covering more or less of the population at hand. Left-truncation (i.e. observations not in the dataset because they are too small to be reported) can strongly drive the outcome of the fit, even when endogenizing the x_min cutoff. I have data on the universe of Belgian firms, much more complete than e.g. US Census data, where I have done some preliminary tests on this. The question is then: how to formalise this distinction and what are the theoretical and practical caveats to look out for when applying this method.
2. MLE fitting seems to be sensitive to the choice of units as well: rescaling a variable by a factor 1000, 1000000 etc seems to influence the endogenous x_min choice and hence the estimated parameter. This reminds me of some work on scaling invariance in negative binomial estimators. What is going on here?
3. Can we set up a model that generalises both? I've been looking at Levy stable distributions, but did not do anything with it yet.

Interested people:
* Corbain
* Laura
*Binyang
*Sahil
*Tirtha
*Glenn
*Junming

Literature:
Powerlaw fitting in empirical data - Clauset, Shalizi and Newman (2009): http://arxiv.org/abs/0706.1062

==Organ Transplant Analysis==
Over 120,000 people in the United States are currently on the waiting list for an organ transplant. The size of this waiting list relates to over 6,000 deaths a year while waiting for a transplant and tens of billions of dollars in government spending. I have access to the following data sets:
 
* All transplants performed in the US from October 1987 to June 2014 (including follow-up data)
* All living and deceased organ donors in the same time period (with follow-up data on living donors)
* Waiting list data for everyone who signed up for the list
* 2012 National Survey on Attitudes and Behaviors on Organ Donation
* Social media data relating to organ donation since 2008

Open to ideas and suggestions for the topic, there are a lot of interesting questions to investigate including cultural/racial/gender differences in organ donation. I have several preliminary reports and exploratory analysis done on differences in donors.

Second Meeting Thursday, June 11th 4:15 in the coffee shop!

===Matching Game===
Rules for the matching game:

Objective: Create the largest chain possible using blood types in the room.

Follow the blood type guidelines for donation to form a donor chain.
*Assume that self-matches are not possible, for example someone with a donor of type AB and a recipient of type AB can not match themselves and needs to find someone else to complete their chain.
*Create a donor chain to help as many people as possible.
*Post results below for the prize
*There will be several "good samaritan" donors which will donate and need nothing in return, this is a great way to start your chain. Using a good samaritan chain also means you don't need a donor for the end point.

====Game Workspace====
Use this workspace to post for and find matches!

==== Donor Chains ====
Post your chain here. Example is given below:

=====Example Chain 1=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| A
|-
| Laura
| AB
| B
|-
| Matt
| A
| AB
|}

=====Example Chain 2=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| None
|-
| Laura
| A
| B
|-
| Matt
| None
| A
|}

======Richard's Current Table======
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| X
|-
| MatthIn
| AB
| O
|-
| Susanne
| A
| AB
|-
| Michael
| O
| A
|-
| MatthewO
| AB
| O
|-
| Brent
| O
| AB
|-
| James
| AB
| O
|-
| Alejandro
| O
| AB
|-
| Glenn
| O
| A
|-
| AndySchauf
| A
| A
|-
| Richard
| A
| A
|-
| Maria
| O
| A
|-
| Keith
| AB
| O
|-
| Vanessa
| A
| AB
|-
| Carolina
| O
| A
|-
| Kleber
| O
| A
|-
| Sam
| O
| B
|-
| Masa
| A
| O
|-
| Cobain
| B
| A
|-
| Melissa
| A
| B
|-
| Jakub
| O
| A
|-
| Martina
| A
| O
|-
| Jae
| B
| A
|-
| Charon
| B
| B
|-
| Nilton
| O
| B
|-
| Alice
| AB
| O
|-
| Jelle
| O
| AB
|-
| MariePierre
| A
| O
|-
| Urs
| B
| A
|-
| Laurence
| O
| B
|-
| Chris
| O
| A
|-
| Havier
| AB
| O
|}

Unused:

{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Jelle
| AB
| X
|-
| JGab
| O
| X
|-
| LauraCondon
| O
| X
|-
| Maggie
| O
| X
|-
| Matthew
| O
| X
|}

==Multi-dimensional social networks in the evolution, development and resilience of informal economies. ==
Informal economies are defined as economic activities that occur outside the purview of corporate public and private institutions. These types of economies proliferate where traditional economic actors are unable to productively exercise their activities, especially due to costly constraints (De Soto 2003). Firms may face adverse incentives to expand production by hiring more workers or incorporating more capital due to the low productivity of their workers or the predatory practices of rapacious elites or corrupt governments. Labor itself, i.e. people, may face hurdles in trying to make the jump towards entrepreneurial activities or jobs in the formal economy which allow the accumulation of experience, retirement savings, access to insurance or precautionary savings to face unexpected events (like disease, disability, etc.) due to lack of capital access (whether human and financial).

'''For this project, we propose a different interpretation: We seek to understand and model how multi-faceted social networks provide robust alternatives to formal economies, which far from being seen as degenerate forms of social organization, in many instances co-exist, challenge and compete with formal employment and economic activities. While some types of informal economies operate under adverse contexts, their resiliency may be understood as a type of adaptive fitness and not the mere result of stubborn cultural path-dependency. '''

For the most part, informal economies have been analysed as counterpoint to an ideal type of a formal economy with low levels of trust, respect of property rights, access to credit and public institutions - like the court systems (see Losby et al. 2002 for a review). However, informal economies have been coupled with their formal counterparts since the development of capitalism in the 19th Century. To name a famous historical example, Old London's East End's informal sector was described harshly by Engels (1844) in his famous tract, "The Condition of the Working Class in England". But almost fifty years later, in a new preface to the book, Engels recognized the progress that took place there under the aegis of working class organizations in the area.

Research has begun to catch on to this idea – developing a literature in the areas of informal risk sharing and remittances, which are in some sense informal counterparts to insurance and banking. Consumption patterns in poor, rural villages are remarkably smooth suggesting that risk-sharing measures are prevalent, although imperfect (Townsend). Field studies of networks underlying village risk sharing systems have found that households primarily receive help from existing social connections, such as friends and relatives, in the form of informal loans or transfers. Theoretical work in network economics, by authors such as Matt Jackson, found that certain empirically prevalent network structures may directly benefit the stability of favor exchange systems. Another line of inquiry focuses on remittance income, generally informal transfers across kinship ties. World Bank researchers have found that remittances from overseas migrants respond dramatically to regional income shocks, replacing upwards of 60% of lost income in households with international migrants. Further studies, cited below, have generalized those results. Furthermore, reductions in the cost of sending remittances, which occurs with the advent of mobile money, further mitigates exposure to risk in receiving households.

Also recently, authors have written on how communities come together in situations where formal economies are unavailable by external shocks (like disasters) or due to lack of access (due to conditions of abject poverty). In the case of the latter, Venkatesh (2008) provided gripping testimonies of how informal economies arose and developed in the South Side of Chicago in low trust environments via cash transactions and informal service contracts enforced by (and for the benefit of) local criminal gangs. These gangs were seen, surprisingly, as alternative coordinating mechanisms to settle claims between neighbors. In the case of the former, Storr and Grube (2013) in a series of papers has argued how "shared histories and perspectives, and the stability of social networks within the community" allows communities who suffered disasters to cope and endogenously resolve immediate and complex problems. Providing an example of these social networks in action, research has found that remittances flow quickly into areas affected by natural disasters when there is a technology in place for it to happen.

Taking these lines of inquiry together, the findings suggest that informal handling of risk takes place on a large scale and that social networks informally connect communities in ways that impact their economies. Recent trends suggest that the interaction of formal and informal economic processes is growing on a nationwide scale. First, the aggregate value of remittance flows is large and growing, nearing the value of foreign direct investment. Second, advancements that formalize previously informal transactions are expanding dramatically in emerging economies through various forms of branchless banking. National economies may be embedded in profoundly influential informal systems that have never been holistically studied.

On a more general note, this project touches lingering questions in economics. For example, some might argue that more stringent labor regulations should have increased informal employment, but in fact the opposite happened. And while the economic rationale for the former statement remains valid, we suggest that unions, as other types of social organizations, as examples of ways whereas people interact via organized networks, provide richer dimensions than those suggested by their interaction as mere economic agents. Hence, unions, as well as other types of faith-based, ethnic, community and other interest groups may provide ways of interaction that escape narrow economic outcomes.

====References====

• De Soto, Hernando (2000/2003) The Mystery of Capital. Basic Books.
• Losby, Jan; Else, John; Kingslow, Marcia; Edgcomb, Elaine; Malm, Erika and Vivian Kao (2002) The Informal Economy: A Literature Review. ISED Consulting and Research and the Aspen Institute Working Paper. http://www.kingslow-assoc.com/images/Informal_Economy_Lit_Review.pdf
• Townsend, Robert M. "Risk and Insurance in Village India." (1994)
• Fafchamps, Marcel, and Susan Lund. "Risk-sharing Networks in Rural Philippines." (2003)
• Weerdt, Joachim De, and Stefan Dercon. "Risk-sharing Networks and Insurance against Illness." (2006)
• Matthew O. Jackson, Tomas Rodriguez-Barraquer and Xu Tan. “Social Capital and Social Quilts: Network Patterns of Favor Exchange.” (2012)
• Yang D and H Choi."Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines"(2007)
• Kurosaki, Takashi. "Consumption vulnerability to risk in rural Pakistan." (2006)
• Jack, W, and T. Suri. "Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution” (2014)
• Engels, Friedrich (1844/1892) The Condition of the Working Class in England. http://www.gutenberg.org/files/17306/17306-h/17306-h.htm
• Venkatesh, Sudhir (2008) Gang Leader for a Day: A Rogue Sociologist Takes to the Streets. Penguin Books.
• Storr, Virgil and Laura Grube (2013) The Capacity for Self-Governance and Post-Disaster Resiliency. George Mason University, Department of Economics Working Paper 13-37. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2350830##
• Blumenstock, Joshua Evan and Fafchamps, Marcel and Eagle, Nathan. “Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda” (2011).
• Ratha, Dilip. "Workers’ remittances: an important and stable source of external development finance." (2005).
• Pénicaud, Claire, and Arunjay Katakam. State of the Industry 2013: Mobile Financial Services for the Unbanked. Rep. N.p.: GSMA MMU, 2013.

If interested, please list your name below.

'''Interested:'''
<li> Eloy Fisher (eloy.fisher@fulbrightmail.org) </li>
<li> Carolina Mattsson (carromattsson@gmail.com) </li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>

<li> Jakub Rojcek (jakub.rojcek@gmail.com) </li>

==Exploring Community Formation through Analysis of Scholarly Corpus (ArXiv)==

The Arxiv [http://arxiv.org/] is a free online repository of scientific preprint articles, mostly from physics and mathematics. It currently contains over 800,000 articles, dating back to 1991. Currently, a lot of the data associated with this repository is completely available: paper submission dates; full texts of papers; author names and coauthorship information. Additionally, there is some citations data available. (If necessary, I can also find papers' subdiscipline labels; submitting authors' email address domain names; other things.)

In the past I have been using these data to try and explore how communities of authors who have shared interests grow over time. For example, we can pinpoint the first ever paper about topological insulators, and search for all subsequent papers on that topic. As more papers are written, authors begin to join the field of research and to form strong ties by collaborating with one another. We can use the ArXiv data to visualize and analyze exactly how these communities form and grow.

===Brainstorming notes===
Isolation between topics, crossing interdisciplinary boundaries, finding (topological) separation distance between disciplines or research groups

Tipping points - can we look for separation of or mergers of two groups or disciplines?

Can we identify key papers (or groups of papers) that initiate ties between fields.

Sentiment analysis: can we identify when a paper's citation is an endorsement or a refutation?

Tools for Analysis: Change point detection; relational event models

Can we find more comprehensive/better citation data?

Lucene and indexing tools:
- Can we re-index our text database after removing stop words?
- Can we index the titles and abstracts?
- Elastic Search - tool for interacting with Lucene

This set of text-processing [http://alias-i.com/lingpipe/demos/tutorial/read-me.html tutorials] is pretty handy for background info and inspiration. The software package doesn't simply plug-into a Lucene/Solr/Elastic Search index, but it could be done. This package from [http://nlp.stanford.edu/software/ Standford] seems very capable.

===Next meeting===
Thursday @ 9AM in Coffee Shop

== PolComplex - Epistemic communities and policy dynamics in the UK Parliament ==

Over the last twenty years, there has been extensive debate about what the core topics in public policy are, and if they change according to type and number. Furthermore, it is still not clear how policy topics mutate and diffuse over time. Human-based text analysis of governmental documents has shed only some light on these research questions: 21 general topics have been proposed, while other accounts tend to restrict it to about five main clusters. Yet, this approach has not been able to devise a reliable and systemic method to capture topic dynamics, diffusion, and their relation to the human actors that talk about them.

This project is an extension of an exploratory research that intends to overcome such limitations, through the application of natural language processing (more specifically, dynamic topic modeling and sentiment analysis), topological and network analysis on a dataset containing UK House of Commons debates ranging from 1975 to 2014. The aim is manyfold. We hope to identify the structure and dynamics of epistemic policy communities - i.e. of political actors revolving around similar policy interest -, and how these relate to factors such as party membership, seniority, relevant historical events.

Some references:

[http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.734168 Taddy (2013), "Multinomial Inverse Regression for Text Analysis"] 

[https://www.cs.princeton.edu/~blei/papers/BleiLafferty2007.pdf Blei and Lafferty (2007), "A Correlated Topic Model of Science"] 

== Genomic variations from a chaotic mapping ==

Following Dabby CHAOS 6 (2), 1996 Musical variations from a chaotic mapping, this project will explore genomic variations in key metabolic genes that are known to be widely spread across bacterial phylogeny (e.g. the Nif cluster which is used in nitrogen fixation). There are several ways that genomic data can be treated as "music":

1) Nucleotide: 4 notes - A, G, C, T 
2) Amino Acids: 20 notes 
3) codon triplets: 64 notes 
4) tetra nucleotides: 256 notes. 

One possible goal is to take a seed sequence from some random organism and generate new variations. Then using homology search (e.g. BLAST) or phylogenetics, determine if that variation exists in nature? If not, perhaps model the potential protein folding (if possible?) to determine if that variation could exist in nature. This is VERY preliminary and can go in many directions.

If interested please list name below or contact Jarrod at jscott@bigelow.org

Reading
http://digitalcommons.olin.edu/cgi/viewcontent.cgi?article=1000&context=ahse_pub&sei-redir=1&referer=https%3A%2F%2Fscholar.google.com%2Fscholar%3Fhl%3Den%26q%3Ddabby%2Bchaos%26btnG%3D%26as_sdt%3D1%252C32%26as_sdtp%3D#search=%22dabby%20chaos%22

== Free Energy Theory ==
The Free Energy (FE) minimisation framework tries to explain how biological systems (such as a cell or a brain) self-organise in order to occupy the (often very limited number of) non-equilibrium states that minimise free energy. This is also known as active inference. A simple corollary of active inference is that agents behave as to minimise their prediction error, or the difference between prediction and reality. Thermodynamic free energy is a measure of energy available in a system to do useful work. This can be framed in an information theoretic setting, as the difference between how the world is being represented and how it actually is. A better fit means a lower information-theoretic free energy, as more resources are being put to ‘good use’ in representing the world. The overarching logic of FE theory is that a better model of the world help maintain structure and organisation, which ultimately helps the system resist increases in entropy.
 
 
This is not a set-in-stone project with any concrete aim (yet). We are a few people interested in exploring the theoretical and practical implications of these ideas, and you're more than welcome to join in!
 
 
'''Interested:'''
 
<ul>
<li> Jelle Bruineberg (j.bruineberg@gmail.com ) </li>
<li> Tobias Morville (tobiasmorville@gmail.com) </li>
<li> Susanne Pettersson (guspsusa85@student.edu.se) </li>
<li> Maggie Simon (margaret.w.simon@gmail.com) </li>
<li> Martina Steffen (martinasemail@gmail.com) </li>
<li> Sahil (sahilgar@usc.edu) </li>
<li> Tirtha (Tirtha.Bandy@csiro.au) </li>
<li> Anna (annza944@gmail.com) </li>
<li> Matt O (mmosmond@gmail.com) </li>
<li> Will Chang (williamkurtischang at gmail dot com) </li>
<li> Cobain (mrdc1g10@soton.ac.uk) </li>
</ul>
Reading:
 
http://rsif.royalsocietypublishing.org/content/10/86/20130475 
http://arxiv.org/abs/1503.04187 
http://www.mdpi.com/1099-4300/15/1/311 

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
'Carolina'
'Alice'
'Federico'
'Jean Gab'
'Sola'
'Ilaria'
'Daniel T'

==Comparison of Network vs Scaling Theory based Models in Ecology (Cobain - mrdc1g10@soton.ac.uk)==

One of the biggest difficulties ecologists face is trying to understand the ecosystem dynamics based on very little biological information (compared to the size of the system) - observational data is logistically difficult and can be very expensive to acquire, as well as often being very time consuming. In terms of modelling, to overcome this problem, two approaches have been utilised. 

The first is a network based approach whereby the nodes represent biomass density of a particular species (or a higher level taxanomic group and/or resources) and edges as the trophic interactions (who eats who). This method depends on what we know about the trophic behaviour of the species involved (which is often very limited and there can be many species to parameterise), and represents only the central tendency of what could potentially be very diverse behaviour within and between populations. 

The second approach is to use scaling theory to describe average trophic interactions and other biological processes based on individual organism body size along the size continuum, viewing the community as a very size structured dynamical system. This requires less specific knowledge about the organisms in the community ''per se'', however this again represents only the central tendency of a given size class of what could be very diverse behaviour. Functional differences can be potentially introduced (coupled benthic-pelagic systems for example), but to the detriment of braking down the predictability of the well known allometric scaling laws with size as specificity increases. 

There has been little to no comparison of these two methods of modelling ecological systems (at least to my knowledge) and the question arises that, given the same starting information, how well do both approaches model the dynamics of an ecosystem, given the limited biological information we have? Is one approach better at capturing energy flow through the system / community structure and stability etc. compared to the other? If the models vary then such information will better inform ecologists on which to use depending on what type of questions they are trying to answer. 

Therefore, the project proposed here aims to investigate these two methods. The community that is used to seed an experimental mesocosm setup (mesocosms are large tank set-ups designed to reflect the complexity of natural ecosystems but still being able to be artificially controlled and are still relatively simple), will be input into two models based on the separate approaches and then run to steady state. The model outputs will then be compared and contrasted with eachother as well as the mesocosm community at steady state. Further work may include examining perturbation dynamics, dependent on what data we have available.

'''Interested: Name / Email''' 
Martina Steffen (martinasemail@gmail.com)

==Dynamics of homicide (Matthew Ingram - mingram@albany.edu)==

Integrating temporal, spatial, and multi-level concepts

1:30pm in the coffee shop

Am interested in the discussion on this project...Sola Omoju

Interested - Nilton Cardoso

==Multiplex Adaptive Networks (Daniel - dtc65@cornell.edu)==

7 pm in the coffee shop

==Modeling brain diseases (or cancerous bio pathways) (Sahil - sahilgar@usc.edu)==
Interested people can put a meeting time as per their convenience here.
 
No meeting time indicated
 
One potential meeting time can be 3pm in coffee shop ?
 
Information theoretic algorithms can also be explored for the problem.
 
Discovering Structure in High-Dimensional Data Through Correlation Explanation. http://papers.nips.cc/paper/5580-positive-curvature-and-hamiltonian-monte-carlo
 
Maximally Informative Hierarchical Representations
of High-Dimensional Data. http://jmlr.org/proceedings/papers/v38/versteeg15.pdf

 
'''Interested:'''
 
<ul>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> Laura Condon (lcondon@mymail.mines.edu)</li>
</ul>
 
 

==Analysis of UK parliament speeches 1935-2014 (Stefano - etstefano@gmail.com)==

No meeting time indicated

==Mapping Complexity/Human Knowledge as a Complex Adaptive System (radjohn@live.com)==
2pm/Wed 6/10 in Conference Room (Tentative)

==Navigating Music, Brain and The Edge of Chaos (radjohn@live.com)==
10:45am/Wed 6/10 in Conference Room
9:00am/Thu 7/10 at the Coffee Shop

'''Interested:'''
Sahil Garg

==Analysis of rule-based modeling for dopamine-dependent synaptic plasticity (emilia.m.wysocka@gmail.com)==

----------------------------------------------------------------------------------
Modeling and analysis of phenomenons and evolution of stochastic, combinatorially complex signalling systems in a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system).

----------------------------------------------------------------------------------
Rule-based modeling features:

*biological systems as concurrent processes
*dynamics of post-translational modifications
*domain availability
*competitive binding
*causality and intrinsic structure
*binding sites
*interaction rules replace reaction equations
*infinite number of reactions with a small and finite number of rules
*reduction of parameter space
*“don't care don't write” - adjustable rule contextualization
*single reaction rule and parameters generalize classes of multiple rules
*modular and extensible language
*specification language & simulation/integration environment
*static and causal analysis
*Kappa/KaSim & BioNetGen/NFsim -- specification language/network-free simulator

----------------------------------------------------------------------------------
----------------------------------------------------------------------------------
Meetings:
----------------------------------------------------------------------------------
* WEDNESDAY 11/06/2015

Addressing problems in terms of the other aspects of the projects apart from the biological questions and verification (matching results to some published experiments or known behaviour). So my plan is as follows:

* divide the group (roughly) into people who look in to biological meaning and validation and the one which tries to do the analysis of system phenomenons and evolution of stochastic, combinatorially complex signalling systems in both a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system) - all possible states, scenarios of the system abstracted from the biological meaning.

Things that could be modelled (look in dropbox folder: https://www.dropbox.com/sh/g080w60pt2vgi7v/AACHlMf2JwpP2EZqKq7Di6N2a/possible%20models?dl=0):

* to make things easier and have the modeling part done quickly- base the model on the dopamine-related synaptic plasticity (SBML ODE model): http://www.ebi.ac.uk/biomodels-main/MODEL1101170000;

* I found a series of papers suitable to the subject of the Complex Systems course, e.g.: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC344803/. I was thinking to even try to compare higher level brain data (networks, spiking ..) with purely molecular model

* noise and low copy number (http://www.princeton.edu/~wbialek/our_papers/bialek+setayeshgar_08.pdf); this is quite interesting as for sufficiency of binding events -> http://www.princeton.edu/~wbialek/our_papers/bialek+ranganathan_07.pdf

* spontaneous flipping of interactions (phosphorylations and others) between proteins, described by the god of non-linear dynamics (S. Strogatz)

----------------------------------------------------------------------------------
* THURSDAY 11/06/2015 : http://doodle.com/se23ibwtkrkccs4s

----------------------------------------------------------------------------------
Some refs:
*https://www.dropbox.com/sh/g080w60pt2vgi7v/AADUy7Ge-J-oSYkyTeYOo7V8a?dl=0)
*http://www.pps.univ-paris-diderot.fr/~jkrivine/homepage/Teaching.html

==Ecology non-working group (williamkurtischang at gee-mail dot com)==
[[Informal ecology interest group]].

==Making Supply Chains Resilient to Disasters (mh2905 att columbia dott edu)==
'''Key words''': resilience, natural hazards, supply chains, interdependency, interconnected risks, cascading failures

'''Rational''': I am interested in examining what kinds of network structures and features contribute to increasing resilience of supply chains to natural disasters. I believe this area of work is important because regional disasters negatively impact the global economy through disruptions in supply chain networks. The pioneer study published in Nature urges the need for making supply chains climate-smart (Levermann 2014). Also in the industry, the World Economic Forum published a report to address this issue in 2013. Few researches, however, assess and model the impacts of adverse weather on supply chains. I would like to evaluate the impacts based on modeling.

'''Data set''': Supply chains data of a multinational manufacturing company.Global climate data, disaster data, etc.

'''Possible techniques''': Agent-Based Model, Complexity science, network theory and evolution, complex adaptive systems, GIS, operations research, manufacturing engineering, and I am open to any techniques.

'''Myself''': As I joined the program yesterday, please find my background here.
http://tuvalu.santafe.edu/events/workshops/index.php/Masahiko_Haraguchi

'''Please put your names below if you want to be informed about this project by email'''

'''Interested''':
Masa Haraguchi

==From Ethnic Diversity to Religious Zeal: Retrospective/Predictive Construction of the World Ethnic Map (vdzutsati@asu.edu)==

There is a new dataset on ethnic groups (2014), which claims to include all ethnic groups of the world http://www.cidcm.umd.edu/mar/amar_project.asp. There are also several cross national datasets on religion and other variables of interest, normally, socio-economic http://www.thearda.com/Archive/CrossNational.asp. Coming from social sciences I thought it would be really interesting to apply methods and perspectives from other disciplines to social science data.

One idea that I have in mind is to explore how ethnicity and religion interact.

Quick empirical checks indicate that Subsaharan Africa is home to about 1,500 ethnic and subethnic groups, in comparison to about 90 ethnic groups in the Middle East and North Africa. India alone accounts for nearly 2,000 ethnic and subethnic groups, while Europe, including all the countries of the former USSR and large immigrant groups from other parts of the world, has only about 260 ethnic groups. The picture that emerges from this simple comparison is that the spread of Abrahamic religions appears to be associated with the high depletion rate of ethnic groups. The exception of China, which has little more than 50 ethnic groups, can be explained by the country’s long history of a centralized state.

The idea then is to assume that we have had the control group of nations that did not experience the influx of Abrahamic religions (or more precisely received limited exposure to them) and the experimental group that was exposed to the spread of Abrahamic religions. Since we know what the distribution of ethnic groups in the control group is we can project what the distribution of ethnic groups in the experimental group would be, if the group were not exposed to Abrahamic religions.

Of course, we could go in the other direction too and make a projection about future – what would happen to ethnic groups if all of them adopted an Abrahamic religion.

One of the challenges in this project is absence of a dynamical system, I.e. We don’t have data on how these things changed over time, but I still think that projection about the future and the past would be kinda cool:)

'''Interested''':

==Nationalist vs religious rebel violence (vdzutsati@asu.edu)==

I would like to propose yet another possible project. This time it’s on rebel violence. I have a large dataset on rebel (and government) violence (~1 million observations). The paper that used this data was published couple of months ago (Islamists and Nationalists: Rebel Motivation and Counterinsurgency in Russia’s North Caucasus). The dataset contains event counts, GIS data, demographic and economic characteristics and some other stuff. The primary hunch of the paper was to discover the differences between nationalist and Islamist rebels.

Given all the cool methods we are learning here, my first instinct is to use the methods and tools (TISEAN?) to explore and discover whether nationalist violence is substantively different from religious violence and try to answer why.

One problem that I can see is that the event coding was done by a machine, so this may have “contaminated” the data. But if both religious and nationalist data were contaminated equally then the difference should still be evident.

'''Interested''':

==[[Archived Projects ]]==

Complex Systems Summer School 2015-Projects & Working Groups

2015-07-02T19:18:43Z

Rbarnes: /* Organ Transplant Analysis */

{{Complex Systems Summer School 2015}}

==[[Reservations for Evans Science rm. 215]]==

Click on the title link to reserve Evans Science rm. 215 for group usage.

==Californian Drought Model==
Problem definition:
Water is an ongoing problem in California. Although corrective measures are taken for some years, periods of droughts are depleting water ground levels to not sustainable levels.
Aims:
A Netlogo simulation on how likely Californian agents evolve with regards to drought. The ABM could be used to explore the potential impacts of forms of regulation.
Outcomes:
Create an exploratory agent based model based on real data, enabling the possibility of simulating different methods of regulation, including feedbacks between the economic and hydrologic systems.

Extension 1: We will conduct a network analysis of Twitter data (hashtags #drought #California) to explore the social dynamics and information flows between stakeholders.

Extension 2: Phase-space reconstruction of groundwater level time series data, comparing the dynamics in different parts of the Central Valley.

==City Resilience==
'''Summary:''' This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities. 
'''Contact:''' Richard Barnes (rbarnes@umn.edu) 
'''Participants:''' Alex Tejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang, Martina Steffen, Juan Carlos Castilla, Brent Schneeman 
'''Wiki page:''' [[Resilience of Cities]]

==Complex Adaptive Systems and the Narrative approach: transdisciplinary methodologies for Complexity Science==
The narrative approach and it causality is different from causality in logico-scientific approaches. Making bridges among methodologies transfers knowledge, encourages important questions and switches philosophical paradigms. Formal frameworks of C(A)S can realy be complemented with the narrative, and actually should. The narrative approaches in Complexity Science are an emerging trend for fund-granting, and is really up to date to process documentary and speech-based data, reveal hidden meanings, distinguish causes from effects in overlapping systems of realms - "at the edge" of technology, social, economic, scientific, legal and other domains. The role of time in coding, intuitively generated conditional rules and computational paths. Combinations of C(A)S and the narrative is combination of quantitative and qualitative data and thinking in original - out of convertation and information loss. Synthesis of sciences is what it is about.

'''Questions''':
How to combine CS approaches (in particular CAS) and the narrative ones in a rule-based way? What to start from? What vizualizations there can be designed and used? Other questions at your descrete.

'''Some references''':
- Non-Equilibrium Social Science in ICT and Economics, CORDIS, EU: http://cordis.europa.eu/project/rcn/102344_en.html, http://www.nessnet.eu/

- Combining Complexity Theory with Narrative Research: https://youtu.be/pHjeFFGug1Y

- Haridimos Tsoukas and Mary Jo Hatch (2001), "Complex thinking, complex practice: The case for a narrative approach to organizational complexity: http://www.brown.uk.com/teaching/qualitativepostgrad/tsoukas.pdf

- David Christian, "Big History", Astrophysics, Chemistry, Biology, Information, emergence of life, technology: https://youtu.be/GlmvFjFceD8

'''Contact''': Anna (annza944@gmail.com)

Meet on Monday over lunch

'''Interested''': Marie Pierre, Jeroen, Jim, Melissa

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
 
'Carolina'
 
'Alice'
 
'Federico'
 
'Jean Gab'
 
'Sola'
 
'Ilaria'
 
'Daniel T'
 
'Sahil Garg

==Ebola==
The 2014-15 Ebola virus disease (EVD) outbreak in West Africa presented both unique opportunities and unique challenges to the epidemiological modeling community. For the first time during an emerging infectious disease outbreak, high resolution data--from a variety of sources--were made available to the academic community and many public health decision makers genuinely engaged with mathematical and computational modelers. However, the popular and scientific press were highly critical of most models ability to project the outbreak's course. The following key and open questions seem ripe for investigation using a complex adaptive systems lens:
 
1) What features of EVD transmission are most problematic for reliable, robust forecasting: changing behavior, intervention, viral evolution, complex social networks, etc?
 
2) How/can we use digital data to either improve forecasts or inform model selection?
 
3) Can one quantify the value of additional information in real-time?
 
'''Contact:''' Samuel Scarpino, SFI Omidyar Fellow, Santa Fe Institute - scarpino@santafe.edu
 
 
'''Interested:'''
 
Marie-Pierre Hasne 
Chris Verzijl 
Junming Huang 
'Sola Omoju 
Christine Harvey 
Daniel Citron (dtc65@cornell.edu) 
William Chang (williamkurtischang at gee-mail)

==Effect of landscape topography on vegetation connectivity and navigability==
''Context''
 
Landscape topography influences the dynamics of the processes that take place on it. Evolution of ecosystems networks, river networks, vegetation cover type, microclimate cycles are all interlinked deeply to the local landscape topography.

In addition to the landscape these networks are also influenced by each other conditioning the emergence and stability of ecosystems, and subsequently the behaviour of agents in the ecosystem like migration pathways of animal herds, human settlement patterns etc.

Connectivity patterns emerge from the interaction of these processes, and thus a better understanding and quantification of those patterns is critical to understanding the dynamics of the system.

 
''How do we want to approach the problem''
 
In this project, we will randomly generate landscape topography and subsequent vegetation cover from a set of parameters from known geological and biological processes. The generated data set will then be used to investigate the following questions:
 
(1) What is the connectivity of landscape patterns that emerge at different scales using different techniques such as clustering analysis, percolation theory or network theory, and can we quantify them ?
 
(2) What is the navigability of biological agents (e.g animals, humans, robots!) under such landscape patterns. We can compare mobility trails in existing landscapes to validate our hypothesis. We propose to use the tools like ABMs to simulate and characterise mobility success.
 
(3) We further aim to compare the measures of navigability with the metrics of connectivity, establishing a framework of comparison.
 

''Contact:''
 tirtha.bandy@csiro.au
 alej.tejedor@gmail.com

''Meetings''
(1) Wednesday 1pm Coffee shop

People Interested 
Martina

==[[Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe]]==
Click on the link to go to the project page for meetings, project details, and progress.
 
contact: mveitzel@ucsc.edu
 

==Mapping Complexity/Human Knowledge as a Complex Adaptive System‏==
''Context''
 
Ants leave pheromone trail patterns which they are aware of only in a local sense. They do not have the cognitive faculties to step back and look at the trails and grasp the ant-trail network as a totality. Also, the artifacts they leave behind are physical entities which then provides the aggregate feedback to the aggregate ant body to then feed the evolution of the ant body as a CAS system. In contrast, humans do have the requisite cognitive abilities. The "pheromone trails" we leave behind are the knowledge trails coded in symbolic knowledge artifacts. In contrast to the physical artifacts that ants leave behind, the knowledge artifacts that we leave behind are far more flexible and potent, both at the aggregate as well as at the individual levels. But like the ants, until recently, we did not have the means to step back and map the knowledge "pheromone trails" to obtain the big picture and its global/local dynamics. The burgeoning field of scientometrics is making available visualization tools to help us map and study the evolutionary dynamics of the knowledge network structures.
 
''Data and Questions''
 
The goals of this project include
<ol>
<li>Extract the terms from approximately 1600 working papers published by SFI</li>
<li>Map the intra/inter conceptual network structures</li>
<li>Study the evolution of these structures across time</li>
<li>High-light the gap-closure of knowledge reverse-salients (if any)</li>
<li>Capture any of the network patterns that repeat</li>
<li>Study the diffusion of concepts across the network</li>
<li>Provide visualization tools for navigating the complexity corpus, etc</li>
</ol>
 
''Possible methods''
 
Latent Semantic Analysis (LSA) and Latent Document Analysis (LDA)
 
References:
<ol>
<li> SFI Working Papers: http://www.santafe.edu/research/working-papers/</li>
<li> Atlas-Science-Visualizing: http://www.amazon.com/Atlas-Science-Visualizing-What-Know/dp/0262014459</li>
<li> Atlas-Science-Visualizing WebSite: http://scimaps.org/atlas</li>
<li> Mapping-Scientific-Frontiers-Knowledge-Visualization: http://www.amazon.com/Mapping-Scientific-Frontiers-Knowledge-Visualization/dp/1447151275</li>
<li> Katy Börner presents at Science of Science: https://www.youtube.com/watch?v=pzCqGBNzomE</li>
<li> Scholarly Data, Network Science, and (Google) Maps:https://www.youtube.com/watch?v=vos5QBDywMM</li>
<li> LSA Video Lect: http://videolectures.net/slsfs05_hofmann_lsvm/</li>
<li> What is LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf</li>
<li> LSA Wiki:http://en.wikipedia.org/wiki/Latent_semantic_analysis</li>
<li> LDA: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm</li>
<li> Fusari, A. (2014). Methodological Misconceptions in the Social Sciences: Rethinking Social Thought and Social Processes: http://www.springer.com/us/book/9789401786744 </li>
<li> Kirsh, D. (2013). Thinking with external representations. Cognition Beyond the Brain: http://adrenaline.ucsd.edu/kirsh/Articles/Interaction/thinkingexternalrepresentations.pdf </li>
<li> Holland, J. H. (1992). Adaptation in natural and artificial systems: https://mitpress.mit.edu/index.php?q=books/adaptation-natural-and-artificial-systems </li>
<li> Where to start with text mining. http://tedunderwood.com/2012/08/14/where-to-start-with-text-mining/ </li>
<li> Singular Value Decomposition Tutorial https://www.ling.ohio-state.edu/~kbaker/pubs/Singular_Value_Decomposition_Tutorial.pdf </li>
<li> Latent Semantic Analysis (LSA) Tutorial: http://www.puffinwarellc.com/index.php/news-and-articles/articles/33-latent-semantic-analysis-tutorial.html </li>
<li> LSA in Detail: http://www2.denizyuret.com/ref/berry/berry95using.pdf </li>
<li> Web Based LSA: http://webapp1.dlib.indiana.edu/newton/lsa/index.php </li>
<li> Another Technique: t-Distributed Stochastic Neighbor Embedding (t-SNE): http://lvdmaaten.github.io/tsne/ </li>
<li> tf–idf: https://en.wikipedia.org/wiki/Tf%E2%80%93idf </li>

</ol>
 
'''Interested:'''
 
<ul>
<li> John Thomas (radjohn@live.com) </li>
<li> Haitao Shang (hts@mit.edu) </li>
<li> Christopher Verzijl (cjo.verzijl@gmail.com) </li>
<li> Anna Zaytseva (annza944@gmail.com) </li>
<li> Penny Mealy (penny.mealy@gmail.com) </li>
</ul>
 
 

==Navigating Music, Brain and The Edge of Chaos ‏==
''Context'' 
[[Navigating Music, Brain and The Edge of Chaos]]
 
Brain Sciences have revealed that we/it lives "on the edge of chaos" exhibiting "self-organized criticality" that is tentatively balanced between normalcy and madness. Over the course of history, humans have used various agents and activities to shape, influence and control this living-chaos ranging from substances such as caffeine, sugar, drugs etc., to activities such as the arts (including music), social-discourse/therapy, meditation etc. Of these, music has a distinct role in shaping our moods and helping us transition between different mental states, as well as maintain it for extended periods of time. Clearly we have been using music to help us control and shape the internal chaos. But until recently, the quantitative instrumentation of this massively complex system that comprises of close to a 100-billion neurons networked into a 1000-trillion synaptic edifice has not been available to the common man. But of late, affordable, wearable EEG's are available on the market, thus making the quantitative study of the influence of music in brain dynamics feasible on a large-scale/crowd-sourcing sense. To help come to terms with the complexity of our 1000-trillion synaptic edifice, we need to gather data on a vast scale. The proposed research is a proof-of-concept, exploratory foray into making this happen.
 

History of music in 5 min
https://www.youtube.com/watch?v=Gt2zubHcER4

'''Interested:'''
 
<ul>
<li> Sara Lumbreras (sara.lumbreras@gmail.com) </li>
<li> Ilaria b (bertazzi.ilaria@alice.it) </li>
<li> Braun, Urs (urs.braun@zi-mannheim.de) </li>
<li> John Thomas (radjohn@live.com) </li>
<li> Glenn Magerman (glenn.magerman@kuleuven.be) </li>
<li> Daniel Friedman (DanielAriFriedman@gmail.com)</li>
</ul>
 
 

==Powerlaw fitting and alternative distributions - Theory/statistics ==
Clauset, Shalizi and Newman (2009) propose a maximum likelihood method to estimate the powerlaw exponent of a variable of interest. This is a great improvement on earlier methods such as OLS that dominated the literature up to then. However, one can fit a powerlaw to any dataset and the most we can say is that our observations are consistent with the hypothesis that x is drawn from a powerlaw distribution. One easily implementable method to compare the powerlaw fit to other fits is then a likelihood ratio test for both models.

One particular discussion is the distinction between a powerlaw (PL) and a log-normal (LN) fit. For an avid discussion between both fits on city size and Zipf's law, see Eeckhout (2004, 2009) and Levy (2009), where the discussion now settled on city sizes following a log-normal distribution instead of a powerlaw. Similarly for the discussion on firm size distribution: Simon and Bonini, 1958; Ijri and Simon, 1977; Stanley et al., 1995; Sutton, 1997; Axtell, 2001; Okuyama et al., 1999; Cabral and Mata, 2003; Gaffeo et al., 2003; Aoyama et al., 2004; Fujiwara et al., 2004a,b; Kaizoji et al., 2006; Takayasu et al., 2008; Duchin and Levy, 2008; Schwarzkopf and Farmer, 2008, ...

This distinction matters for several reasons:
- PL and LN come from very similar model and differences in initial conditions can lead to very different outcomes. PL exhibits a choice for x_min, below which the unit of observation is not feasible to exist (eg minimum city size, firm size, word length, ...). LN has no minimum size.
- PL and LN that look similar are the difference between infinite (PL) and large but finite variance (LN)
- shock propagation: when unit-level shocks are large enough to show aggregate perturbances if the distribution is powerlaw with infinite variance, while these shocks wash out fast when the distribution is log-normal. (Gabaix, 1999; Gabaix 2009; Acemoglu et al. 2012, ...).

I have encountered some issues which I would like to explore further:
1. The distinction between lognormal and powerlaw in the data is very sensitive to data truncation: in the above discussions, researchers have slightly different datasets, covering more or less of the population at hand. Left-truncation (i.e. observations not in the dataset because they are too small to be reported) can strongly drive the outcome of the fit, even when endogenizing the x_min cutoff. I have data on the universe of Belgian firms, much more complete than e.g. US Census data, where I have done some preliminary tests on this. The question is then: how to formalise this distinction and what are the theoretical and practical caveats to look out for when applying this method.
2. MLE fitting seems to be sensitive to the choice of units as well: rescaling a variable by a factor 1000, 1000000 etc seems to influence the endogenous x_min choice and hence the estimated parameter. This reminds me of some work on scaling invariance in negative binomial estimators. What is going on here?
3. Can we set up a model that generalises both? I've been looking at Levy stable distributions, but did not do anything with it yet.

Interested people:
* Corbain
* Laura
*Binyang
*Sahil
*Tirtha
*Glenn
*Junming

Literature:
Powerlaw fitting in empirical data - Clauset, Shalizi and Newman (2009): http://arxiv.org/abs/0706.1062

==Organ Transplant Analysis==
Over 120,000 people in the United States are currently on the waiting list for an organ transplant. The size of this waiting list relates to over 6,000 deaths a year while waiting for a transplant and tens of billions of dollars in government spending. I have access to the following data sets:
 
* All transplants performed in the US from October 1987 to June 2014 (including follow-up data)
* All living and deceased organ donors in the same time period (with follow-up data on living donors)
* Waiting list data for everyone who signed up for the list
* 2012 National Survey on Attitudes and Behaviors on Organ Donation
* Social media data relating to organ donation since 2008

Open to ideas and suggestions for the topic, there are a lot of interesting questions to investigate including cultural/racial/gender differences in organ donation. I have several preliminary reports and exploratory analysis done on differences in donors.

Second Meeting Thursday, June 11th 4:15 in the coffee shop!

===Matching Game===
Rules for the matching game:

Objective: Create the largest chain possible using blood types in the room.

Follow the blood type guidelines for donation to form a donor chain.
*Assume that self-matches are not possible, for example someone with a donor of type AB and a recipient of type AB can not match themselves and needs to find someone else to complete their chain.
*Create a donor chain to help as many people as possible.
*Post results below for the prize
*There will be several "good samaritan" donors which will donate and need nothing in return, this is a great way to start your chain. Using a good samaritan chain also means you don't need a donor for the end point.

====Game Workspace====
Use this workspace to post for and find matches!

==== Donor Chains ====
Post your chain here. Example is given below:

=====Example Chain 1=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| A
|-
| Laura
| AB
| B
|-
| Matt
| A
| AB
|}

=====Example Chain 2=====
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| Christine
| O
| None
|-
| Laura
| A
| B
|-
| Matt
| None
| A
|}

======Richard's Current Table======
{| class="wikitable"
|-
! Person
! Donor Type
! Recipient Type
|-
| LauraCondon
| O
| X
|-
| Urs
| B
| A
|-
| Sam
| O
| B
|-
| MariePierre
| A
| O
|-
| Glenn
| O
| A
|-
| Richard
| A
| A
|-
| Chris
| O
| A
|-
| Masa
| A
| O
|-
| Kleber
| O
| A
|-
| Jakub
| O
| A
|-
| Keith
| AB
| O
|-
| Brent
| O
| AB
|-
| Alice
| AB
| O
|-
| Jelle
| O
| AB
|-
| AndySchauf
| A
| A
|-
| Michael
| O
| A
|-
| MatthewO
| AB
| O
|-
| Alejandro
| O
| AB
|-
| Melissa
| A
| B
|-
| Carolina
| O
| A
|-
| MatthIn
| AB
| O
|-
| Susanne
| A
| AB
|-
| Jae
| B
| A
|-
| Charon
| B
| B
|-
| Nilton
| O
| B
|-
| Havier
| AB
| O
|-
| Vanessa
| A
| AB
|-
| Maria
| O
| A
|-
| Martina
| A
| O
|-
| Cobain
| B
| A
|-
| Laurence
| O
| B
|-
| James
| AB
| O
|}

==Multi-dimensional social networks in the evolution, development and resilience of informal economies. ==
Informal economies are defined as economic activities that occur outside the purview of corporate public and private institutions. These types of economies proliferate where traditional economic actors are unable to productively exercise their activities, especially due to costly constraints (De Soto 2003). Firms may face adverse incentives to expand production by hiring more workers or incorporating more capital due to the low productivity of their workers or the predatory practices of rapacious elites or corrupt governments. Labor itself, i.e. people, may face hurdles in trying to make the jump towards entrepreneurial activities or jobs in the formal economy which allow the accumulation of experience, retirement savings, access to insurance or precautionary savings to face unexpected events (like disease, disability, etc.) due to lack of capital access (whether human and financial).

'''For this project, we propose a different interpretation: We seek to understand and model how multi-faceted social networks provide robust alternatives to formal economies, which far from being seen as degenerate forms of social organization, in many instances co-exist, challenge and compete with formal employment and economic activities. While some types of informal economies operate under adverse contexts, their resiliency may be understood as a type of adaptive fitness and not the mere result of stubborn cultural path-dependency. '''

For the most part, informal economies have been analysed as counterpoint to an ideal type of a formal economy with low levels of trust, respect of property rights, access to credit and public institutions - like the court systems (see Losby et al. 2002 for a review). However, informal economies have been coupled with their formal counterparts since the development of capitalism in the 19th Century. To name a famous historical example, Old London's East End's informal sector was described harshly by Engels (1844) in his famous tract, "The Condition of the Working Class in England". But almost fifty years later, in a new preface to the book, Engels recognized the progress that took place there under the aegis of working class organizations in the area.

Research has begun to catch on to this idea – developing a literature in the areas of informal risk sharing and remittances, which are in some sense informal counterparts to insurance and banking. Consumption patterns in poor, rural villages are remarkably smooth suggesting that risk-sharing measures are prevalent, although imperfect (Townsend). Field studies of networks underlying village risk sharing systems have found that households primarily receive help from existing social connections, such as friends and relatives, in the form of informal loans or transfers. Theoretical work in network economics, by authors such as Matt Jackson, found that certain empirically prevalent network structures may directly benefit the stability of favor exchange systems. Another line of inquiry focuses on remittance income, generally informal transfers across kinship ties. World Bank researchers have found that remittances from overseas migrants respond dramatically to regional income shocks, replacing upwards of 60% of lost income in households with international migrants. Further studies, cited below, have generalized those results. Furthermore, reductions in the cost of sending remittances, which occurs with the advent of mobile money, further mitigates exposure to risk in receiving households.

Also recently, authors have written on how communities come together in situations where formal economies are unavailable by external shocks (like disasters) or due to lack of access (due to conditions of abject poverty). In the case of the latter, Venkatesh (2008) provided gripping testimonies of how informal economies arose and developed in the South Side of Chicago in low trust environments via cash transactions and informal service contracts enforced by (and for the benefit of) local criminal gangs. These gangs were seen, surprisingly, as alternative coordinating mechanisms to settle claims between neighbors. In the case of the former, Storr and Grube (2013) in a series of papers has argued how "shared histories and perspectives, and the stability of social networks within the community" allows communities who suffered disasters to cope and endogenously resolve immediate and complex problems. Providing an example of these social networks in action, research has found that remittances flow quickly into areas affected by natural disasters when there is a technology in place for it to happen.

Taking these lines of inquiry together, the findings suggest that informal handling of risk takes place on a large scale and that social networks informally connect communities in ways that impact their economies. Recent trends suggest that the interaction of formal and informal economic processes is growing on a nationwide scale. First, the aggregate value of remittance flows is large and growing, nearing the value of foreign direct investment. Second, advancements that formalize previously informal transactions are expanding dramatically in emerging economies through various forms of branchless banking. National economies may be embedded in profoundly influential informal systems that have never been holistically studied.

On a more general note, this project touches lingering questions in economics. For example, some might argue that more stringent labor regulations should have increased informal employment, but in fact the opposite happened. And while the economic rationale for the former statement remains valid, we suggest that unions, as other types of social organizations, as examples of ways whereas people interact via organized networks, provide richer dimensions than those suggested by their interaction as mere economic agents. Hence, unions, as well as other types of faith-based, ethnic, community and other interest groups may provide ways of interaction that escape narrow economic outcomes.

====References====

• De Soto, Hernando (2000/2003) The Mystery of Capital. Basic Books.
• Losby, Jan; Else, John; Kingslow, Marcia; Edgcomb, Elaine; Malm, Erika and Vivian Kao (2002) The Informal Economy: A Literature Review. ISED Consulting and Research and the Aspen Institute Working Paper. http://www.kingslow-assoc.com/images/Informal_Economy_Lit_Review.pdf
• Townsend, Robert M. "Risk and Insurance in Village India." (1994)
• Fafchamps, Marcel, and Susan Lund. "Risk-sharing Networks in Rural Philippines." (2003)
• Weerdt, Joachim De, and Stefan Dercon. "Risk-sharing Networks and Insurance against Illness." (2006)
• Matthew O. Jackson, Tomas Rodriguez-Barraquer and Xu Tan. “Social Capital and Social Quilts: Network Patterns of Favor Exchange.” (2012)
• Yang D and H Choi."Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines"(2007)
• Kurosaki, Takashi. "Consumption vulnerability to risk in rural Pakistan." (2006)
• Jack, W, and T. Suri. "Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution” (2014)
• Engels, Friedrich (1844/1892) The Condition of the Working Class in England. http://www.gutenberg.org/files/17306/17306-h/17306-h.htm
• Venkatesh, Sudhir (2008) Gang Leader for a Day: A Rogue Sociologist Takes to the Streets. Penguin Books.
• Storr, Virgil and Laura Grube (2013) The Capacity for Self-Governance and Post-Disaster Resiliency. George Mason University, Department of Economics Working Paper 13-37. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2350830##
• Blumenstock, Joshua Evan and Fafchamps, Marcel and Eagle, Nathan. “Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda” (2011).
• Ratha, Dilip. "Workers’ remittances: an important and stable source of external development finance." (2005).
• Pénicaud, Claire, and Arunjay Katakam. State of the Industry 2013: Mobile Financial Services for the Unbanked. Rep. N.p.: GSMA MMU, 2013.

If interested, please list your name below.

'''Interested:'''
<li> Eloy Fisher (eloy.fisher@fulbrightmail.org) </li>
<li> Carolina Mattsson (carromattsson@gmail.com) </li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>

<li> Jakub Rojcek (jakub.rojcek@gmail.com) </li>

==Exploring Community Formation through Analysis of Scholarly Corpus (ArXiv)==

The Arxiv [http://arxiv.org/] is a free online repository of scientific preprint articles, mostly from physics and mathematics. It currently contains over 800,000 articles, dating back to 1991. Currently, a lot of the data associated with this repository is completely available: paper submission dates; full texts of papers; author names and coauthorship information. Additionally, there is some citations data available. (If necessary, I can also find papers' subdiscipline labels; submitting authors' email address domain names; other things.)

In the past I have been using these data to try and explore how communities of authors who have shared interests grow over time. For example, we can pinpoint the first ever paper about topological insulators, and search for all subsequent papers on that topic. As more papers are written, authors begin to join the field of research and to form strong ties by collaborating with one another. We can use the ArXiv data to visualize and analyze exactly how these communities form and grow.

===Brainstorming notes===
Isolation between topics, crossing interdisciplinary boundaries, finding (topological) separation distance between disciplines or research groups

Tipping points - can we look for separation of or mergers of two groups or disciplines?

Can we identify key papers (or groups of papers) that initiate ties between fields.

Sentiment analysis: can we identify when a paper's citation is an endorsement or a refutation?

Tools for Analysis: Change point detection; relational event models

Can we find more comprehensive/better citation data?

Lucene and indexing tools:
- Can we re-index our text database after removing stop words?
- Can we index the titles and abstracts?
- Elastic Search - tool for interacting with Lucene

This set of text-processing [http://alias-i.com/lingpipe/demos/tutorial/read-me.html tutorials] is pretty handy for background info and inspiration. The software package doesn't simply plug-into a Lucene/Solr/Elastic Search index, but it could be done. This package from [http://nlp.stanford.edu/software/ Standford] seems very capable.

===Next meeting===
Thursday @ 9AM in Coffee Shop

== PolComplex - Epistemic communities and policy dynamics in the UK Parliament ==

Over the last twenty years, there has been extensive debate about what the core topics in public policy are, and if they change according to type and number. Furthermore, it is still not clear how policy topics mutate and diffuse over time. Human-based text analysis of governmental documents has shed only some light on these research questions: 21 general topics have been proposed, while other accounts tend to restrict it to about five main clusters. Yet, this approach has not been able to devise a reliable and systemic method to capture topic dynamics, diffusion, and their relation to the human actors that talk about them.

This project is an extension of an exploratory research that intends to overcome such limitations, through the application of natural language processing (more specifically, dynamic topic modeling and sentiment analysis), topological and network analysis on a dataset containing UK House of Commons debates ranging from 1975 to 2014. The aim is manyfold. We hope to identify the structure and dynamics of epistemic policy communities - i.e. of political actors revolving around similar policy interest -, and how these relate to factors such as party membership, seniority, relevant historical events.

Some references:

[http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.734168 Taddy (2013), "Multinomial Inverse Regression for Text Analysis"] 

[https://www.cs.princeton.edu/~blei/papers/BleiLafferty2007.pdf Blei and Lafferty (2007), "A Correlated Topic Model of Science"] 

== Genomic variations from a chaotic mapping ==

Following Dabby CHAOS 6 (2), 1996 Musical variations from a chaotic mapping, this project will explore genomic variations in key metabolic genes that are known to be widely spread across bacterial phylogeny (e.g. the Nif cluster which is used in nitrogen fixation). There are several ways that genomic data can be treated as "music":

1) Nucleotide: 4 notes - A, G, C, T 
2) Amino Acids: 20 notes 
3) codon triplets: 64 notes 
4) tetra nucleotides: 256 notes. 

One possible goal is to take a seed sequence from some random organism and generate new variations. Then using homology search (e.g. BLAST) or phylogenetics, determine if that variation exists in nature? If not, perhaps model the potential protein folding (if possible?) to determine if that variation could exist in nature. This is VERY preliminary and can go in many directions.

If interested please list name below or contact Jarrod at jscott@bigelow.org

Reading
http://digitalcommons.olin.edu/cgi/viewcontent.cgi?article=1000&context=ahse_pub&sei-redir=1&referer=https%3A%2F%2Fscholar.google.com%2Fscholar%3Fhl%3Den%26q%3Ddabby%2Bchaos%26btnG%3D%26as_sdt%3D1%252C32%26as_sdtp%3D#search=%22dabby%20chaos%22

== Free Energy Theory ==
The Free Energy (FE) minimisation framework tries to explain how biological systems (such as a cell or a brain) self-organise in order to occupy the (often very limited number of) non-equilibrium states that minimise free energy. This is also known as active inference. A simple corollary of active inference is that agents behave as to minimise their prediction error, or the difference between prediction and reality. Thermodynamic free energy is a measure of energy available in a system to do useful work. This can be framed in an information theoretic setting, as the difference between how the world is being represented and how it actually is. A better fit means a lower information-theoretic free energy, as more resources are being put to ‘good use’ in representing the world. The overarching logic of FE theory is that a better model of the world help maintain structure and organisation, which ultimately helps the system resist increases in entropy.
 
 
This is not a set-in-stone project with any concrete aim (yet). We are a few people interested in exploring the theoretical and practical implications of these ideas, and you're more than welcome to join in!
 
 
'''Interested:'''
 
<ul>
<li> Jelle Bruineberg (j.bruineberg@gmail.com ) </li>
<li> Tobias Morville (tobiasmorville@gmail.com) </li>
<li> Susanne Pettersson (guspsusa85@student.edu.se) </li>
<li> Maggie Simon (margaret.w.simon@gmail.com) </li>
<li> Martina Steffen (martinasemail@gmail.com) </li>
<li> Sahil (sahilgar@usc.edu) </li>
<li> Tirtha (Tirtha.Bandy@csiro.au) </li>
<li> Anna (annza944@gmail.com) </li>
<li> Matt O (mmosmond@gmail.com) </li>
<li> Will Chang (williamkurtischang at gmail dot com) </li>
<li> Cobain (mrdc1g10@soton.ac.uk) </li>
</ul>
Reading:
 
http://rsif.royalsocietypublishing.org/content/10/86/20130475 
http://arxiv.org/abs/1503.04187 
http://www.mdpi.com/1099-4300/15/1/311 

==Improving the design of the power grid using our knowledge about network structure==
''Context''
Given all the renewable energy generation that is being installed and the increasing levels of uncertainty about the future power system, power transmission expansion planning is becoming more and more challenging. There is a lot of literature being published in the field, but it always applies"blind" techniques to the design, such as optimization where the possible lines to add to the system are represented as binary variables. This leads to optimization problems that are too large for real networks. As part of the European Comission FP7 project e-Highway, my team and I have developed methods to reduce the complexity of the network and work with a smaller system.
 
 
I would like to explore a different avenue. Maybe it is possible to describe the structure of good network designs in terms of global parameters. For instance, how does the degree distribution look like for efficient power networks? Then, we could feed that information into the optimization problem, reducing the search space.
 
 
''Data''
 
I have data originating in a European project from the FP7 programme.
 
 
''Relevance''
 
There is lots of interest in this field, a lot being published, lots of money going into projects and many research grants. Nobody seems to be looking at it from a structure perspective though.
 
 
''To know more...''
 
I uploaded a ppt with some initial ideas to my page on the wiki.
 
 
'''Interested:'''
'Sara '
'Carolina'
'Alice'
'Federico'
'Jean Gab'
'Sola'
'Ilaria'
'Daniel T'

==Comparison of Network vs Scaling Theory based Models in Ecology (Cobain - mrdc1g10@soton.ac.uk)==

One of the biggest difficulties ecologists face is trying to understand the ecosystem dynamics based on very little biological information (compared to the size of the system) - observational data is logistically difficult and can be very expensive to acquire, as well as often being very time consuming. In terms of modelling, to overcome this problem, two approaches have been utilised. 

The first is a network based approach whereby the nodes represent biomass density of a particular species (or a higher level taxanomic group and/or resources) and edges as the trophic interactions (who eats who). This method depends on what we know about the trophic behaviour of the species involved (which is often very limited and there can be many species to parameterise), and represents only the central tendency of what could potentially be very diverse behaviour within and between populations. 

The second approach is to use scaling theory to describe average trophic interactions and other biological processes based on individual organism body size along the size continuum, viewing the community as a very size structured dynamical system. This requires less specific knowledge about the organisms in the community ''per se'', however this again represents only the central tendency of a given size class of what could be very diverse behaviour. Functional differences can be potentially introduced (coupled benthic-pelagic systems for example), but to the detriment of braking down the predictability of the well known allometric scaling laws with size as specificity increases. 

There has been little to no comparison of these two methods of modelling ecological systems (at least to my knowledge) and the question arises that, given the same starting information, how well do both approaches model the dynamics of an ecosystem, given the limited biological information we have? Is one approach better at capturing energy flow through the system / community structure and stability etc. compared to the other? If the models vary then such information will better inform ecologists on which to use depending on what type of questions they are trying to answer. 

Therefore, the project proposed here aims to investigate these two methods. The community that is used to seed an experimental mesocosm setup (mesocosms are large tank set-ups designed to reflect the complexity of natural ecosystems but still being able to be artificially controlled and are still relatively simple), will be input into two models based on the separate approaches and then run to steady state. The model outputs will then be compared and contrasted with eachother as well as the mesocosm community at steady state. Further work may include examining perturbation dynamics, dependent on what data we have available.

'''Interested: Name / Email''' 
Martina Steffen (martinasemail@gmail.com)

==Dynamics of homicide (Matthew Ingram - mingram@albany.edu)==

Integrating temporal, spatial, and multi-level concepts

1:30pm in the coffee shop

Am interested in the discussion on this project...Sola Omoju

Interested - Nilton Cardoso

==Multiplex Adaptive Networks (Daniel - dtc65@cornell.edu)==

7 pm in the coffee shop

==Modeling brain diseases (or cancerous bio pathways) (Sahil - sahilgar@usc.edu)==
Interested people can put a meeting time as per their convenience here.
 
No meeting time indicated
 
One potential meeting time can be 3pm in coffee shop ?
 
Information theoretic algorithms can also be explored for the problem.
 
Discovering Structure in High-Dimensional Data Through Correlation Explanation. http://papers.nips.cc/paper/5580-positive-curvature-and-hamiltonian-monte-carlo
 
Maximally Informative Hierarchical Representations
of High-Dimensional Data. http://jmlr.org/proceedings/papers/v38/versteeg15.pdf

 
'''Interested:'''
 
<ul>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> Laura Condon (lcondon@mymail.mines.edu)</li>
</ul>
 
 

==Analysis of UK parliament speeches 1935-2014 (Stefano - etstefano@gmail.com)==

No meeting time indicated

==Mapping Complexity/Human Knowledge as a Complex Adaptive System (radjohn@live.com)==
2pm/Wed 6/10 in Conference Room (Tentative)

==Navigating Music, Brain and The Edge of Chaos (radjohn@live.com)==
10:45am/Wed 6/10 in Conference Room
9:00am/Thu 7/10 at the Coffee Shop

'''Interested:'''
Sahil Garg

==Analysis of rule-based modeling for dopamine-dependent synaptic plasticity (emilia.m.wysocka@gmail.com)==

----------------------------------------------------------------------------------
Modeling and analysis of phenomenons and evolution of stochastic, combinatorially complex signalling systems in a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system).

----------------------------------------------------------------------------------
Rule-based modeling features:

*biological systems as concurrent processes
*dynamics of post-translational modifications
*domain availability
*competitive binding
*causality and intrinsic structure
*binding sites
*interaction rules replace reaction equations
*infinite number of reactions with a small and finite number of rules
*reduction of parameter space
*“don't care don't write” - adjustable rule contextualization
*single reaction rule and parameters generalize classes of multiple rules
*modular and extensible language
*specification language & simulation/integration environment
*static and causal analysis
*Kappa/KaSim & BioNetGen/NFsim -- specification language/network-free simulator

----------------------------------------------------------------------------------
----------------------------------------------------------------------------------
Meetings:
----------------------------------------------------------------------------------
* WEDNESDAY 11/06/2015

Addressing problems in terms of the other aspects of the projects apart from the biological questions and verification (matching results to some published experiments or known behaviour). So my plan is as follows:

* divide the group (roughly) into people who look in to biological meaning and validation and the one which tries to do the analysis of system phenomenons and evolution of stochastic, combinatorially complex signalling systems in both a qualitative (directed acyclic graphs, networks) and quantitative way (time series generated for all agent/species formed/destroyed in the system) - all possible states, scenarios of the system abstracted from the biological meaning.

Things that could be modelled (look in dropbox folder: https://www.dropbox.com/sh/g080w60pt2vgi7v/AACHlMf2JwpP2EZqKq7Di6N2a/possible%20models?dl=0):

* to make things easier and have the modeling part done quickly- base the model on the dopamine-related synaptic plasticity (SBML ODE model): http://www.ebi.ac.uk/biomodels-main/MODEL1101170000;

* I found a series of papers suitable to the subject of the Complex Systems course, e.g.: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC344803/. I was thinking to even try to compare higher level brain data (networks, spiking ..) with purely molecular model

* noise and low copy number (http://www.princeton.edu/~wbialek/our_papers/bialek+setayeshgar_08.pdf); this is quite interesting as for sufficiency of binding events -> http://www.princeton.edu/~wbialek/our_papers/bialek+ranganathan_07.pdf

* spontaneous flipping of interactions (phosphorylations and others) between proteins, described by the god of non-linear dynamics (S. Strogatz)

----------------------------------------------------------------------------------
* THURSDAY 11/06/2015 : http://doodle.com/se23ibwtkrkccs4s

----------------------------------------------------------------------------------
Some refs:
*https://www.dropbox.com/sh/g080w60pt2vgi7v/AADUy7Ge-J-oSYkyTeYOo7V8a?dl=0)
*http://www.pps.univ-paris-diderot.fr/~jkrivine/homepage/Teaching.html

==Ecology non-working group (williamkurtischang at gee-mail dot com)==
[[Informal ecology interest group]].

==Making Supply Chains Resilient to Disasters (mh2905 att columbia dott edu)==
'''Key words''': resilience, natural hazards, supply chains, interdependency, interconnected risks, cascading failures

'''Rational''': I am interested in examining what kinds of network structures and features contribute to increasing resilience of supply chains to natural disasters. I believe this area of work is important because regional disasters negatively impact the global economy through disruptions in supply chain networks. The pioneer study published in Nature urges the need for making supply chains climate-smart (Levermann 2014). Also in the industry, the World Economic Forum published a report to address this issue in 2013. Few researches, however, assess and model the impacts of adverse weather on supply chains. I would like to evaluate the impacts based on modeling.

'''Data set''': Supply chains data of a multinational manufacturing company.Global climate data, disaster data, etc.

'''Possible techniques''': Agent-Based Model, Complexity science, network theory and evolution, complex adaptive systems, GIS, operations research, manufacturing engineering, and I am open to any techniques.

'''Myself''': As I joined the program yesterday, please find my background here.
http://tuvalu.santafe.edu/events/workshops/index.php/Masahiko_Haraguchi

'''Please put your names below if you want to be informed about this project by email'''

'''Interested''':
Masa Haraguchi

==From Ethnic Diversity to Religious Zeal: Retrospective/Predictive Construction of the World Ethnic Map (vdzutsati@asu.edu)==

There is a new dataset on ethnic groups (2014), which claims to include all ethnic groups of the world http://www.cidcm.umd.edu/mar/amar_project.asp. There are also several cross national datasets on religion and other variables of interest, normally, socio-economic http://www.thearda.com/Archive/CrossNational.asp. Coming from social sciences I thought it would be really interesting to apply methods and perspectives from other disciplines to social science data.

One idea that I have in mind is to explore how ethnicity and religion interact.

Quick empirical checks indicate that Subsaharan Africa is home to about 1,500 ethnic and subethnic groups, in comparison to about 90 ethnic groups in the Middle East and North Africa. India alone accounts for nearly 2,000 ethnic and subethnic groups, while Europe, including all the countries of the former USSR and large immigrant groups from other parts of the world, has only about 260 ethnic groups. The picture that emerges from this simple comparison is that the spread of Abrahamic religions appears to be associated with the high depletion rate of ethnic groups. The exception of China, which has little more than 50 ethnic groups, can be explained by the country’s long history of a centralized state.

The idea then is to assume that we have had the control group of nations that did not experience the influx of Abrahamic religions (or more precisely received limited exposure to them) and the experimental group that was exposed to the spread of Abrahamic religions. Since we know what the distribution of ethnic groups in the control group is we can project what the distribution of ethnic groups in the experimental group would be, if the group were not exposed to Abrahamic religions.

Of course, we could go in the other direction too and make a projection about future – what would happen to ethnic groups if all of them adopted an Abrahamic religion.

One of the challenges in this project is absence of a dynamical system, I.e. We don’t have data on how these things changed over time, but I still think that projection about the future and the past would be kinda cool:)

'''Interested''':

==Nationalist vs religious rebel violence (vdzutsati@asu.edu)==

I would like to propose yet another possible project. This time it’s on rebel violence. I have a large dataset on rebel (and government) violence (~1 million observations). The paper that used this data was published couple of months ago (Islamists and Nationalists: Rebel Motivation and Counterinsurgency in Russia’s North Caucasus). The dataset contains event counts, GIS data, demographic and economic characteristics and some other stuff. The primary hunch of the paper was to discover the differences between nationalist and Islamist rebels.

Given all the cool methods we are learning here, my first instinct is to use the methods and tools (TISEAN?) to explore and discover whether nationalist violence is substantively different from religious violence and try to answer why.

One problem that I can see is that the event coding was done by a machine, so this may have “contaminated” the data. But if both religious and nationalist data were contaminated equally then the difference should still be evident.

'''Interested''':

==[[Archived Projects ]]==

Presentations 2015

2015-06-29T22:13:12Z

Rbarnes:

{{Complex Systems Summer School 2015}}

9:00 - 9:15: Zimbabwe Agent-Based Model 

9:15 - 9:30: Zimbabwe Network Analysis 

9:30 - 9:45: Free-energy group: Introduction to the Free Energy Principle 

9:45 - 10:00: Free-energy group: Modeling and ecology 

10:00 - 10:15: PolComplex I 

10:15 - 10:30: BREAK 

10:30 - 10:45: Network analysis of ArXiv 

10:45 - 11:00: Powergrid I 

11:00 - 11:15: Network vs. Scaling 

11:15 - 11:30: 

11:30 - 11:45: 

12:00 - 1:00: 

1:00 - 1:15 Ebola virus spread analysis 

1:15 - 1:30: 

1:30 - 1:45: Processes to overcome ignorance: uncertainty – information – knowledge 

2:00 - 2:15: 

2:15 - 2:30: 

2:30 - 2:45: Cities 

2:45 - 3:00: BREAK 

3:00 - 3:15: Music and the Brain I 

3:15 - 3:30: Music and the Brain II 

3:30 - 3:45: Mapping Complexity I 

3:45-4:00: Mapping Complexity II 

4:00 - 4:15: 

4:15 - 4:30: 

4:30 - 4:45: Model building for bio-pathways with intrinsic dimensionality analysis (Presenter: Emilia) 

4:45-5:00: Social Resource Allocation Analysis: An Applied Application 

'''Projects not scheduled:'''

1. 
2.

Complex Systems Summer School 2015-After Hours

2015-06-27T15:26:02Z

Rbarnes: /* Saturday, June 27: Party At JP's Farm!!! */

{{Complex Systems Summer School 2015}}

==Saturday, June 27: Party At JP's Farm!!!==

I'd like to host a nice little summer party out at my family's farm south of town. Let's have a little summer BBQ!

Let's head out about 4:30-5:00 on Saturday, and spend the late afternoon into the evening there. We should pick up some things to cook at a grocery store on the way out.

JP's GTI 
1.JP 
2.Yared 
3.Melissa 
4.Federico 
5.Susanne 

JP's Totally Cool People Hauler 4Runner 
1.Jean-Gab 
2.Masa 
3.Sara 
4.Maria 
5.Alice 

Connor's well-dented Dodge Intrepid 
1.connor 
2.Glenn 
3.Daniel F. 
4.Sahil Garg 
5.Jakub 

Brent's Excellent Explorer 
1.Brent 
2.Tirtha 
3.Ilaria 
4.Jelle 
5.Sebastian 
6.Matt O 
7.Penny 

Joshua's Car 
1.Joshua 
2.Maggie 
3.Urs 
4.Keith 
5.toby 

Sander's Car 
1.Sander 
2.Kleber 
3.Emilia 
4.Chris 

Still Needs a Ride 
1. Richard 
2. 
3. 
...

==Museum of Indian Arts & Culture, 27 June at 10.00==

Hi there! Saturday 27 June I would like to go to the Museum of Indian Arts & Culture and then maybe go to another museum and have a walk in the town, enjoy the evening, make pictures, take a glass of redwine and observe people around. I love museums. If you want to join, do that. There is no fixed plans, no stress, so you can leave the walk whenever you want.

Anna

==Monday, June 22: Pop-Up Dumplings!==

JP's Car 
1.JP 
2.Haitao 
3. 
4. 
5. 

==Wednesdays==

St. John's College hosts a concert series on the college's athletic field every Wednesday evening from 6 to 8 p.m. These concerts are free and food/sodas are available to purchase.

==Anyday.. everyday??==

"Undefeated" Fuego baseball team plays at 6 everyday at Fort Marcy Field. Games are 6 dollars, beer is available in excess. Schedule is [http://santafefuego.com/santafe.asp?page=11&team=13&year=2015 here] 

We have two dates we will organize for the time being. 

Tuesday, 6/16 

Interested?

1. Matt O 
2. 
3. 
4. 
... 

Saturday, 6/20 

Interested?

1. 
2. 
3. 
4. 
... 

==CSSS Dance June 20==

[[Playlist submissions for Dance]]

==Soccer enthusiasts==

Soccer this Weekend? 
Saturday? 
1. 
2. 
Sunday? 
1. 
2. 

==Basketball==

Golden State 
1. Federico 
2. Connor 
3. ... 
Cleveland 
1. Matthew "King James" 
2. ... 
3. ... 

FYI ran into some breadloafers who said they're going to play Mondays. We should gather a crew to defeat them.

==Morning Yoga==

We meet at the gym at 7, there is an open space we can use. Bring a towel or a yoga mat, if you have it.
Rotating leading turns, feel free to share your favourite yoga position!

Mon-Thu 1 hour
Fri 45 min (because of the shuttle to SFI)

Interested?

Ilaria 
Carolina (M,W,F) 
Alice 

==Saturdays==

* Contra Dancing, 1st & 3rd Sat. Albuquerque Square Dance Center. [http://folkmads.org/events/albuquerque-events/ Details]
** Interested individuals: Richard Barnes
* Contra Dancing, 2nd & 4th Sat. Santa Fe Odd Fellow's Hall. [http://folkmads.org/events/santa-fe-events/ Details]
** Interested individuals: Richard Barnes

== Amma in Santa Fe – June 20, 2015.==
http://amma.org/meeting-amma/north-america/santa-fe
Free Morning Program 10am–approx. 3pm
8:00am: The token line opens. To ensure everyone has an equal chance of getting an early token, please refrain from forming a line until then.
8:30am: Tokens are handed out and guests are escorted to seats.
10:00am: Amma enters the hall and conducts a short meditation.
10:30am: Amma begins to embrace those who have come.
12:30pm: Lunch is served until thirty minutes after Amma leaves the hall.
About morning programs ›

EVERYONE IS WELCOME
To meet Amma, you will need a token which is issued on a first-come-first-served basis. Tokens are limited, so please arrive early.

Location: Buffalo Thunder Resort
30 Buffalo Thunder Trail 
Santa Fe, NM 87506
United States
Hotel: 877.848.6337

Meeting time: Since we take 45 min to arrive the Resort, we should plan to leave at 7:15 AM.
Interested:
1. Nilton 

==June 20, 10:00am - Bandelier Field Trip==

We're taking a trip to [http://www.nps.gov/band/index.htm Bandelier National Monument] on Saturday June 20th. Please visit the [[Bandelier 2015 | Bandelier Field Trip]] Page to sign up!

==June 25 Rodeo de Santa Fe==

Thursday, June 25!

Come on down for the 66th annual Rodeo de Santa Fe! Watch real-life cowboys get thrown off of various species of raging livestock for their competition and your entertainment. Starts at 7:00pm, we should leave SJC about 6:00. http://rodeodesantafe.org/

Juni's Car 
1. Juni 
2. María Pereda 
3. Sara Lumbreras 
4. Sahil Garg 
5. Federico Battiston 

JP's Ferrari (5 seats) 
1.JP 
2. Jakub Rojcek 
3. Glenn Magerman 
4. Dan Hedblom 
5. Yared Abebe 

JP's Lamborghini (5 seats) (NOT ACTUALLY A LAMBORGHINI) 
1. Jean-Gab 
2. Junming Huang 
3. Danqing Liu 
4. Martina Steffen 
5. Song Binyang 

Christine's Jeep (5 seats) 
1. Christine 
2. Sebastian 
3. Sola 
4. Melissa 
5. Masa 

Sam's Car (5 seats total) 
1. Sam 
2. Matthew H 
3. Emilia 
4. Tirtha 
5. Alice 

Sharon's Car (5 seats) 
1. Sharon 
2. Stefano 
3. Tolga 
4. Laurence 
5. Penny Mealy 
 

Joshua's Car (5 seats) 
1. Joshua 
2. Chao Fan 
3. Haitao Shang 
4. Susanne 
5. Tirtha 
 

Laura's Car (5 seats) 
1. Laura 
2. Michael S 
3. Richard Barnes 
4. Jae B. Cho 
5. Alejandro 

 Connor's Car (5 seats) 
1. Connor 
2. Matt O 
3. 
4. Andre 
5. Daniel Citron 

 Valery's Car (7 seats) 
1. Valery 
2. Marie-Pierre 
3. Chris 
4. Urs 
5. Anna 
6. 
7. 

Needs a ride 
1. Andy 
2. Nilton Cardoso 
3. Ilaria 
4. Jelle 

==Grand Canyon Trip , June 26 - June 28==
'''There is a small group heading to the Grand Canyon the last weekend (leaving Jun26 afternoon, returning Jun28). We will be renting a car, the drive is about 6.5 hours. Anyone interested in joining please contact Juan or Nilton.'''
 
Interested:
 
1.
 

==Cinema: Inside Out, Sunday (June 28) ==
Cinema by the Railyard (Violet Crown) 
Movie starts at 8:15 - we'll eat dinner nearby somewhere before 
<strike>The SFI-Bus will probably take us downtown at 5:30.</strike> Ride shares! 

Cars 

JP's Wicked-Cool GTI 
1.[[JP]] 
2.Laurence 
3. Sahil Garg 
4. Richard 
5.Tirtha 

==Jemez Hot Springs, Saturday (27th of June) ==
Day trip to natural hot springs near the town of Jemez Springs (about an hour drive away) 
Meet at 10:00am at the turning circle and hopefully be back in plenty of time for JP's BBQ! 

Cars 
Valery's Tiny Van 
1.Valery 
2.Cobain 
3.Toby 
4.Emilia 
5.Sahil (hope to join) 
6.Will 
7. 

Interested

Complex Systems Summer School 2015-After Hours

2015-06-25T19:52:54Z

Rbarnes: /* Cinema: Inside Out, Sunday (June 28) */

{{Complex Systems Summer School 2015}}

==Saturday, June 27: Party At JP's Farm!!!==

I'd like to host a nice little summer party out at my family's farm south of town. Let's have a little summer BBQ!

Let's head out about 4:30-5:00 on Saturday, and spend the late afternoon into the evening there. We should pick up some things to cook at a grocery store on the way out.

JP's GTI 
1.JP 
2.Yared 
3.Melissa 
4.Federico 
5.Susane 

JP's Totally Cool People Hauler 4Runner 
1.<driver needed> 
2.Masa 
3. 
4. 
5. 

Connor's well-dented Dodge Intrepid 
1.connor 
2.Glenn 
3.Daniel F. 
4.Sahil Garg 
5.Jakub 

Brent's Excellent Explorer 
1.Brent 
2.Tirtha 
3.Ilaria 
4. 
5. 
6. 
7-yes-7 

Joshua's Car 
1.Joshua 
2. 
3. 
4. 
5. 

Still Needs a Ride 
1. 
2. 
3. 
...

==Monday, June 22: Pop-Up Dumplings!==

JP's Car 
1.JP 
2.Haitao 
3. 
4. 
5. 

==Wednesdays==

St. John's College hosts a concert series on the college's athletic field every Wednesday evening from 6 to 8 p.m. These concerts are free and food/sodas are available to purchase.

==Anyday.. everyday??==

"Undefeated" Fuego baseball team plays at 6 everyday at Fort Marcy Field. Games are 6 dollars, beer is available in excess. Schedule is [http://santafefuego.com/santafe.asp?page=11&team=13&year=2015 here] 

We have two dates we will organize for the time being. 

Tuesday, 6/16 

Interested?

1. Matt O 
2. 
3. 
4. 
... 

Saturday, 6/20 

Interested?

1. 
2. 
3. 
4. 
... 

==CSSS Dance June 20==

[[Playlist submissions for Dance]]

==Soccer enthusiasts==

Soccer this Weekend? 
Saturday? 
1. 
2. 
Sunday? 
1. 
2. 

==Basketball==

Golden State 
1. Federico 
2. Connor 
3. ... 
Cleveland 
1. Matthew "King James" 
2. ... 
3. ... 

FYI ran into some breadloafers who said they're going to play Mondays. We should gather a crew to defeat them.

==Morning Yoga==

We meet at the gym at 7, there is an open space we can use. Bring a towel or a yoga mat, if you have it.
Rotating leading turns, feel free to share your favourite yoga position!

Mon-Thu 1 hour
Fri 45 min (because of the shuttle to SFI)

Interested?

Ilaria 
Carolina (M,W,F) 
Alice 

==Saturdays==

* Contra Dancing, 1st & 3rd Sat. Albuquerque Square Dance Center. [http://folkmads.org/events/albuquerque-events/ Details]
** Interested individuals: Richard Barnes
* Contra Dancing, 2nd & 4th Sat. Santa Fe Odd Fellow's Hall. [http://folkmads.org/events/santa-fe-events/ Details]
** Interested individuals: Richard Barnes

== Amma in Santa Fe – June 20, 2015.==
http://amma.org/meeting-amma/north-america/santa-fe
Free Morning Program 10am–approx. 3pm
8:00am: The token line opens. To ensure everyone has an equal chance of getting an early token, please refrain from forming a line until then.
8:30am: Tokens are handed out and guests are escorted to seats.
10:00am: Amma enters the hall and conducts a short meditation.
10:30am: Amma begins to embrace those who have come.
12:30pm: Lunch is served until thirty minutes after Amma leaves the hall.
About morning programs ›

EVERYONE IS WELCOME
To meet Amma, you will need a token which is issued on a first-come-first-served basis. Tokens are limited, so please arrive early.

Location: Buffalo Thunder Resort
30 Buffalo Thunder Trail 
Santa Fe, NM 87506
United States
Hotel: 877.848.6337

Meeting time: Since we take 45 min to arrive the Resort, we should plan to leave at 7:15 AM.
Interested:
1. Nilton 

==June 20, 10:00am - Bandelier Field Trip==

We're taking a trip to [http://www.nps.gov/band/index.htm Bandelier National Monument] on Saturday June 20th. Please visit the [[Bandelier 2015 | Bandelier Field Trip]] Page to sign up!

==June 25 Rodeo de Santa Fe==

Thursday, June 25!

Come on down for the 66th annual Rodeo de Santa Fe! Watch real-life cowboys get thrown off of various species of raging livestock for their competition and your entertainment. Starts at 7:00pm, we should leave SJC about 6:00. http://rodeodesantafe.org/

Juni's Car 
1. Juni 
2. María Pereda 
3. Sara Lumbreras 
4. Sahil Garg 
5. Federico Battiston 

JP's Ferrari (5 seats) 
1.JP 
2. Jakub Rojcek 
3. Glenn Magerman 
4. Dan Hedblom 
5. Yared Abebe 

JP's Lamborghini (5 seats) 
1. <driver needed> 
2. Junming Huang 
3. Danqing Liu 
4. Martina Steffen 
5. Song Binyang 

Christine's Jeep (5 seats) 
1. Christine 
2. Sebastian 
3. Sola 
4. Melissa 
5. Masa 

Sam's Car (5 seats total) 
1. Sam 
2. Matthew H 
3. Emilia 
4. Tirtha 
5. Alice 
 

Sharon's Car (5 seats) 
1. Sharon 
2. Stefano 
3. Tolga 
4. Laurence 
5. Penny Mealy 
 

Joshua's Car (5 seats) 
1. Joshua 
2. Chao Fan 
3. Haitao Shang 
4. Susanne 
5. Tirtha 
 

Laura's Car (5 seats) 
1. Laura 
2. Michael S 
3. Richard Barnes 
4. Jae B. Cho 
5. Alejandro 

 Connor's Car (5 seats) 
1. Connor 
2. Matt O 
3. Jean-Gab 
4. Andre 
5. Daniel Citron 

 Valery's Car (7 seats) 
1. Valery 
2. Marie-Pierre 
3. Chris 
4. Urs 
5. Anna 
6. 
7. 

Needs a ride 
1. Andy 
2. Nilton Cardoso 
3. Ilaria 
4. Jelle 

==Grand Canyon Trip , June 26 - June 28==
'''There is a small group heading to the Grand Canyon the last weekend (leaving Jun26 afternoon, returning Jun28). We will be renting a car, the drive is about 6.5 hours. Anyone interested in joining please contact Juan or Nilton.'''
 
Interested:
 
1.
 

==Cinema: Inside Out, Sunday (June 28) ==
Cinema by the Railyard (Violet Crown) 
Movie starts at 8:15 - we'll eat dinner nearby somewhere before 
<strike>The SFI-Bus will probably take us downtown at 5:30.</strike> Ride shares! 

Cars 

JP's Wicked-Cool GTI 
1.[[JP]] 
2.Laurence 
3. Sahil Garg 
4. Richard 
5. 

==Jemez Hot Springs, Saturday (27th of June) ==
Day trip to natural hot springs near the town of Jemez Springs (about an hour drive away) 
Meet at 10:00am at the turning circle and hopefully be back in plenty of time for JP's BBQ! 

Cars 

Interested 

Cobain 
Toby

Bandelier 2015

2015-06-20T15:05:56Z

Rbarnes:

{{Complex Systems Summer School 2015}}

Please sign up here so we know who's going. 
Also: If you have a car, please put it down. The more cars we have, the more people we can take. 

We'll meet Saturday at 10:00am in the parking circle.

Please remember to bring a hat, sunscreen, water, hiking shoes, and anything else you'll need for a day out in the field.

If you would like to rent a car please visit the [http://santafe.edu/about/contact/ground/ SFI website] for more info

==Cars:==

===Juniper's Car (4 seats)===
1. Juni 
2. Matthew Cobain 
3. 
4. Melissa 
5. Jelle : ) 

===JP's Super Awesome Volkswagen (5 seats)===

1. JP 
2. María Pereda 
3. Tirthankar Bandyopadhyay 
4. Dan Hedblom 
5. Stefano 

===JP's Totally Cool Offroad Toyota 4Runner (5 seats)===
1.Jean-Gab 
2. Ilaria Bertazzi 
3. Emilia Wysocka 
4. Rosana Rogiski 
5. Gilia Patterson 

===Sander's Car (4 seats))===
1.Sander 
2. Alice Patania 
3. Jakub Rojcek 
4. Glenn Magerman 
5. Sola

Sam or Maggie can drive here or contribute an additional car, if needed.

===Christine's Car (5 seats)===
1. Christine 
2. Vanessa 
3. Federico Battiston 
4. Yared Abebe 
5. Sebastian Poledna 

===Uttam's car (3 seats).. if needed===
1. 
2.

===Sharon's Car (5 seats)===
1. Sharon 
2. Michael 
3. Matt H 
4. Sara 

===Valery's Famous Van (7 seats)===
1. Valery 
2. 
3. Matt O 
4. Andre V 
5. Tolga 
6. Alejandro 
7. Urs 

===Carolina's Super New Mexican Outback (5 seats)===
1. Carolina 
2. 
3. Jelle 
4. Susanne 
5. Anna 

===Joshua's Car (5 seats)===
1. Joshua 
2. Jae 
3. Andy 
4. Daniel 
5. Masa 

===Sam/Maggie's Car (5 seats)===
1. Maggie/Sam 
2. Keith 
3. Richard 
4. Kleber Neves 
5. Sahil Garg 

==Without car ==
There are many people in this world, who don't have cars.

Playlist submissions for Dance

2015-06-19T19:21:40Z

Rbarnes:

{{Complex Systems Summer School 2015}}

1. '''Wrecking Ball, by August Burns Red''' (spotify:track:7kkRhGDCAVd5YVoieTChxG) 
2. '''... Baby One More Time, by August Burns Red''' (spotify:track:6C06UooIUOVLNEpr7HFl7D) 
3. '''Survivor,by Betraying the Martyrs''' (spotify:track:4bNBRfeQN2d5rWuBAOxZ50) 
4. '''Are you with me - radio edit, by Lost Frequencies''' (spotify:track:6CO2dTjTIXVJoBHPEN6Qcg) 
5. '''We have it all, by Pennywise''' (spotify:track:01kc2ZJFgrXpLiWDhjY5zL) 
6. '''A day to remember, by Authority Zero''' (spotify:track:0xsyovSsrfBPlxw3whvwAH) 
7. '''Reach for the sky, by Social Distortion''' (spotify:track:17OLUsQ49x9a3YTRCt9Ita) 
8. Mexican Radio by Wall of Voodoo off the album Mexican Radio 
9. In the Summertime by The Rural Alberta Advantage off the album Hometowns 
10. Young Blood by the Naked and Famous off the album Passive Me, Aggressive You 
11. And She Was by the Talking Heads off the album Little Creatures 
12. My Type by Saint Motel off the My Type EP 
13. Ca Plane Pour Moi by the Plastic Betrand off the album Plastic Bertrand 
14. Stolen Dance by Milky Chance off the album Stolen Dance 
15. Pools by Glass Animals off the album ZABA 
16. About to Die by The Dirty Projectors off the album Swing Lo Magellan 
17. Rollercoaster by The Bleachers off the album I Wanna Get Better 
18. Cecilia and the Satellite by Andrew McMahon off the album Andrew McMahon and the Wilderness 
19. 96 Tears by ? & the Mysterious off the album 96 Tears 
20. With a Girl Like You by the Troggs of the EP Collection 
21. Victoria by the Kinks off the album The Best of the Kinks 
22. Telephone Line by ELO off the album The Essential Electric Light Orchestra 
23. Paris 1919 by John Cale off the album Paris 1919 
24. Les Champs Elysees by Joe Dassin off the album Les Champs Elysees 
25. Look on Down from the Sky by Mazzy Star off the album Among my Swan 
26. Paisley Park by Prince off the album Around the World in a Day 
27. When You Were Mine by Prince off the album The B-Sides 
28. Happiness by Molly Drake off the album Molly Drake 
29. Unbelievers by Vampire Weekend off the album Modern Vampires of the City 
30. Lua by Bright Eyes off the album I’m Wide Awake, It’s Morning 
31. A Sunshine Fix by Olivia Tremor Control off the album Presents: Singles and Beyond 
32. Recovery by Frank Turner off the album Recovery 
33. Godspeed by Jenny Lewis off the album Acid Tongue 
34. Closer by Tegan and Sara off the album Heartthrob 
35. Cannonball by Damien Rice off the album O 
36. Breezeblocks by Alt-J off the album An Awesome Wave 
37. Adelaide by Ben Folds off the album supersunnyspeedgraphic 
38. Le Petit Pain Au Chocolat by Joe Dassin off the album Joe Dassin Eternel 
39. Everywhere with Helicopter by Jason Isbell and 400 Unit off the album Sing for Your Meat 
40. Alone Again Or by Love off the album Forever Changes 
41. Trash by The New York Dolls of the album New York Dolls 
42. English Rose by The Jam off the album All Mod Cons 
43. Ne Me Laisse Pas L’Aimer by Brigitte Bardot off the album BB 64 
44. Ho Hey by The Lumineers off the album The Lumineers 
45. San Francisco by the Mowgli’s off the album Love’s Not Dead 
46. Bracelet of Fingers by The Pretty Things off the album SF Sorrow 
47. Do Your Realize by The Flaming Lips off the album Yoshimi Battles the Pink Robots 
48. Stuck on a Puzzle by Alex Turner 
49. Video Games by Lana Del Ray 
50. Sea of Love by Cat Power 
51. Maps by the Yeah Yeah Yeahs 
52. Like a White Star, Tangled and Far, Tulip That's What You Are by Tyrannosaurus Rex 
53. Bailando, by Enrique Iglesias 
54. Gasolina, by Daddy Yankee 
55. Noche de sexo, by Romeo Santos 
56. Loca by Shakira 
57. Hips don't lie by Shakira 
58. Back that a$$ up by Juvenile 
59. Crazy bout my boyfriend (loopy) by Sissy Nobby 
60. Kill me by Make the girl dance 
61. Bulletproof by La Roux 
62. Dance with me tonight by Olly Murs 
63. Ojos Asi by Shakira 
64. Dutty Love by Don Omar 
65. Zero by Yeah Yeah Yeahs 
66. Modern Love by David Bowie 
67. Lust for Life by Iggy Pop 
68. Safe and Sound by Capital Cities 
69. Insane in the brain, by Cypress Hill 
70. Wannabe, by Spice Girls 

Swing Section:

71. Fine Brown Frame, by Dianne Reeves & Lou Rawls 
72. Ain't No Sunshine (Bill Withers) 
73. Fly Me To The Moon (Frank Sinatra) 
74. Jump, Jive, an' Wail (Brian Setzer) 
75. Don't Get Around Much Any More (Michael Buble) 
76. Zoot Suit Riot (Cherry Poppin' Daddies) 
77. Sing, Sing, Sing (Benny Goodman) 
78. In The Mood (Glenn Miller) 
79. Hollywood Nocturne (Glenn Miller)

Waltz Section:

80. See Through Blue (Beth Orton) 
81. Smitten (Contratopia) 
82. Ballroom Echoes (Contratopia) 
83. Julia's Waltz (The Free Raisins) 
84. Josephine's Waltz (Perpetual e-Motion)

Playlist submissions for Dance

2015-06-19T19:21:24Z

Rbarnes:

{{Complex Systems Summer School 2015}}

1. '''Wrecking Ball, by August Burns Red''' (spotify:track:7kkRhGDCAVd5YVoieTChxG) 
2. '''... Baby One More Time, by August Burns Red''' (spotify:track:6C06UooIUOVLNEpr7HFl7D) 
3. '''Survivor,by Betraying the Martyrs''' (spotify:track:4bNBRfeQN2d5rWuBAOxZ50) 
4. '''Are you with me - radio edit, by Lost Frequencies''' (spotify:track:6CO2dTjTIXVJoBHPEN6Qcg) 
5. '''We have it all, by Pennywise''' (spotify:track:01kc2ZJFgrXpLiWDhjY5zL) 
6. '''A day to remember, by Authority Zero''' (spotify:track:0xsyovSsrfBPlxw3whvwAH) 
7. '''Reach for the sky, by Social Distortion''' (spotify:track:17OLUsQ49x9a3YTRCt9Ita) 
8. Mexican Radio by Wall of Voodoo off the album Mexican Radio 
9. In the Summertime by The Rural Alberta Advantage off the album Hometowns 
10. Young Blood by the Naked and Famous off the album Passive Me, Aggressive You 
11. And She Was by the Talking Heads off the album Little Creatures 
12. My Type by Saint Motel off the My Type EP 
13. Ca Plane Pour Moi by the Plastic Betrand off the album Plastic Bertrand 
14. Stolen Dance by Milky Chance off the album Stolen Dance 
15. Pools by Glass Animals off the album ZABA 
16. About to Die by The Dirty Projectors off the album Swing Lo Magellan 
17. Rollercoaster by The Bleachers off the album I Wanna Get Better 
18. Cecilia and the Satellite by Andrew McMahon off the album Andrew McMahon and the Wilderness 
19. 96 Tears by ? & the Mysterious off the album 96 Tears 
20. With a Girl Like You by the Troggs of the EP Collection 
21. Victoria by the Kinks off the album The Best of the Kinks 
22. Telephone Line by ELO off the album The Essential Electric Light Orchestra 
23. Paris 1919 by John Cale off the album Paris 1919 
24. Les Champs Elysees by Joe Dassin off the album Les Champs Elysees 
25. Look on Down from the Sky by Mazzy Star off the album Among my Swan 
26. Paisley Park by Prince off the album Around the World in a Day 
27. When You Were Mine by Prince off the album The B-Sides 
28. Happiness by Molly Drake off the album Molly Drake 
29. Unbelievers by Vampire Weekend off the album Modern Vampires of the City 
30. Lua by Bright Eyes off the album I’m Wide Awake, It’s Morning 
31. A Sunshine Fix by Olivia Tremor Control off the album Presents: Singles and Beyond 
32. Recovery by Frank Turner off the album Recovery 
33. Godspeed by Jenny Lewis off the album Acid Tongue 
34. Closer by Tegan and Sara off the album Heartthrob 
35. Cannonball by Damien Rice off the album O 
36. Breezeblocks by Alt-J off the album An Awesome Wave 
37. Adelaide by Ben Folds off the album supersunnyspeedgraphic 
38. Le Petit Pain Au Chocolat by Joe Dassin off the album Joe Dassin Eternel 
39. Everywhere with Helicopter by Jason Isbell and 400 Unit off the album Sing for Your Meat 
40. Alone Again Or by Love off the album Forever Changes 
41. Trash by The New York Dolls of the album New York Dolls 
42. English Rose by The Jam off the album All Mod Cons 
43. Ne Me Laisse Pas L’Aimer by Brigitte Bardot off the album BB 64 
44. Ho Hey by The Lumineers off the album The Lumineers 
45. San Francisco by the Mowgli’s off the album Love’s Not Dead 
46. Bracelet of Fingers by The Pretty Things off the album SF Sorrow 
47. Do Your Realize by The Flaming Lips off the album Yoshimi Battles the Pink Robots 
48. Stuck on a Puzzle by Alex Turner 
49. Video Games by Lana Del Ray 
50. Sea of Love by Cat Power 
51. Maps by the Yeah Yeah Yeahs 
52. Like a White Star, Tangled and Far, Tulip That's What You Are by Tyrannosaurus Rex 
53. Bailando, by Enrique Iglesias 
54. Gasolina, by Daddy Yankee 
55. Noche de sexo, by Romeo Santos 
56. Loca by Shakira 
57. Hips don't lie by Shakira 
58. Back that a$$ up by Juvenile 
59. Crazy bout my boyfriend (loopy) by Sissy Nobby 
60. Kill me by Make the girl dance 
61. Bulletproof by La Roux 
62. Dance with me tonight by Olly Murs 
63. Ojos Asi by Shakira 
64. Dutty Love by Don Omar 
65. Zero by Yeah Yeah Yeahs 
66. Modern Love by David Bowie 
67. Lust for Life by Iggy Pop 
68. Safe and Sound by Capital Cities 
69. Insane in the brain, by Cypress Hill 
70. Wannabe, by Spice Girls 

Swing Section:

71. Fine Brown Frame, by Dianne Reeves & Lou Rawls 
72. Ain't No Sunshine (Bill Withers) 
73. Fly Me To The Moon (Frank Sinatra) 
74. Jump, Jive, an' Wail (Brian Setzer) 
75. Don't Get Around Much Any More (Michael Buble) 
76. Zoot Suit Riot (Cherry Poppin' Daddies) 
77. Sing, Sing, Sing (Benny Goodman) 
78. In The Mood (Glenn Miller) 
79. Hollywood Nocturne (Glenn Miller)

Waltz Section:

80. See Through Blue (Beth Orton)
81. Smitten (Contratopia)
82. Ballroom Echoes (Contratopia)
83. Julia's Waltz (The Free Raisins)
84. Josephine's Waltz (Perpetual e-Motion)

Playlist submissions for Dance

2015-06-19T19:05:57Z

Rbarnes:

Playlist submissions for Dance

2015-06-19T19:03:32Z

Rbarnes:

Playlist submissions for Dance

2015-06-19T19:02:53Z

Rbarnes:

{{Complex Systems Summer School 2015}}

1. '''Wrecking Ball, by August Burns Red''' (spotify:track:7kkRhGDCAVd5YVoieTChxG) 
2. '''... Baby One More Time, by August Burns Red''' (spotify:track:6C06UooIUOVLNEpr7HFl7D) 
3. '''Survivor,by Betraying the Martyrs''' (spotify:track:4bNBRfeQN2d5rWuBAOxZ50) 
4. '''Are you with me - radio edit, by Lost Frequencies''' (spotify:track:6CO2dTjTIXVJoBHPEN6Qcg) 
5. '''We have it all, by Pennywise''' (spotify:track:01kc2ZJFgrXpLiWDhjY5zL) 
6. '''A day to remember, by Authority Zero''' (spotify:track:0xsyovSsrfBPlxw3whvwAH) 
7. '''Reach for the sky, by Social Distortion''' (spotify:track:17OLUsQ49x9a3YTRCt9Ita) 
8. Mexican Radio by Wall of Voodoo off the album Mexican Radio 
9. In the Summertime by The Rural Alberta Advantage off the album Hometowns 
10. Young Blood by the Naked and Famous off the album Passive Me, Aggressive You 
11. And She Was by the Talking Heads off the album Little Creatures 
12. My Type by Saint Motel off the My Type EP 
13. Ca Plane Pour Moi by the Plastic Betrand off the album Plastic Bertrand 
14. Stolen Dance by Milky Chance off the album Stolen Dance 
15. Pools by Glass Animals off the album ZABA 
16. About to Die by The Dirty Projectors off the album Swing Lo Magellan 
17. Rollercoaster by The Bleachers off the album I Wanna Get Better 
18. Cecilia and the Satellite by Andrew McMahon off the album Andrew McMahon and the Wilderness 
19. 96 Tears by ? & the Mysterious off the album 96 Tears 
20. With a Girl Like You by the Troggs of the EP Collection 
21. Victoria by the Kinks off the album The Best of the Kinks 
22. Telephone Line by ELO off the album The Essential Electric Light Orchestra 
23. Paris 1919 by John Cale off the album Paris 1919 
24. Les Champs Elysees by Joe Dassin off the album Les Champs Elysees 
25. Look on Down from the Sky by Mazzy Star off the album Among my Swan 
26. Paisley Park by Prince off the album Around the World in a Day 
27. When You Were Mine by Prince off the album The B-Sides 
28. Happiness by Molly Drake off the album Molly Drake 
29. Unbelievers by Vampire Weekend off the album Modern Vampires of the City 
30. Lua by Bright Eyes off the album I’m Wide Awake, It’s Morning 
31. A Sunshine Fix by Olivia Tremor Control off the album Presents: Singles and Beyond 
32. Recovery by Frank Turner off the album Recovery 
33. Godspeed by Jenny Lewis off the album Acid Tongue 
34. Closer by Tegan and Sara off the album Heartthrob 
35. Cannonball by Damien Rice off the album O 
36. Breezeblocks by Alt-J off the album An Awesome Wave 
37. Adelaide by Ben Folds off the album supersunnyspeedgraphic 
38. Le Petit Pain Au Chocolat by Joe Dassin off the album Joe Dassin Eternel 
39. Everywhere with Helicopter by Jason Isbell and 400 Unit off the album Sing for Your Meat 
40. Alone Again Or by Love off the album Forever Changes 
41. Trash by The New York Dolls of the album New York Dolls 
42. English Rose by The Jam off the album All Mod Cons 
43. Ne Me Laisse Pas L’Aimer by Brigitte Bardot off the album BB 64 
44. Ho Hey by The Lumineers off the album The Lumineers 
45. San Francisco by the Mowgli’s off the album Love’s Not Dead 
46. Bracelet of Fingers by The Pretty Things off the album SF Sorrow 
47. Do Your Realize by The Flaming Lips off the album Yoshimi Battles the Pink Robots 
48. Stuck on a Puzzle by Alex Turner 
49. Video Games by Lana Del Ray 
50. Sea of Love by Cat Power 
51. Maps by the Yeah Yeah Yeahs 
52. Like a White Star, Tangled and Far, Tulip That's What You Are by Tyrannosaurus Rex 
53. Bailando, by Enrique Iglesias 
54. Gasolina, by Daddy Yankee 
55. Noche de sexo, by Romeo Santos 
56. Loca by Shakira 
57. Hips don't lie by Shakira 
58. Back that a$$ up by Juvenile 
59. Crazy bout my boyfriend (loopy) by Sissy Nobby 
60. Kill me by Make the girl dance 
61. Bulletproof by La Roux 
62. Dance with me tonight by Olly Murs 
63. Ojos Asi by Shakira 
64. Dutty Love by Don Omar 
65. Zero by Yeah Yeah Yeahs 
66. Modern Love by David Bowie 
67. Lust for Life by Iggy Pop 
68. Safe and Sound by Capital Cities 
69. Insane in the brain, by Cypress Hill 
70. Wannabe, by Spice Girls 
71. Fine Brown Frame, by Dianne Reeves & Lou Rawls
72. Ain't No Sunshine (Bill Withers)
73. Fly Me To The Moon (Frank Sinatra)
74. Jump, Jive, an' Wail (Brian Setzer)
75. Don't Get Around Much Any More (Michael Buble)
76. Zoot Suit Riot (Cherry Poppin' Daddies)
77. Sing, Sing, Sing (Benny Goodman)

Complex Systems Summer School 2015-Tutorials

2015-06-19T00:11:40Z

Rbarnes: /* Git: A Crash Course */

{{Complex Systems Summer School 2015}}

CSSS participants come from a wide range of disciplines. Participants are encouraged to share their knowledge by organizing their own tutorials.

You can schedule your own tutorial here, they will be held in the ESL study hall. Please do not schedule during other CSSS Lectures.

try to use this template:

=='''Tutorial: Skilled action, complex systems science and the Free Energy Principle''' ==
'''Speaker: Jelle Bruineberg''' 
'''Date & Time: June 11th, 20:00''' 
'''Motivation and content: Quite some people seemed to be interested in the "Variational Approaches to Mind and Life" project that we are trying to get of the ground. Apart from this, some people were curious how philosophy relates to complex systems science. I would like to present my own work on skilled action and relate it to complex systems science. After this, I will sketch how the Free Energy Principle (the principle to be studied in the Variational Approaches to Mind and Life group) relates to this work. This is the point, where, I hope, the presentation part will stop and the brainstorm/discussion session will take over.''' 
'''Prerequisite: Being open to a bit of philosophy :)''' 
'''Slides: will follow''' 
'''Paper: [http://journal.frontiersin.org/article/10.3389/fnhum.2014.00599/abstract]''' 

=='''Introduction to NetLogo''' ==
'''Speaker:''' Christine Harvey (ceharvey@mitre.org) 
'''Date & Time:''' June 16th 7pm in Tutorial Room 
'''Motivation and content:''' Basic introduction to NetLogo for new users/programmers. Quick overview of the screens, language and possibilities. And standard documentation practices for the model. Walkthrough editing a model. 
'''Prerequisite:''' Download and install NetLogo (https://ccl.northwestern.edu/netlogo/download.shtml) 
'''Interested people:'''
*

=='''Tutorial: R, EDA, a bit of geo-mapping''' ==
'''Speaker:''' Brent Schneeman 
'''Date & Time:''' Tuesday June 23, 7pm (1900) 
'''Motivation and content:''' The "Great Circles" t-shirt design generated some interest in how it was done. I'll walk through the code showing how R can access the Google Maps API and generate great circle arcs. Along the way, we'll look at generating simple descriptive plots of a dataset that will likely resonate with you (because you heard Shalizi talk about kernel density estimates). If we're lucky, we'll be able to translate the arcs and the world map longitudinally. A teensy bit of github will also be shown. I do not claim to be any sort of expert in anything demo'ed, but bring your questions anyway. 
'''Prerequisite:''' Minimal: none, but if you want to type along, install [http://cran.r-project.org/ R] and [http://www.rstudio.com/products/RStudio/#Desk RStudio] (and maybe [http://git-scm.com/ git]). Maximal: you've checked out the [https://github.com/schnee/csss-geo CSSS-geo] code from github, OR you've clicked the little "Download ZIP" button (lower right hand side of the [https://github.com/schnee/csss-geo CSSS-geo] page) and have decompressed into a folder. 
'''Slides:''' [https://docs.google.com/presentation/d/1HzKfmb4ARChrviMavlGkWqxNCD0P8Q_E62f9xktOHjY/edit?usp=sharing Google] 
'''Source Code:''' [https://github.com/schnee/csss-geo CSSS-geo] 
'''Interested:'''
*Christine
*Glenn
*Chris
*Song Binyang
*Jakub
*Alejandro
*Haitao Shang
*Jarrod Scott
* Nilton Cardoso
* Matt Ingram

=='''Python: A Crash Course''' ==
'''Speaker:''' Richard Barnes 
'''Date & Time: June 17 @ 7pm''' 
'''Motivation and content:''' This tutorial assumes some familiarity with programming and covers basic interaction with Python, pros and cons of using it as a language, and a summary of some of its useful packages. If there are particular things you'd like covered, or if you'd like to co-instruct, drop me a line (rbarnes@umn.edu). A few people have expressed interest on following up on this tutorial by teaching workshops on specific packages for networking, machine learning, and scientific computation. 
'''Prerequisite:''' Have [https://www.python.org/downloads/ Python] installed on your computer ([http://continuum.io/downloads Anaconda] is an easy way to get this set up). Please have a code editor installed, [https://www.sublimetext.com/ SublimeText] is an excellent choice. 
'''Interested people:'''
<li> Kiki(no background at all)
<li> Glenn Magerman (no background, only R, Stata)
<li> María Pereda (no background in Python, but I like programming, R, Netlogo, Matlab, C, CUDA)
<li> Laurence (no Python, only R, C++, ..)
<li> Valery (no background in Pyton, only R and Netlogo)
<li> Song Binyang (know a little about Python)
<li> Tolga Oztan (some Python, mostly R)
<li> Jakub (No python, so far Java, Matlab)
<li> Anna(no background in Python, but would love to learn)
<li> Sola...(no background in Python. Proficient in STATA)
<Ii> Jeroen de Wilde (no background)
<li> Alejandro(no background in Python. C and Matlab)
<li> Haitao Shang (no background in Python, only know MatLab)
<li> Jarrod Scott(a little background in Python, a little better with R)
<li> Nilton Cardoso (no background in Python, only R and other stat packages)
<li> Urs (very little background in Python, only Matlab)
<li> Matt Ingram (R, Stata, some Python)
<li> Matt O (R, Mathematica, Matlab)
<li> Jun (only C)
<li> Brent (very very little Python. R, Java, Scala (someday....))
<li> Laura (very little Python mostly R)

=='''Git: A Crash Course''' ==
'''Speaker:''' Richard Barnes 
'''Date & Time:''' Monday the 22nd 
'''Motivation and content:''' This course will cover the basic concepts of Git. It will walk you through creating a repository, committing changes to your code, and collaborating with others. If there are particular things you'd like covered, or if you'd like to co-instruct, drop me a line (rbarnes@umn.edu). 
'''Prerequisite:''' Install [https://www.sourcetreeapp.com/ SourceTree]. Have a code editor, preferably [https://www.sublimetext.com/ SublimeText], installed. 
'''Interested people:'''
<li> Kiki (no background at all)
<li> Glenn Magerman (no background)
<li> Valery (no background)
<li> Jakub (Used it once)
<li> Will (used a little)
<li> Tolga Oztan (used it once)
<li> Anna (no background in this)
<li> Jim Caton (no background)
<li> Alejandro (no background)
<li> Haitao Shang (no background)
<li> Jarrod Scott (some background)
<li> Nilton Cardoso (no backgroung)
<li> Urs (no background)
<li> Matt Ingram (no background)
<li> Laurence (some background)
<li> Laura (no background)
<li> Juan (used a little)

=='''Cloud Computing Introduction''' ==
'''Speaker:''' Christine Harvey (ceharvey@mitre.org) 
'''Date & Time:''' TBD, targeting third or fourth week 
'''Motivation and content:''' This will cover an introduction to cloud computing using Amazon Web Services. This will review setting up an AWS account, launching an instance, logging on to the remote computing resource, and we can try to do a little something else as well. Open to suggestions! 
'''Prerequisite:''' Amazon account and a credit card (compute time should cost < $1) 
'''Interested people:'''
* Glenn Magerman
* Chris
* Valery
* Jakub
* Anna
* Alejandro
* Haitao Shang
* Nilton Cardoso
* Jae
* Matt Ingram
* Laurence

=='''Reproducible Research with iPython Notebooks''' ==
'''Speaker:''' Christine Harvey (ceharvey@mitre.org) 
'''Date & Time:''' TBD, targeting third or fourth week 
'''Motivation and content:''' iPython notebooks are a great way to keep track of your analysis and track data manipulations. Ideal for anyone working with data sets and creating visualizations along the way. More details to follow! Example: http://ipython.org/_static/sloangrant/9_home_fperez_prof_grants_1207-sloan-ipython_proposal_fig_ipython-notebook-specgram.png 
'''Prerequisite:''' Python Install with iPython Notebooks (other packages to be listed). Easiest install is the Anaconda Install (http://continuum.io/downloads) 
'''Interested people:'''
* Glenn Magerman
* Valery
* Song Binyang
* Tolga Oztan
* Anna
* Nilton Cardoso
* Matt Ingram
* Brent
* Jakub
* María Pereda
* Cobain
* Laura
* Ilaria

=='''Topological Data Analysis - Persistent homology''' ==
'''Speaker:''' Alice Patania 
'''Date & Time:''' Sunday, June 21st, 7pm 
'''Content:'''The tutorial will be a short introduction to topological data analysis and its applications to complex systems. I will try to illustrate the utility of these class of methods in several real world examples, and give some computational tools to apply them. 
'''Motivation:''' Topological Data Analysis is sensitive to both large and small scale patterns that often fail to be detected by other analysis methods, such as principal component analysis, (PCA), multidimensional scaling, (MDS), and cluster analysis. PCA and MDS produce unstructured scatterplots and clustering methods produce distinct, unrelated groups. These methodologies sometimes obscure geometric features that topological methods can capture. 
'''Prerequisite:''' 
'''References:''' 
'''Interested people:''' 
1. Nilton 
2. Jean-Gab 
3. Jakub 
4. Richard 
5. María Pereda 
6. Laura 
7. Matt Ingram 
8. Glenn Magerman 
9. Ilaria 
10. Andy 
11. Chris 

=='''NetworkX: Exploring Python's network library and what you can do with it''' ==
'''Speaker:''' Carolina Mattsson and possibly others (carromattsson@gmail.com) 
'''Date & Time:''' Tuesday June 23 - 7:00pm 
'''Motivation and content:''' We'll be having four (!) lectures on networks. Thankfully some of those giants on whose shoulders we stand have built excellent libraries in python, R, and C that make playing around with networks a whole lot easier. In this tutorial we'll be digging into python's network library - NetworkX - and some things it can be used for. Network libraries in other languages have similar functionality, so don't let the python scare you. Having this after the network lectures means we can directly incorporate things that Newman talks about. I'll most likely use iPython notebook as a teaching tool, but if you don't want to install we can work around. There are people other than me who also know NetworkX quite well, if that's you and you want to help, just let me know! 
'''Prerequisites:''' Some Python knowledge - Richard's tutorial would be enough! 
'''Interested co-instructors:''' 
1. Carolina 
2. 
'''Interested people:''' 
1. Jakub 
2. Laura 
3. Matt Ingram 
4. Chris 

=='''Basic examples of dummy variable use in econometrics applied to some Prof. Wooldridge datasets''' ==
''' Speaker:''' Nilton Cardoso 
''' Date & Time:''' Thu, Jun 25, 7:00 PM 
''' Contents:''' Let´s go over some basic examples using Wooldridge´s datasets to illustrate the use of dummy varibales in econometrics. 
''' Motivation:''' Dummy variables in econometric models can capture group/ categories effects and are very useful in estimating different patterns within the sample. 
''' Prerequisite:''' Install R Studio (http://www.rstudio.com/products/rstudio/download/); selected csv data from Wooldridge data sets (http://www.cengage.com/aise/economics/wooldridge_3e_datasets/) 
''' References:''' http://www.amazon.com/Jeffrey-M.-Wooldridge/e/B001IGLWNY 
''' Interested people: ''' 
1. Alice 
2. Sola

=='''Modeling With NetLogo''' ==
'''Speaker:''' Christine Harvey (ceharvey@mitre.org) and Keith Burghardt (keith@umd.edu) 
'''Date & Time:''' 7:00PM, June 16 
'''Motivation:''' NetLogo is a powerful tool for Agent-Based Models (ABM) due to its ease to code, simple-to-create visualization tools, and relatively fast computation capabilities. NetLogo is written in Java, which gives this modeling environment great portability at the expense of speed compared to C or Fortran. 
'''Content:''' In this talk, I will be reviewing NetLogo for students with mild backgrounds in coding by describing the basic program environment, GUI interface, and ways to reduce performance issues through massive parallelization, and avoiding read/write race conditions that can crop up. 
'''To Install NetLogo:''' https://ccl.northwestern.edu/netlogo/download.shtml 
'''Introduction Walkthrough:''' Download the guide for the tutorial here: [[File:NetLogoTutorial.pdf]] 
'''Slides Upload: '''[[File:NetLogo_Review.pdf]] 
'''Interested people:''' 
1. Cobain 
2. Jakub 
3. Nilton 
4. Maggie 
5. Urs 
6. Laurence 
7. Matt H

Resilience of Cities

2015-06-18T22:24:39Z

Rbarnes:

=Resilience of Cities=

[[File:dublin_road_nodes.png]]

==Summary==
Summary: This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities.

Contact: Richard Barnes (rbarnes@umn.edu)

Participants: Alex Ejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang, Brent Schneeman

==Possibly relevant literature==

"REDARS 2 Methodology and Software for Seismic Risk Analysis of Highway Systems"
http://trid.trb.org/view.aspx?id=815535

"osmar - OpenStreetMap and R" http://journal.r-project.org/archive/2013-1/eugster-schlesinger.pdf A practical example of extracting data from the OSM API via R. Talks about "ways" and "nodes". A "way" seems to be very equivalent to our "edge".

OpenStreetMap Wiki: http://wiki.openstreetmap.org/wiki/OSM_XML describes the XML schema (but no XML Schema Document exists). As they say, "Basically it is a list of instances of our data primitives (nodes, ways, and relations)." Oh, and you can get the entire data via a ~30GB binary compress format or an ~500GB text XML format. Certain regional extracts exist. See http://wiki.openstreetmap.org/wiki/Planet.osm.

Osmosis - http://wiki.openstreetmap.org/wiki/Osmosis needed for consuming OSM data via osmar

Python package for accessing OpenStreetMaps data http://wiki.openstreetmap.org/wiki/Osmapi

==Notes==

Also when people (Brent I think?) were talking about locations you'd
go in an emergency, I thought of the CERT volunteers: https://www.fema.gov/community-emergency-response-teams

"Non-stationary biological event" <-- noted for posterity

==Personnel==
Will Chang williamkurtischang at gmail dot com 
Richard Barnes rbarnes at umn dot edu 
matthew histen (linguo42 at gmail dot com) 
penny mealy penny.mealy at gmail dot com 
Brent Schneeman brent at homeaway dot com 
Martina Steffen martinasemail at gmail dot com 
Laurence Brandenberger lenzi.m.b at gmail dot com 
Tirtha Bandy tirtha.bandy at csiro dot au 
Kleber Neves kleberna at gmail dot com 
Alejandro Tejedor alej.tejedor at gmail.com

File:Dublin road nodes.png

2015-06-18T22:24:13Z

Rbarnes:

Complex Systems Summer School 2015-After Hours

2015-06-18T19:13:43Z

Rbarnes:

{{Complex Systems Summer School 2015}}

==Wednesdays==

St. John's College hosts a concert series on the college's athletic field every Wednesday evening from 6 to 8 p.m. These concerts are free and food/sodas are available to purchase.

==Anyday.. everyday??==

"Undefeated" Fuego baseball team plays at 6 everyday at Fort Marcy Field. Games are 6 dollars, beer is available in excess. Schedule is [http://santafefuego.com/santafe.asp?page=11&team=13&year=2015 here] 

We have two dates we will organize for the time being. 

Tuesday, 6/16 

Interested?

1. Matt O 
2. 
3. 
4. 
... 

Saturday, 6/20 

Interested?

1. 
2. 
3. 
4. 
... 

==CSSS Dance June 20==

[[Playlist submissions for Dance]]

==Soccer enthusiasts==

Soccer on thursday -> CANCELLED: lecture!!!
7pm at the field!

Team A (white shirts) 
1. Federico 
2. ... 
3. ... 
4. ... 
5. ... 
Team B (colored shirts) 
1. ... 
2. ... 
3. ... 
4. ... 
5. ... 

==Basketball==

Golden State 
1. Federico 
2. ... 
3. ... 
Cleveland 
1. Matthew "King James" 
2. ... 
3. ... 

FYI ran into some breadloafers who said they're going to play Mondays. We should gather a crew to defeat them.

==Morning Yoga==

We meet at the gym at 7, there is an open space we can use. Bring a towel or a yoga mat, if you have it.
Rotating leading turns, feel free to share your favourite yoga position!

Mon-Thu 1 hour
Fri 45 min (because of the shuttle to SFI)

Interested?

Ilaria 
Carolina (M,W,F) 
Alice 

==Saturdays==

* Contra Dancing, 1st & 3rd Sat. Albuquerque Square Dance Center. [http://folkmads.org/events/albuquerque-events/ Details]
** Interested individuals: Richard Barnes
* Contra Dancing, 2nd & 4th Sat. Santa Fe Odd Fellow's Hall. [http://folkmads.org/events/santa-fe-events/ Details]
** Interested individuals: Richard Barnes

== Amma in Santa Fe – June 20, 2015.==
http://amma.org/meeting-amma/north-america/santa-fe
Free Morning Program 10am–approx. 3pm
8:00am: The token line opens. To ensure everyone has an equal chance of getting an early token, please refrain from forming a line until then.
8:30am: Tokens are handed out and guests are escorted to seats.
10:00am: Amma enters the hall and conducts a short meditation.
10:30am: Amma begins to embrace those who have come.
12:30pm: Lunch is served until thirty minutes after Amma leaves the hall.
About morning programs ›

EVERYONE IS WELCOME
To meet Amma, you will need a token which is issued on a first-come-first-served basis. Tokens are limited, so please arrive early.

Location: Buffalo Thunder Resort
30 Buffalo Thunder Trail 
Santa Fe, NM 87506
United States
Hotel: 877.848.6337

Meeting time: Since we take 45 min to arrive the Resort, we should plan to leave at 7:15 AM.
Interested:
1. Nilton 

==June 20, 10:00am - Bandelier Field Trip==

We're taking a trip to [http://www.nps.gov/band/index.htm Bandelier National Monument] on Saturday June 20th. Please visit the [[Bandelier 2015 | Bandelier Field Trip]] Page to sign up!

==June 25 Rodeo de Santa Fe==

Thursday, June 25!

Come on down for the 66th annual Rodeo de Santa Fe! Watch real-life cowboys get thrown off of various species of raging livestock for their competition and your entertainment. Starts at 7:00pm, we should leave SJC about 6:00. http://rodeodesantafe.org/

Juni's Car 
1. Juni 
2. María Pereda 
3. Sara Lumbreras 
4. Sahil Garg 
5. Federico Battiston 

JP's Ferrari (5 seats) 
1.JP 
2. Jakub Rojcek 
3. Glenn Magerman 
4. Dan Hedblom 
5. Yared Abebe 

JP's Lamborghini (5 seats) 
1. <driver needed> 
2. Junming Huang 
3. Danqing Liu 
4. Martina Steffen 
5. Song Binyang 

Christine's Jeep (5 seats) 
1. Christine 
2. Vanessa 
3. Sola 
4. Laura 
5. Melissa 

Sam's Car (5 seats total) 
1. Sam 
2. Matthew H 
3. Emilia 
4. Tirtha 
5. Alice 
 

Sharon's Car (5 seats) 
1. Sharon 
2. Stefano 
3. Tolga 
4. Laurence 
5. Penny Mealy 
 

Needs a ride 

1. Chao Fan 
2. Kleber Neves 
3. Richard Barnes 
5. Haitao Shang 
6. Jae B. Cho 
7. Alejandro 
8. Susanne 
9. Matt O 
11. Jean-Gab 
12. Andre 
13. Daniel Citron 
14.
15. Nilton Cardoso 
16. Masa 

==Grand Canyon Trip , June 26 - June 28==
'''There is a small group heading to the Grand Canyon the last weekend (leaving Jun26 afternoon, returning Jun28). We will be renting a car, the drive is about 6.5 hours. Anyone interested in joining please contact Juan or Nilton.'''
1.

Complex Systems Summer School 2015-TShirts

2015-06-18T19:13:01Z

Rbarnes: /* Front (with Mandelbrot fractal and Game of Life glider spears */

{{Complex Systems Summer School 2015}}

Every year we have a T-shirt design contest, where CSSS Students create designs (relevant to the summer school) and during the end of the second week we vote on the one we will print and distribute to the cohort. Please post your T-shirt design below. The design will be printed on a single colored T-shirt (keep in mind which color T-shirt you would like to print on while planning your design). The print that will go on the T-shirt is limited to a two color back and a one color front.

If anybody would like a high resolution SFI logo, please see JP.

==Strangely attracted to the future... (DAF)==

Concept piece : Since the ancient Past, our societies/minds have been drawn to sterotyped attractor states (anti-egalitarianism and theoretical violence, respectively). With the help of the momentum generated by our transient at CSSS, we can escape the fallacies of the past - and explore the great beyond of the Future.

[[File:DAF CSSS2015.jpg]]

This image feels like it would do well on the back of the shirt, paired with a simple/tasteful SFI logo on the front.

Or, this image could be on the front, and the back would have a SFI logo, and some text, for example: 

"Strangely attracted to the future..." 

"Isn't life strange..." 

Edits/suggestions/refunds gladly accepted IRL or via email.

==SFI Attractor Concept (Brent)==

Comment: what if you slice the image of the world right down Sri Lanka, and past the right third onto the left side? Then the arcs will align with SFI at the epicenter.

Great Circle Arcs to the SFI Attractor. Arcs terminate at SFI and at participant's institution's address as determined by Google's Maps API.

[[File:Students-front-600x400.png]]
[[File:Students-back-600x400.png]]

Investigating how to move SFI to horizontal center (so as to not break arcs on date line, but I kinda like the fact that something "weird" is going on on the right side of the front image). This [https://docs.google.com/spreadsheets/d/1aSHcKuAmiHrOvq0_NXtFkrqbC7AvgpllReSywQyBq98/edit?usp=sharing data] drives the visual. The "location" column came from the wiki (but may been edited) - that column is fed to the Maps API to get the address and longitude and latitude. If you need to update, change the location column to something that Google will resolve to what you want as your address and I'll re-generate.

==Dragons Concept (Richard)==

===Back===

[[File:dragons2b.png]]

===Front (with Mandelbrot fractal and Game of Life glider spears)===

[[File:richard_dragons_front.png|500x400px]]

===Higher resolution===

[[File:dragons.png]]

==Flocking==

Idea here is to take the aesthetic of flocking models in Netlogo and put it on a shirt. Flock could even flowing around the SFI logo on the front.

Here's a sketch-up. Might need a little bit of refinement.

Logo optional. I sort of like it on the arm myself.

Front(s): 
[[file:Shirt_Front_Arm.png|450px]] 
[[file:Shirt Front 2015.png|450px]] 
Back: 
[[file:Shirt_Back.png|450px]] 

Or maybe white?
[[file:Shirt_Front_2015_white.png‎]]

==Evolution finds a way (The flower on the image is a phlox triovulata. You will find them all over campus!)==

[[File:Nature_white.png|500px]]
[[File:Flower_white.png|500px]]
 
 
[[File:Nature_black.png|500px]]
[[File:Flower_black.png|500px]]
 
 
by Tobias

== Butterfly and chaos (Kleber) ==

So, as you must've heard ... a butterfly flaps its wings in one side of the world and causes a tornado on the other side.

===Back===

[[File:tshirt1.png|700px]]

===Front===

[[File:sfi logo.png|700px]]

== Friday night in Santa Fe ==

[[File:srfs.png|500px]]

== Swipe Right for Science (Harvey, Ariel dorm & Gus) ==

===Front===
[[File:HarveyFront.png]]
===Back===
[[File:HarveyBack.png]]

== JGab ==
[[File:JGab_lowres.png|500px]]

== Symmetry by pencil ==
[[File:Symetry.jpg]]

==Hübler!!!==

[[file:Screen_shot_2015-06-18_at_12.27.11_PM.png‎ ]]

Complex Systems Summer School 2015-TShirts

2015-06-18T19:12:49Z

Rbarnes: /* Front */

{{Complex Systems Summer School 2015}}

Every year we have a T-shirt design contest, where CSSS Students create designs (relevant to the summer school) and during the end of the second week we vote on the one we will print and distribute to the cohort. Please post your T-shirt design below. The design will be printed on a single colored T-shirt (keep in mind which color T-shirt you would like to print on while planning your design). The print that will go on the T-shirt is limited to a two color back and a one color front.

If anybody would like a high resolution SFI logo, please see JP.

==Strangely attracted to the future... (DAF)==

Concept piece : Since the ancient Past, our societies/minds have been drawn to sterotyped attractor states (anti-egalitarianism and theoretical violence, respectively). With the help of the momentum generated by our transient at CSSS, we can escape the fallacies of the past - and explore the great beyond of the Future.

[[File:DAF CSSS2015.jpg]]

This image feels like it would do well on the back of the shirt, paired with a simple/tasteful SFI logo on the front.

Or, this image could be on the front, and the back would have a SFI logo, and some text, for example: 

"Strangely attracted to the future..." 

"Isn't life strange..." 

Edits/suggestions/refunds gladly accepted IRL or via email.

==SFI Attractor Concept (Brent)==

Comment: what if you slice the image of the world right down Sri Lanka, and past the right third onto the left side? Then the arcs will align with SFI at the epicenter.

Great Circle Arcs to the SFI Attractor. Arcs terminate at SFI and at participant's institution's address as determined by Google's Maps API.

[[File:Students-front-600x400.png]]
[[File:Students-back-600x400.png]]

Investigating how to move SFI to horizontal center (so as to not break arcs on date line, but I kinda like the fact that something "weird" is going on on the right side of the front image). This [https://docs.google.com/spreadsheets/d/1aSHcKuAmiHrOvq0_NXtFkrqbC7AvgpllReSywQyBq98/edit?usp=sharing data] drives the visual. The "location" column came from the wiki (but may been edited) - that column is fed to the Maps API to get the address and longitude and latitude. If you need to update, change the location column to something that Google will resolve to what you want as your address and I'll re-generate.

==Dragons Concept (Richard)==

===Back===

[[File:dragons2b.png]]

===Front (with Mandelbrot fractal and Game of Life glider spears===

[[File:richard_dragons_front.png|500x400px]]

===Higher resolution===

[[File:dragons.png]]

==Flocking==

Idea here is to take the aesthetic of flocking models in Netlogo and put it on a shirt. Flock could even flowing around the SFI logo on the front.

Here's a sketch-up. Might need a little bit of refinement.

Logo optional. I sort of like it on the arm myself.

Front(s): 
[[file:Shirt_Front_Arm.png|450px]] 
[[file:Shirt Front 2015.png|450px]] 
Back: 
[[file:Shirt_Back.png|450px]] 

Or maybe white?
[[file:Shirt_Front_2015_white.png‎]]

==Evolution finds a way (The flower on the image is a phlox triovulata. You will find them all over campus!)==

[[File:Nature_white.png|500px]]
[[File:Flower_white.png|500px]]
 
 
[[File:Nature_black.png|500px]]
[[File:Flower_black.png|500px]]
 
 
by Tobias

== Butterfly and chaos (Kleber) ==

So, as you must've heard ... a butterfly flaps its wings in one side of the world and causes a tornado on the other side.

===Back===

[[File:tshirt1.png|700px]]

===Front===

[[File:sfi logo.png|700px]]

== Friday night in Santa Fe ==

[[File:srfs.png|500px]]

== Swipe Right for Science (Harvey, Ariel dorm & Gus) ==

===Front===
[[File:HarveyFront.png]]
===Back===
[[File:HarveyBack.png]]

== JGab ==
[[File:JGab_lowres.png|500px]]

== Symmetry by pencil ==
[[File:Symetry.jpg]]

==Hübler!!!==

[[file:Screen_shot_2015-06-18_at_12.27.11_PM.png‎ ]]

Complex Systems Summer School 2015-TShirts

2015-06-18T19:12:20Z

Rbarnes: /* Front */

{{Complex Systems Summer School 2015}}

Every year we have a T-shirt design contest, where CSSS Students create designs (relevant to the summer school) and during the end of the second week we vote on the one we will print and distribute to the cohort. Please post your T-shirt design below. The design will be printed on a single colored T-shirt (keep in mind which color T-shirt you would like to print on while planning your design). The print that will go on the T-shirt is limited to a two color back and a one color front.

If anybody would like a high resolution SFI logo, please see JP.

==Strangely attracted to the future... (DAF)==

Concept piece : Since the ancient Past, our societies/minds have been drawn to sterotyped attractor states (anti-egalitarianism and theoretical violence, respectively). With the help of the momentum generated by our transient at CSSS, we can escape the fallacies of the past - and explore the great beyond of the Future.

[[File:DAF CSSS2015.jpg]]

This image feels like it would do well on the back of the shirt, paired with a simple/tasteful SFI logo on the front.

Or, this image could be on the front, and the back would have a SFI logo, and some text, for example: 

"Strangely attracted to the future..." 

"Isn't life strange..." 

Edits/suggestions/refunds gladly accepted IRL or via email.

==SFI Attractor Concept (Brent)==

Comment: what if you slice the image of the world right down Sri Lanka, and past the right third onto the left side? Then the arcs will align with SFI at the epicenter.

Great Circle Arcs to the SFI Attractor. Arcs terminate at SFI and at participant's institution's address as determined by Google's Maps API.

[[File:Students-front-600x400.png]]
[[File:Students-back-600x400.png]]

Investigating how to move SFI to horizontal center (so as to not break arcs on date line, but I kinda like the fact that something "weird" is going on on the right side of the front image). This [https://docs.google.com/spreadsheets/d/1aSHcKuAmiHrOvq0_NXtFkrqbC7AvgpllReSywQyBq98/edit?usp=sharing data] drives the visual. The "location" column came from the wiki (but may been edited) - that column is fed to the Maps API to get the address and longitude and latitude. If you need to update, change the location column to something that Google will resolve to what you want as your address and I'll re-generate.

==Dragons Concept (Richard)==

===Back===

[[File:dragons2b.png]]

===Front===

[[File:richard_dragons_front.png|500x400px]]

===Higher resolution===

[[File:dragons.png]]

==Flocking==

Idea here is to take the aesthetic of flocking models in Netlogo and put it on a shirt. Flock could even flowing around the SFI logo on the front.

Here's a sketch-up. Might need a little bit of refinement.

Logo optional. I sort of like it on the arm myself.

Front(s): 
[[file:Shirt_Front_Arm.png|450px]] 
[[file:Shirt Front 2015.png|450px]] 
Back: 
[[file:Shirt_Back.png|450px]] 

Or maybe white?
[[file:Shirt_Front_2015_white.png‎]]

==Evolution finds a way (The flower on the image is a phlox triovulata. You will find them all over campus!)==

[[File:Nature_white.png|500px]]
[[File:Flower_white.png|500px]]
 
 
[[File:Nature_black.png|500px]]
[[File:Flower_black.png|500px]]
 
 
by Tobias

== Butterfly and chaos (Kleber) ==

So, as you must've heard ... a butterfly flaps its wings in one side of the world and causes a tornado on the other side.

===Back===

[[File:tshirt1.png|700px]]

===Front===

[[File:sfi logo.png|700px]]

== Friday night in Santa Fe ==

[[File:srfs.png|500px]]

== Swipe Right for Science (Harvey, Ariel dorm & Gus) ==

===Front===
[[File:HarveyFront.png]]
===Back===
[[File:HarveyBack.png]]

== JGab ==
[[File:JGab_lowres.png|500px]]

== Symmetry by pencil ==
[[File:Symetry.jpg]]

==Hübler!!!==

[[file:Screen_shot_2015-06-18_at_12.27.11_PM.png‎ ]]

Complex Systems Summer School 2015-TShirts

2015-06-16T04:43:12Z

Rbarnes: /* Dragons Concept (Richard) */

Complex Systems Summer School 2015-TShirts

2015-06-16T04:41:23Z

Rbarnes: /* Dragons Concept (Richard) */

Complex Systems Summer School 2015-TShirts

2015-06-16T04:41:04Z

Rbarnes: /* Dragons Concept (Richard) */

File:Dragons2b.png

2015-06-16T04:40:08Z

Rbarnes:

Complex Systems Summer School 2015-TShirts

2015-06-16T04:30:53Z

Rbarnes: /* Dragons Concept (Richard) */

File:Richard dragons front.png

2015-06-16T04:30:04Z

Rbarnes:

Background Readings CSSS15

2015-06-15T23:43:00Z

Rbarnes:

{{Complex Systems Summer School 2015}}

The following is a collection of papers regarded as "classic" literature in Complex Systems Science. This list has been growing over the past few years and was formalized by Dan Rockmore for the 2010 Complex Systems Summer School.

For more readings, please explore the wiki content of previous summer schools.
 
 
[http://philoscience.unibe.ch/documents/uk/weaver1948.pdf Science and complexity by Warren Weaver]

[http://www.jstor.org/stable/184253 Rosenblueth, A., and N. Wiener. 1945. The Role of Models in Science. Philosophy of Science 12 (4):316-321.]

[http://cm.bell-labs.com/cm/ms/what/shannonday/shannon1948.pdf Shannon, C.E. 1948. A Mathematical Theory of Communication. Bell System Technical Journal 27:379-423 623-656.]

[http://www.google.com/url?sa=t&source=web&ct=res&cd=1&ved=0CBsQFjAA&url=http%3A%2F%2Fwww.dna.caltech.edu%2Fcourses%2Fcs191%2Fpaperscs191%2Fturing.pdf&ei=-iEFTOabHI7eNYSZkDw&usg=AFQjCNGopAGCXkZ7OBVBFLY0rIh-9O6Fhg&sig2=oO6O7ehULch49r03agAWlA Turing, A.M. 1952. The Chemical Basis of Morphogenesis. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 237 (641):37-72.]

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.79.7413&rep=rep1&type=pdf Minksy, M. 1961. Steps Toward Artificial Intelligence. Proceedings of the Institute of Radio Engineers 49 (1):8-30.]

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.68.7646&rep=rep1&type=pdf Landauer, R. 1961. Irreversibility and Heat Generation in the Computing Process. IBM Journal of Research and Development 5:183-191.]

[http://www.jstor.org/stable/2295952 Arrow, K.J. 1962. The Economic Implications of Learning by Doing. Review of Economic Studies 80:155-173.]

[http://www.jstor.org/stable/1301992 Raup, D.M. 1966. Geometric Analysis of Shell Coiling; General Problems. Journal of Paleontology 40 (5):1178-1190.]

[http://portal.acm.org/citation.cfm?id=1045373 Holland, J.H., and J.S. Reitman. 1977. Cognitive Systems Based on Adaptive Algorithms. SIGART Newsletter (63):49.]

[http://www.google.com/url?sa=t&source=web&ct=res&cd=3&ved=0CB8QFjAC&url=http%3A%2F%2Fchaos.swarthmore.edu%2Fcourses%2FSOC26%2FBak-Sneppan%2F07_Gould.pdf&ei=6CUFTJb8LqPCMu2MpDs&usg=AFQjCNH0Ua8JG_7QtipRRBvLt5V8E7kJyQ&sig2=457Aj9mKdg5-b-z4NrbHCw Gould, S.J., and N. Eldredge. 1977. Punctuated Equilibria: the Tempo and Mode of Evolution Reconsidered. Paleobiology 3 (2):115-151.]

[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TVK-4CVPV04-C&_user=1695018&_coverDate=11%2F30%2F1986&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_rerunOrigin=scholar.google&_acct=C000054245&_version=1&_urlVersion=0&_userid=1695018&md5=42419038a0560af45057c065a719b326 Langton, C.G. 1986. Studying Artificial Life with Cellular Automata. Physica D: Nonlinear Phenomena 22 (1-3):120-149.]

[http://arxiv.org/abs/cond-mat/0412004 M. E. J. Newman. 2005. "Power laws, Pareto distributions and Zipf's law." Contemporary Physics 46, 323-351.]

[http://arxiv.org/abs/0706.1062 Aaron Clauset, Cosma Rohilla Shalizi, M. E. J. Newman. 2009. "Power-law distributions in empirical data." SIAM Review 51, 661-703.]

Last Updated for SFI CSSS13 March 25 2013

=Bibles (Awesome Reference Books)=

==Statistics==

* All of Statistics (Wasserman). Recommended by '''Cosma Shalizi'''.
* All of Nonparametric Statistics (Wasserman). Recommended by '''Cosma Shalizi'''.

==Computer Science==

* Nature of Computation (Moore). Recommended by '''Christopher Moore'''.
* The Algorithm Design Manual (Skiena). Recommended by Richard Barnes.

==Data Analysis==

* Nonlinear Time Series Analysis (Kantz & Schreiber). Recommended by '''Liz Bradley'''.

Complex Systems Summer School 2015-Tutorials

2015-06-15T22:51:30Z

Rbarnes: /* Python: A Crash Course */

Complex Systems Summer School 2015-Tutorials

2015-06-15T22:51:18Z

Rbarnes: /* Python: A Crash Course */

Complex Systems Summer School 2015-Tutorials

2015-06-15T22:50:25Z

Rbarnes: /* Topological Data Analysis - Persistent homology */

Physics Lab 2015

2015-06-15T19:26:12Z

Rbarnes:

{{Complex Systems Summer School 2015}}

==Tuesday June 9, 7:00 - 9:00 PM==

1. MV Eitzel 
2. Daniel Citron 
3. Tobias Morville 
4. Urs Braun 
5. Ilaria Bertazzi 
6. Jakub Rojcek 
7. Nilton Cardaso 
8. Christine Harvey 
9. Michael Smallegan 
10. Jelle Bruineberg 
11. Susanne Petterson 
12. Jun Takahashi 
13. Andrew Schauf 
14. Maggie Simon 
15. Matt Ingram 

==Thursday June 11, 7:00 - 9:00 PM==

1. Glenn Magerman 
2. Carolina Mattson 
3. Valerie Dzutsev 
4. Sara Lumbreras 
5. Richard Barnes 
6. Laurence Brandenberger 
7. 'Sola Omoju 
8. Andre Veski 
9. John Thomas 
10. Vanessa Chioffi 
11. Chris Verzijl 
12. Daniel Friedman 
13. Jeroen de Wilde 
14. James Caton 
15. Masa Haraguchi 

==Monday June 15, 7:00 - 9:00 PM==

1. Sahil Garg 
2. Junming Huang 
3. John Thomas 
4. Chao Fan 
5. Juan Carlos Castilla 
6. Alejandro Tejedor 
7. Brent Schneeman 
8. Emilia Wysocka 
9. William Chang 
10. Martina Steffen 
11. Sam Way 
12. Kleber Neves 
13. Jae B Cho 
14. Matthew Histen 
15. Haitao Shang 
16. Tirthankar Bandyopadhyay 

==Tuesday June 16, 1:00 PM - 3:00 PM==

1. Federico Battiston 
2. Alice Patania 
3. Sebastian Poledna 
4. Keith Burghardt 
5. María Pereda 
6. Matthew Cobain 
7. Daniel Hedblom 
8. Jean-Gabriel Young 
9. Matthew Osmond 
10. Penny Mealy 
11. Marie-Pierre Hasne 
12. Sharon Greenblum 
13. Laura Condon 
14. 
15. Jarrod Scott 
16. Anna Zaytseva

Complex Systems Summer School 2015-Sign Up Sheet for Meetings with Josh

2015-06-15T18:48:25Z

Rbarnes:

{{Complex Systems Summer School 2015}}

{| border="1" cellpadding="2"
! scope="col"width="100" align="center | Time
! scope="col"width="700" align="center | Activity

|- bgcolor="#aaaaaa" align="center"
| 
| Monday, June 15 
|-
|2:30 p.m. - 2:40 p.m.
|California drought project
|-
|2:45 p.m. - 2:55 p.m.
| Zimbabwe group
|-
|3:00 p.m. - 3:10 p.m.
|Christine Harvey - Organ Transplant Analysis
|-
|3:15 p.m. - 3:25 p.m.
|
|-
|3:30 p.m. - 3:40 p.m.
|
|-
|3:45 p.m. - 3:55 p.m.
|cities
|-
|4:00 p.m. - 4:10 p.m.
|Break
|-
|4:15 p.m. - 4:25 p.m.
|
|-
|4:15 p.m. - 4:25 p.m.
|
|-
|4:30 p.m. - 4:40 p.m.
|
|-
|4:45 p.m. - 4:55 p.m.
|
|-

|- bgcolor="#aaaaaa" align="center"
| 
| Tuesday, June 16 

|-
|2:45 p.m. - 2:55 p.m.
|Arxiv
|-
|3:00 p.m. - 3:10 p.m.
|
|-
|3:15 p.m. - 3:25 p.m.
|
|-
|3:30 p.m. - 3:40 p.m.
|
|-
|3:45 p.m. - 3:55 p.m.
|
|-
|4:00 p.m. - 4:10 p.m.
|Break
|-
|4:15 p.m. - 4:25 p.m.
|Genomic Variation using Chaotic Mapping
|-
|4:15 p.m. - 4:25 p.m.
|
|-
|4:30 p.m. - 4:40 p.m.
|Music and the Brain.
|-
|4:45 p.m. - 4:55 p.m.
|Mapping Complexity/Human Knowledge as a Complex Adaptive System
|-

Complex Systems Summer School 2015-Sign Up Sheet for Meetings with Josh

2015-06-15T18:46:53Z

Rbarnes:

{{Complex Systems Summer School 2015}}

{| border="1" cellpadding="2"
! scope="col"width="100" align="center | Time
! scope="col"width="700" align="center | Activity

|- bgcolor="#aaaaaa" align="center"
| 
| Monday, June 15 
|-
|2:30 p.m. - 2:40 p.m.
|California drought project
|-
|2:45 p.m. - 2:55 p.m.
| Zimbabwe group
|-
|3:00 p.m. - 3:10 p.m.
|Christine Harvey - Organ Transplant Analysis
|-
|3:15 p.m. - 3:25 p.m.
|
|-
|3:30 p.m. - 3:40 p.m.
|
|-
|3:45 p.m. - 3:55 p.m.
|cities
|-
|4:00 p.m. - 4:10 p.m.
|Break
|-
|4:15 p.m. - 4:25 p.m.
|
|-
|4:15 p.m. - 4:25 p.m.
|
|-
|4:30 p.m. - 4:40 p.m.
|
|-
|4:45 p.m. - 4:55 p.m.
|
|-

|- bgcolor="#aaaaaa" align="center"
| 
| Tuesday, June 16 

|-
|2:45 p.m. - 2:55 p.m.
|
|-
|3:00 p.m. - 3:10 p.m.
|
|-
|3:15 p.m. - 3:25 p.m.
|
|-
|3:30 p.m. - 3:40 p.m.
|
|-
|3:45 p.m. - 3:55 p.m.
|
|-
|4:00 p.m. - 4:10 p.m.
|Break
|-
|4:15 p.m. - 4:25 p.m.
|Genomic Variation using Chaotic Mapping
|-
|4:15 p.m. - 4:25 p.m.
|
|-
|4:30 p.m. - 4:40 p.m.
|Music and the Brain.
|-
|4:45 p.m. - 4:55 p.m.
|Mapping Complexity/Human Knowledge as a Complex Adaptive System
|-

Complex Systems Summer School 2015-After Hours

2015-06-12T16:58:47Z

Rbarnes: /* June 13, 10:00 am - Rio Grande Gorge Bridge */

{{Complex Systems Summer School 2015}}

==Wednesdays==

St. John's College hosts a concert series on the college's athletic field every Wednesday evening from 6 to 8 p.m. These concerts are free and food/sodas are available to purchase.

==MAKE Santa Fe Pop Up Event==

When: Saturday, June 13 9:00 a.m. - 12:00 p.m.
Where: Warehouse 21 (1614 Paseo De Peralta)

Pop-event that includes demonstrations of 3D printing, laser cutting, and CNC routing. Learn about and explore these super cool tools! Part of the Currents New Media Festival.

Interested?

1. 
2. 
3. 
4. 
... 

==Todd and the Fox at the Cowgirl (concert)==

Come watch Juniper's brother play with his band, Todd and the Fox, at the Cowgirl BBQ this Saturday, June 13. The show starts at 8:30 p.m. and is free to attend. Great music and great fun.

Interested?

1. Christine 
2. 
3. 
4. 
... 

==Grand Canyon Trip==
There is a small group heading to the Grand Canyon this weekend (leaving Jun12 afternoon, returning Jun14). We will be renting a car, the drive is about 6.5 hours. Anyone interested in joining please contact Juan or Nilton.

==Anyday.. everyday??==

"Undefeated" Fuego baseball team plays at 6 everyday at Fort Marcy Field. Games are 6 dollars, beer is available in excess. Schedule is [http://santafefuego.com/santafe.asp?page=11&team=13&year=2015 here] 

We have two dates we will organize for the time being. 

Tuesday, 6/16 

Interested?

1. Matt O 
2. 
3. 
4. 
... 

Saturday, 6/20 

Interested?

1. 
2. 
3. 
4. 
... 

==Soccer enthusiasts==

Nice game guys! When is the next one (Monday or Tuesday)?

1. Sola 
2. Federico 
3. Urs 
4. Sharon 
5. Matt Ingram 
6. Christine Harvey 
7. Yared Abebe 
8. Matt O 
9. Connor 
10. Carolina (this thursday I have lab, though. Next time!) 
11. Jakub (next Thursday) 
...

==Morning Yoga==

We meet at the gym at 7, there is an open space we can use. Bring a towel or a yoga mat, if you have it.
Rotating leading turns, feel free to share your favourite yoga position!

Mon-Thu 1 hour
Fri 45 min (because of the shuttle to SFI)

Interested?

Ilaria 
Carolina would join you! In the morning and ourside would be awesome, but i don't have a mat either. I'd use a towel or stay on the grass :)

==Saturdays==

* Contra Dancing, 1st & 3rd Sat. Albuquerque Square Dance Center. [http://folkmads.org/events/albuquerque-events/ Details]
** Interested individuals: Richard Barnes
* Contra Dancing, 2nd & 4th Sat. Santa Fe Odd Fellow's Hall. [http://folkmads.org/events/santa-fe-events/ Details]
** Interested individuals: Richard Barnes

==June 12 Currents New Media Festival==

When: Friday June 12 8pm 
Where: Meet at 2nd Street Railyard 1607 Paseo De Peralta #10, Santa Fe, NM 87501 

Super cool interactive new media festival on the Santa Fe Railyard. Let's meet at 2nd Street Brewery around 8pm for drinks and then walk through the festival. Here's the info http://currentsnewmedia.org/ 

-Juniper 

Whoo! Let's Go! -JP

JP's VW 
1.JP 
2. Glenn Magerman 
3. Laurence 
4. Matthew 
5. Chris 

Interested Folks: 
Connor 
Matt O 
Emilia 
Masa 
Sharon 

==June 13, 10:00 am - Rio Grande Gorge Bridge==
Who wants to see the [http://en.wikipedia.org/wiki/Rio_Grande_Gorge_Bridge Rio Grande Gorge Bridge] and nearby attractions? About a 90 minute drive out of Santa Fe; we can jet over to Taos before or after and perhaps checkout the Pueblo. Or we could do a big loop and check out [http://en.wikipedia.org/wiki/Ghost_Ranch Ghost Ranch]

Interested?

Brent's Car (5 seats total) 
1.Brent 
2.Stefano 
3.Keith 
4.Haitao 
5.Jae B. Cho 

Christine's Car (5 seats total) 
1.Christine 
2.Vanessa 
3. Urs 
4.Laurence 
5.Richard 

JP's Car! 
1.JP 
2.Jean-Gab 
3.Alice 
4. Melissa 
5. Song Binyang 

Valery's Car (7 seats total) 
1.Valery 
2. Alejandro 
3. Matt Histen 
4. Chris 
5. Ilaria 
6. Daniel F. 
7. Matt O 

Sharon's Car (5 seats total) 
1. Sharon 
2. Kleber 
3. Federico 
4. Yared 
5. Daniel Citron 
 

Carolina's Car (5 seats total) 
I might want to check out the construction going on at the ski area, if people in my car are ok with it. Can't help myself. :P
There's a really nice couple mile hike up from there too, to to Williams lake. Just a thought! 
1. Carolina 
2. Andre 
3. Jakub 
4. Sara 
5. María 

Sam's Car (5 seats total) 
1. Sam 
2. Emilia 
3. Junming 
4. Danqing 
5. Chao 
 

'''Richard's Stick Shift Rental (5 seats total)''' 
'''I need someone who can stick well to drive this. I can't reserve the car until I find this person.''' 
Also: it'd be cool to hike a bit. 
1. Richard 
2. '''Excellent stick shift driver name here''' 
3. Penny 
4. 
5. 

Needs a ride: 
Tobias 
Sola 
Tirtha 
Masa 
Will 
Nilton 

==June 13: Contra dancing in Santa Fe==

7:00 pm – Lesson, 7:30-10:30 – Dancing
Member $8, Non-members $9, Students half price, Under 12 free

Interested?

1. Richard 
2. Matt O 
3. 
4. 
... 

==June 14 (Sunday evening): Jurassic World Cinema excursion==

7:00 pm - Regal Cinemas Santa Fe 14
it's in RealD 3D, tickets are 16$

Interested?

Sam's Car (5 seats total) 
1. Sam 
2. Matthew H 
3. Masa 
4. 
5. 
 

1. Laurence Brandenberger 
2. Richard 
3. 
4. Chris 
5. Brent 
6. Alejandro 
7. Jae 
8. Tobias 
9. Jean-Gab 
10. Stefano 
11. 
12. Penny 
13. Tirtha 

==June 20, 10:00am - Bandelier Field Trip==

We're taking a trip to [http://www.nps.gov/band/index.htm Bandelier National Monument] on Saturday June 20th. Please visit the [[Bandelier 2015 | Bandelier Field Trip]] Page to sign up!

==June 25 Rodeo de Santa Fe==

Thursday, June 25!

Come on down for the 66th annual Rodeo de Santa Fe! Watch real-life cowboys get thrown off of various species of raging livestock for their competition and your entertainment. Starts at 7:00pm, we should leave SJC about 6:00. http://rodeodesantafe.org/

Juni's Car 
1. Juni 
2. María Pereda 
3. Sara Lumbreras 
4. Sahil Garg 
5. Federico Battiston 

JP's Ferrari (5 seats) 
1.JP 
2. Jakub Rojcek 
3. Glenn Magerman 
4. Dan Hedblom 
5. Yared Abebe 

JP's Lamborghini (5 seats) 
1. <driver needed> 
2. Junming Huang 
3. Danqing Liu 
4. Martina Steffen 
5. Song Binyang 

Christine's Jeep (5 seats) 
1. Christine 
2. Vanessa 
3. Sola 
4. Laura 
5. Melissa 

Sam's Car (5 seats total) 
1. Sam 
2. Matthew H 
3. Emilia 
4. 
5. 
 

Needs a ride 

1. Chao Fan 
2. Kleber Neves 
3. Richard Barnes 
4. Laurence Brandenberger 
5. Haitao Shang 
6. Jae B. Cho 
7. Alejandro 
8. 
9. Matt O 
10. Alice 
11. Jean-Gab 
12. Andre 
13. Daniel Citron 
14. Penny Mealy 
15. Nilton Cardoso 
16. Tirtha

Complex Systems Summer School 2015-Tutorials

2015-06-12T00:54:03Z

Rbarnes:

Complex Systems Summer School 2015-Tutorials

2015-06-12T00:53:48Z

Rbarnes: /* Python: A Crash Course */

Physics Lab 2015

2015-06-11T15:30:05Z

Rbarnes: /* Thursday June 11, 7:00 - 9:00 PM */

{{Complex Systems Summer School 2015}}

==Tuesday June 9, 7:00 - 9:00 PM==

1. MV Eitzel 
2. Daniel Citron 
3. Tobias Morville 
4. Urs Braun 
5. Ilaria Bertazzi 
6. Jakub Rojcek 
7. Nilton Cardaso 
8. Christine Harvey 
9. Michael Smallegan 
10. Jelle Bruineberg 
11. Susanne Petterson 
12. Jun Takahashi 
13. Andrew Schauf 
14. Maggie Simon 
15. Matt Ingram 

==Thursday June 11, 9:00 AM - 11:00 AM==

1. Federico Battiston 
2. Alice Patania 
3. Sebastian Poledna 
4. Keith Burghardt 
5. María Pereda 
6. Matthew Cobain 
7. Daniel Hedblom 
8. Jean-Gabriel Young 
9. Matthew Osmond 
10. Penny Mealy 
11. Marie-Pierre Hasne 
12. Sharon Greenblum 
13. Laura Condon 
14. Richard Barnes 
15. Jarrod Scott 

==Thursday June 11, 7:00 - 9:00 PM==

1. Glenn Magerman 
2. Carolina Mattson 
3. Anna Zaytseva 
4. Tirthankar Bandyopadhyay 
5. Sara Lumbreras 
6. Richard Barnes 
7. Laurence Brandenberger 
8. 'Sola Omoju 
9. Andre Veski 
10. 
11. Vanessa Chioffi 
12. 
13. 
14. Jeroen de Wilde 
15. 

==Monday June 15, 7:00 - 9:00 PM==

1. Sahil Garg 
2. Junming Huang 
3. John Thomas 
4. Chao Fan 
5. Juan Carlos Castilla 
6. Alejandro Tejedor 
7. Brent Schneeman 
8. Emilia Wysocka 
9. William Chang 
10. Martina Steffen 
11. Sam Way 
12. Kleber Neves 
13. 
14. 
15.

Physics Lab 2015

2015-06-11T15:26:25Z

Rbarnes: /* Thursday June 11, 7:00 - 9:00 PM */

{{Complex Systems Summer School 2015}}

==Tuesday June 9, 7:00 - 9:00 PM==

1. MV Eitzel 
2. Daniel Citron 
3. Tobias Morville 
4. Urs Braun 
5. Ilaria Bertazzi 
6. Jakub Rojcek 
7. Nilton Cardaso 
8. Christine Harvey 
9. Michael Smallegan 
10. Jelle Bruineberg 
11. Susanne Petterson 
12. Jun Takahashi 
13. Andrew Schauf 
14. Maggie Simon 
15. Matt Ingram 

==Thursday June 11, 9:00 AM - 11:00 AM==

1. Federico Battiston 
2. Alice Patania 
3. Sebastian Poledna 
4. Keith Burghardt 
5. María Pereda 
6. Matthew Cobain 
7. Daniel Hedblom 
8. Jean-Gabriel Young 
9. Matthew Osmond 
10. Penny Mealy 
11. Marie-Pierre Hasne 
12. Sharon Greenblum 
13. Laura Condon 
14. Richard Barnes 
15. Jarrod Scott 

==Thursday June 11, 7:00 - 9:00 PM==

1. Glenn Magerman 
2. Carolina Mattson 
3. Anna Zaytseva 
4. Tirthankar Bandyopadhyay 
5. Sara Lumbreras 
6. 
7. Laurence Brandenberger 
8. 'Sola Omoju 
9. Andre Veski 
10. 
11. Vanessa Chioffi 
12. 
13. 
14. Jeroen de Wilde 
15. 

==Monday June 15, 7:00 - 9:00 PM==

1. Sahil Garg 
2. Junming Huang 
3. John Thomas 
4. Chao Fan 
5. Juan Carlos Castilla 
6. Alejandro Tejedor 
7. Brent Schneeman 
8. Emilia Wysocka 
9. William Chang 
10. Martina Steffen 
11. Sam Way 
12. Kleber Neves 
13. 
14. 
15.

Complex Systems Summer School 2015-Project Group Meeting Schedule

2015-06-11T15:13:50Z

Rbarnes:

{{Complex Systems Summer School 2015}}

'''Please use this space to list the times and locations of project group meetings, in the following format:''' 
'''12:30pm Name of Project (meeting place)''' 
 
 

==Thursday, June 11==
1615 - Cities in the Coffee Shop

Complex Systems Summer School 2015-Project Group Meeting Schedule

2015-06-11T15:12:45Z

Rbarnes:

{{Complex Systems Summer School 2015}}

'''Please use this space to list the times and locations of project group meetings, in the following format:''' 
'''12:30pm Name of Project (meeting place)''' 
 

==Thursday, June 11==
1615 - Cities in the Coffee Shop

Physics Lab 2015

2015-06-11T14:52:20Z

Rbarnes: /* Thursday June 11, 7:00 - 9:00 PM */

{{Complex Systems Summer School 2015}}

==Tuesday June 9, 7:00 - 9:00 PM==

1. MV Eitzel 
2. Daniel Citron 
3. Tobias Morville 
4. Urs Braun 
5. Ilaria Bertazzi 
6. Jakub Rojcek 
7. Nilton Cardaso 
8. Christine Harvey 
9. Michael Smallegan 
10. Jelle Bruineberg 
11. Susanne Petterson 
12. Jun Takahashi 
13. Andrew Schauf 
14. Maggie Simon 
15. Matt Ingram 

==Thursday June 11, 9:00 AM - 11:00 AM==

1. Federico Battiston 
2. Alice Patania 
3. Sebastian Poledna 
4. Keith Burghardt 
5. María Pereda 
6. Matthew Cobain 
7. Daniel Hedblom 
8. Jean-Gabriel Young 
9. Matthew Osmond 
10. Penny Mealy 
11. Marie-Pierre Hasne 
12. Sharon Greenblum 
13. Laura Condon 
14. Richard Barnes 
15. Jarrod Scott 

==Thursday June 11, 7:00 - 9:00 PM==

1. Glenn Magerman 
2. Carolina Mattson 
3. Anna Zaytseva 
4. Tirthankar Bandyopadhyay 
5. Sara Lumbreras 
6. 
7. Laurence Brandenberger 
8. 'Sola Omoju 
9. Andre Veski 
10. Kleber Neves 
11. Vanessa Chioffi 
12. Richard Barnes 
13. 
14. Jeroen de Wilde 
15. 

==Monday June 15, 7:00 - 9:00 PM==

1. Sahil Garg 
2. Junming Huang 
3. John Thomas 
4. Chao Fan 
5. Juan Carlos Castilla 
6. Alejandro Tejedor 
7. Brent Schneeman 
8. Emilia Wysocka 
9. William Chang 
10. Martina Steffen 
11. Sam Way 
12. 
13. 
14. 
15.

Physics Lab 2015

2015-06-11T14:51:39Z

Rbarnes: /* Thursday June 11, 9:00 AM - 11:00 AM */

{{Complex Systems Summer School 2015}}

==Tuesday June 9, 7:00 - 9:00 PM==

1. MV Eitzel 
2. Daniel Citron 
3. Tobias Morville 
4. Urs Braun 
5. Ilaria Bertazzi 
6. Jakub Rojcek 
7. Nilton Cardaso 
8. Christine Harvey 
9. Michael Smallegan 
10. Jelle Bruineberg 
11. Susanne Petterson 
12. Jun Takahashi 
13. Andrew Schauf 
14. Maggie Simon 
15. Matt Ingram 

==Thursday June 11, 9:00 AM - 11:00 AM==

1. Federico Battiston 
2. Alice Patania 
3. Sebastian Poledna 
4. Keith Burghardt 
5. María Pereda 
6. Matthew Cobain 
7. Daniel Hedblom 
8. Jean-Gabriel Young 
9. Matthew Osmond 
10. Penny Mealy 
11. Marie-Pierre Hasne 
12. Sharon Greenblum 
13. Laura Condon 
14. Richard Barnes 
15. Jarrod Scott 

==Thursday June 11, 7:00 - 9:00 PM==

1. Glenn Magerman 
2. 
3. Anna Zaytseva 
4. Tirthankar Bandyopadhyay 
5. Sara Lumbreras 
6. 
7. Laurence Brandenberger 
8. 'Sola Omoju 
9. Andre Veski 
10. Kleber Neves 
11. Vanessa Chioffi 
12. Richard Barnes 
13. 
14. Jeroen de Wilde 
15. 

==Monday June 15, 7:00 - 9:00 PM==

1. Sahil Garg 
2. Junming Huang 
3. John Thomas 
4. Chao Fan 
5. Juan Carlos Castilla 
6. Alejandro Tejedor 
7. Brent Schneeman 
8. Emilia Wysocka 
9. William Chang 
10. Martina Steffen 
11. Sam Way 
12. 
13. 
14. 
15.

Resilience of Cities

2015-06-11T03:34:12Z

Rbarnes: Created page

=Resilience of Cities=

==Summary==
Summary: This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities.

Contact: Richard Barnes (rbarnes@umn.edu)

Participants: Alex Ejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang

==Possibly relevant literature==

"REDARS 2 Methodology and Software for Seismic Risk Analysis of Highway Systems"
http://trid.trb.org/view.aspx?id=815535

==Notes==

Also when people (Brent I think?) were talking about locations you'd
go in an emergency, I thought of the CERT volunteers: https://www.fema.gov/community-emergency-response-teams

Complex Systems Summer School 2015-Projects & Working Groups

2015-06-11T03:31:46Z

Rbarnes: Broke Cities resilience into its own page

{{Complex Systems Summer School 2015}}

==City Resilience==
'''Summary:''' This group aims to develop metrics of cities' resilience to various types of disaster, empirically verify this method using information from recent disasters, and compare resilience between a large number of global cities. 
'''Contact:''' Richard Barnes (rbarnes@umn.edu) 
'''Participants:''' Alex Ejedor, Laurence Brandenberger, Masa Haraguchi, Matthew Histen, Will Chang 
'''Wiki page:''' [[Resilience of Cities]]

==Ebola==
The 2014-15 Ebola virus disease (EVD) outbreak in West Africa presented both unique opportunities and unique challenges to the epidemiological modeling community. For the first time during an emerging infectious disease outbreak, high resolution data--from a variety of sources--were made available to the academic community and many public health decision makers genuinely engaged with mathematical and computational modelers. However, the popular and scientific press were highly critical of most models ability to project the outbreak's course. The following key and open questions seem ripe for investigation using a complex adaptive systems lens:
 
1) What features of EVD transmission are most problematic for reliable, robust forecasting: changing behavior, intervention, viral evolution, complex social networks, etc?
 
2) How/can we use digital data to either improve forecasts or inform model selection?
 
3) Can one quantify the value of additional information in real-time?
 
'''Contact:''' Samuel Scarpino, SFI Omidyar Fellow, Santa Fe Institute - scarpino@santafe.edu
 
 
'''Interested:'''
 
Marie-Pierre Hasne 
Chris Verzijl 
Junming Huang 
'Sola Omoju 
Christine Harvey 
Daniel Citron (dtc65@cornell.edu) 
William Chang (williamkurtischang at gee-mail)

==Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe==
''Context''
 
Many communities in Africa have been surprisingly resilient in the face of a host of devastating challenges. The people of Mazvihwa Communal Area in Zimbabwe have lived through more than a century of rapid change through the colonial, liberation war, and post-colonial periods. There have been dramatic changes in public health (ranging from better control of communicable diseases after World War II, to child vaccination programs after independence, to the AIDS pandemic especially from the mid-1990s to the end of the 2000s) and in land access and use (with repeated removals, resistance, and returns of communities to land designated for white settlement). These shifts in population distribution have interacted with rapid natural increase in population (especially in the period 1950-1990) driven by high fertility and declining mortality; followed by recent decades of declining fertility and high AIDS-related mortality. Differences in religious beliefs mean that these changes are uneven across households and areas. The country's economy has meanwhile gone through a series of long cycles of boom and busts, and during the 2000s experienced inflation reaching a billion billion billion per cent.
 
 
The Muonde Trust is a Zimbabwean non-governmental organization established to help support the community in Mazvihwa to continue developing and deploying bottom-up solutions in response to these challenges. Mazvihwa has a semi-arid subtropical climate with remnant woodlands and a combination of largely subsistence agriculture and livestock production. From the point of view of most of the people in Mazvihwa, and as taken up by the community network of the Muonde Trust, the “sustainability” of their area now requires a series of linked changes in land use and investments in natural capital.
 
 
''Data and Questions''
 
The data we have on this community and ecosystem originates from an ongoing community-based participatory research project originally begun in the 1980s and since continued by the Muonde Trust. It includes robust quantitative data on human demography, health, nutrition, agricultural practices, rainfall, land use choices, woodland dynamics, household assets, and land tenure. Our goal at SFI is to develop theoretical or simulation studies which would help us to better understand the resilience and sustainability of this system, which would eventually be informed by the data. Questions we might address using complex systems methods include:
 
 
1) How do individuals and resources flow through households and communities? (Empirical data shows that the composition of households changes rapidly, even though most analyses of these societies tends to assume they are static and natural units of analysis). It is clear that individuals are variously strategizing through households as well as within other kin, religious and clan groups. At the same time households also have emergent properties. In contexts of rapidly shifting demography and changing resource access, are there ways that we can use network analysis to illuminate these complexities?
 
 
2) How best can community as a whole allocate their land to agriculture, pasture, and woodland when these components interact and feedback to each other? One of the main land-use decisions facing the community is the trade-off between agricultural cultivation (which requires fencing to keep out livestock as well as water harvesting techniques) and retaining woodland areas that have cultural value as well as providing grazing space and forage for livestock (and many other economic benefits). This relationship is complex, with livestock providing benefits to agriculture (manure for fertilizer and draft power for cultivation), and vice versa (well-tended fields provide considerable feed for livestock). The community derives benefits from all these land uses, including food for subsistence from agriculture, meat and milk from livestock, and cultural values and a wide variety of benefits from woodland (including fuelwood, construction materials, a variety of foods and medicines, and improved soil characteristics). In addition, community members may sell livestock, as well as using them for bridewealth and compensation in the case of some deaths. How can this system be represented and manipulated in a model to create optimal strategies for the well-being of the system?
 
 
''Possible methods''
 
Our methodology is open to what we learn during the summer school, but some ideas include: network analysis to study the way people and resources connect and flow through the households and other components of the system; an analytical mathematical model of the interacting components of the system, e.g. coupled differential equations; cellular automata which can represent the land use category of each part of a farmer's land and underlie a decision support tool.
 
 
Contact: mveitzel@ucsc.edu
 
 
''Meetings''
 
1) Wednesday June 10th at 10:45 am in the Senior Commons Room 
2) Thursday June 11th at 4:15 in the Senior Commons Room 
3) Friday June 12th at 10:45 at SFI (specific location to be determined; Skype with collaborators from community) 
 

'''Interested:'''
'Sola Omoju

==Mapping Complexity/Human Knowledge as a Complex Adaptive System‏==
''Context''
 
Ants leave pheromone trail patterns which they are aware of only in a local sense. They do not have the cognitive faculties to step back and look at the trails and grasp the ant-trail network as a totality. Also, the artifacts they leave behind are physical entities which then provides the aggregate feedback to the aggregate ant body to then feed the evolution of the ant body as a CAS system. In contrast, humans do have the requisite cognitive abilities. The "pheromone trails" we leave behind are the knowledge trails coded in symbolic knowledge artifacts. In contrast to the physical artifacts that ants leave behind, the knowledge artifacts that we leave behind are far more flexible and potent, both at the aggregate as well as at the individual levels. But like the ants, until recently, we did not have the means to step back and map the knowledge "pheromone trails" to obtain the big picture and its global/local dynamics. The burgeoning field of scientometrics is making available visualization tools to help us map and study the evolutionary dynamics of the knowledge network structures.
 
''Data and Questions''
 
The goals of this project include
<ol>
<li>Extract the terms from approximately 1600 working papers published by SFI</li>
<li>Map the intra/inter conceptual network structures</li>
<li>Study the evolution of these structures across time</li>
<li>High-light the gap-closure of knowledge reverse-salients (if any)</li>
<li>Capture any of the network patterns that repeat</li>
<li>Study the diffusion of concepts across the network</li>
<li>Provide visualization tools for navigating the complexity corpus, etc</li>
</ol>
 
''Possible methods''
 
Latent Semantic Analysis (LSA) and Latent Document Analysis (LDA)
 
References:
<ol>
<li> SFI Working Papers: http://www.santafe.edu/research/working-papers/</li>
<li> Atlas-Science-Visualizing: http://www.amazon.com/Atlas-Science-Visualizing-What-Know/dp/0262014459</li>
<li> Atlas-Science-Visualizing WebSite: http://scimaps.org/atlas</li>
<li> Mapping-Scientific-Frontiers-Knowledge-Visualization: http://www.amazon.com/Mapping-Scientific-Frontiers-Knowledge-Visualization/dp/1447151275</li>
<li> Katy Börner presents at Science of Science: https://www.youtube.com/watch?v=pzCqGBNzomE</li>
<li> Scholarly Data, Network Science, and (Google) Maps:https://www.youtube.com/watch?v=vos5QBDywMM</li>
<li> LSA Video Lect: http://videolectures.net/slsfs05_hofmann_lsvm/</li>
<li> What is LSA: http://lsa.colorado.edu/papers/dp1.LSAintro.pdf</li>
<li> LSA Wiki:http://en.wikipedia.org/wiki/Latent_semantic_analysis</li>
<li> LDA: http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm</li>
<li> Fusari, A. (2014). Methodological Misconceptions in the Social Sciences: Rethinking Social Thought and Social Processes: http://www.springer.com/us/book/9789401786744 </li>
<li> Kirsh, D. (2013). Thinking with external representations. Cognition Beyond the Brain: http://adrenaline.ucsd.edu/kirsh/Articles/Interaction/thinkingexternalrepresentations.pdf </li>
<li> Holland, J. H. (1992). Adaptation in natural and artificial systems: https://mitpress.mit.edu/index.php?q=books/adaptation-natural-and-artificial-systems </li>

</ol>
 
'''Interested:'''
 
<ul>
<li> John Thomas (radjohn@live.com) </li>
<li> Laura Condon (lcondon@mymail.mines.edu) </li>
<li> Haitao Shang (hts@mit.edu) </li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>
<li> Christopher Verzijl (cjo.verzijl@gmail.com) </li>
<li> Nilton Cardoso (nilton_cardoso@hotmail.com) </li>
<li> Glenn Magerman (glenn.magerman@kuleuven.be) </li>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> Laurence Brandenberger (laurence.brandenberger@eawag.ch) </li>
<li> Matthew Histen (matthew.histen@uconn.edu) </li>
<li> Anna Zaytseva (annza944@gmail.com) </li>
<li> Song Binyang (binyang_song@mymail.sutd.edu.sg) </li>
</ul>
 
 

==Navigating Music, Brain and The Edge of Chaos ‏==
''Context''
 
Brain Sciences have revealed that we/it lives "on the edge of chaos" exhibiting "self-organized criticality" that is tentatively balanced between normalcy and madness. Over the course of history, humans have used various agents and activities to shape, influence and control this living-chaos ranging from substances such as caffeine, sugar, drugs etc., to activities such as the arts (including music), social-discourse/therapy, meditation etc. Of these, music has a distinct role in shaping our moods and helping us transition between different mental states, as well as maintain it for extended periods of time. Clearly we have been using music to help us control and shape the internal chaos. But until recently, the quantitative instrumentation of this massively complex system that comprises of close to a 100-billion neurons networked into a 1000-trillion synaptic edifice has not been available to the common man. But of late, affordable, wearable EEG's are available on the market, thus making the quantitative study of the influence of music in brain dynamics feasible on a large-scale/crowd-sourcing sense. To help come to terms with the complexity of our 1000-trillion synaptic edifice, we need to gather data on a vast scale. The proposed research is a proof-of-concept, exploratory foray into making this happen.

 
''Data and Questions''
 
The goals of this project include
<ol>
<li>Evaluate and purchase a wearable EEG</li>
<li>Set up the instrumentation for data capture</li>
<li>Set up the experimentation/data-capture plan </li>
<li>Recruit Subjects</li>
<li>Perform Data Capture</li>
<li>Analyze Results</li>
<li>Propose pathways to take this to the market by embedding it as an app</li>
</ol>
 
''Possible methods''
References:
<ol>
<li> Hacking Your Brain Waves: Wearable Meditation Headsets: http://www.diygenius.com/hacking-your-brain-waves/</li>
<li> Measure your brainwaves and modify your mind: http://venturebeat.com/2012/12/06/muse-eeg-mood/</li>
<li> This wearable device reads your brain waves. Is there a market for it?: http://fortune.com/2014/02/10/this-wearable-device-reads-your-brain-waves-is-there-a-market-for-it/</li>
<li> Your-brain-is-on-the-brink-of-chaos: http://nautil.us/issue/15/turbulence/your-brain-is-on-the-brink-of-chaos</li>
<li> Two Decades of Search for Chaos in Brain: http://www.um.sav.sk/en/images/stories/dep03/doc/krakmeas09.pdf</li>
<li> How You Are Who You Are--in Chaos Theory:https://www.psychologytoday.com/blog/is-your-brain-culture/201008/how-you-are-who-you-are-in-chaos-theory</li>
<li> Diana Dabby Links: https://www.bostonglobe.com/ideas/2013/06/15/what-little-chaos-does-for-music/QLkNTkPIgmec20Db39oHbN/story.html</li>
<li> Diana Dabby Links: http://newsoffice.mit.edu/1998/dabby-0318</li>
<li> Liz Bradley/Diana Dabby Links: https://www.youtube.com/watch?v=6v4vK1iGOCg&feature=youtu.be</li>
<li> Music and the Brain: https://www.youtube.com/watch?v=RsJl6Pys880</li>
<li> Why Music Moves Us: https://www.youtube.com/watch?v=m6Pn9KRVCi4</li>
<li> Measuring musical expressivity: https://www.youtube.com/watch?v=GtSCVqIDl-k</li>
<li> The World In Six Songs: https://www.youtube.com/watch?v=gAl2I30SoTA</li>
<li> This-Your-Brain-Music-Obsession: http://www.amazon.com/This-Your-Brain-Music-Obsession/dp/0452288525 </li>
<li> World-Six-Songs: http://www.amazon.com/World-Six-Songs-Musical-Created/dp/0452295483 </li>
<li> Computer Music and the Importance of Fractals, Chaos, and Complexity Theory: http://recherche.ircam.fr/equipes/repmus/jim96/actes/milicevic.html</li>
<li> The_Complexity_of_Songs: http://en.wikipedia.org/wiki/The_Complexity_of_Songs</li>
<li> Stefan-Koelsch Papers: http://www.stefan-koelsch.de/papers.html</li>
<li> Grammar Based Music Composition: http://www.csse.monash.edu.au/~jonmc/research/Papers/L-systemsMusic.pdf</li>
</ol>
 

Four principles of bio-musicology, W. Tecumseh Fitch 2015 
This paper is a prospectus for biomusical research. Main points: 
i) musical is complex (yes!) 
ii) questions must be asked from a Tinbergean perspective (i.e. mechanism, ontogeny, phylogeny and function), 
iii) comparative between animals (not relevant for us), and 
iiii) "ecologically motivated," i.e. not just Western skilled musicians. 
http://rstb.royalsocietypublishing.org/content/370/1664/20140091 

Neurological implications and neuropsychological considerations on folk music and dance, Sironi V.A., Riva M.A., 2015 
Calls for "Interdisciplinary research on these subjects (ethnomusicology and cultural anthropology, clinical neurology and dynamic psychology, neuroradiology and neurophysiology, and socioneurology and neuromusicology)" 
http://www.ncbi.nlm.nih.gov/pubmed/25725916

'''Interested:'''
 
<ul>
<li> Sara Lumbrera (sara.lumbreras@gmail.com) </li>
<li> Ilaria b (bertazzi.ilaria@alice.it) </li>
<li> Braun, Urs (urs.braun@zi-mannheim.de) </li>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> J Bruineberg (j.bruineberg@gmail.com) </li>
<li> William Kurtis Chang (williamkurtischang@gmail.com) </li>
<li> John Thomas (radjohn@live.com) </li>
<li> Christopher Verzijl (cjo.verzijl@gmail.com) </li>
<li> Glenn Magerman (glenn.magerman@kuleuven.be) </li>
<li> Sahil Garg (sahilgar@usc.edu)</li>
<li> Daniel Hedblom (hedblom@uchicago.edu)</li>
<li> Christine Harvey (ceharvey@mitre.org)</li>
<li> Vanessa Chioffi (v.chioffi@gmail.com)</li>
<li> Daniel Friedman (DanielAriFriedman@gmail.com)</li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>
</ul>
 
 

==Powerlaw fitting and alternative distributions - Theory/statistics ==
Clauset, Shalizi and Newman (2009) propose a maximum likelihood method to estimate the powerlaw exponent of a variable of interest. This is a great improvement on earlier methods such as OLS that dominated the literature up to then. However, one can fit a powerlaw to any dataset and the most we can say is that our observations are consistent with the hypothesis that x is drawn from a powerlaw distribution. One easily implementable method to compare the powerlaw fit to other fits is then a likelihood ratio test for both models.

One particular discussion is the distinction between a powerlaw (PL) and a log-normal (LN) fit. For an avid discussion between both fits on city size and Zipf's law, see Eeckhout (2004, 2009) and Levy (2009), where the discussion now settled on city sizes following a log-normal distribution instead of a powerlaw. Similarly for the discussion on firm size distribution: Simon and Bonini, 1958; Ijri and Simon, 1977; Stanley et al., 1995; Sutton, 1997; Axtell, 2001; Okuyama et al., 1999; Cabral and Mata, 2003; Gaffeo et al., 2003; Aoyama et al., 2004; Fujiwara et al., 2004a,b; Kaizoji et al., 2006; Takayasu et al., 2008; Duchin and Levy, 2008; Schwarzkopf and Farmer, 2008, ...

This distinction matters for several reasons:
- PL and LN come from very similar model and differences in initial conditions can lead to very different outcomes. PL exhibits a choice for x_min, below which the unit of observation is not feasible to exist (eg minimum city size, firm size, word length, ...). LN has no minimum size.
- PL and LN that look similar are the difference between infinite (PL) and large but finite variance (LN)
- shock propagation: when unit-level shocks are large enough to show aggregate perturbances if the distribution is powerlaw with infinite variance, while these shocks wash out fast when the distribution is log-normal. (Gabaix, 1999; Gabaix 2009; Acemoglu et al. 2012, ...).

I have encountered some issues which I would like to explore further:
1. The distinction between lognormal and powerlaw in the data is very sensitive to data truncation: in the above discussions, researchers have slightly different datasets, covering more or less of the population at hand. Left-truncation (i.e. observations not in the dataset because they are too small to be reported) can strongly drive the outcome of the fit, even when endogenizing the x_min cutoff. I have data on the universe of Belgian firms, much more complete than e.g. US Census data, where I have done some preliminary tests on this. The question is then: how to formalise this distinction and what are the theoretical and practical caveats to look out for when applying this method.
2. MLE fitting seems to be sensitive to the choice of units as well: rescaling a variable by a factor 1000, 1000000 etc seems to influence the endogenous x_min choice and hence the estimated parameter. This reminds me of some work on scaling invariance in negative binomial estimators. What is going on here?
3. Can we set up a model that generalises both? I've been looking at Levy stable distributions, but did not do anything with it yet.

contact: glenn.magerman@kuleuven.be

==App design for interaction registration==
I would like to see a simple smartphone app that can track connections being made between people at events. This would allow to map the evolution of a network at eg a network meeting, SFI 2015, social events etc. I know some people at MIT have been working on ID badges for nurses and doctors to track interaction in a hospital, and there are some business apps that show a plethora of features to enjoy a network event (eg like the Yapp app). However, it would be nice to have a simple app that just registers a link between people when their phones are close enough for a certain interval of time. Additionally, it might record some conversation as to create edge information as well.
Unfortunately, I'm not a wizzkid and would need help from an apps programmer to work this out. If it is feasible, I think SFI CSSS 2015 would be a great test case!

contact: glenn.magerman@kuleuven.be

'''Interested:'''
<li> Laurence Brandenberger (laurence.brandenberger@eawag.ch) </li>
*
*
*

==Organ Transplant Analysis==
Over 120,000 people in the United States are currently on the waiting list for an organ transplant. The size of this waiting list relates to over 6,000 deaths a year while waiting for a transplant and tens of billions of dollars in government spending. I have access to the following data sets:
 
* All transplants performed in the US from October 1987 to June 2014 (including follow-up data)
* All living and deceased organ donors in the same time period (with follow-up data on living donors)
* Waiting list data for everyone who signed up for the list
* 2012 National Survey on Attitudes and Behaviors on Organ Donation
* Social media data relating to organ donation since 2008

Open to ideas and suggestions for the topic, there are a lot of interesting questions to investigate including cultural/racial/gender differences in organ donation. I have several preliminary reports and exploratory analysis done on differences in donors.

Second Meeting Thursday, June 11th 4:15

===First Meeting Notes===
Ideas:
*Look into the differences in waiting time geographically and map how it changes over time.
*Differences in cities compared to suburban areas.
*What are the critical links to perform matches for multiple people.
*Look at scaling laws in the data, if the number of donors grow, how does the number of successful transplants also grow. This includes the correlation between wait time and organ donation or size of waiting list.
*Investigate trends and dynamical systems, review the following dynamics as a function of time:
**Entry rate to the waiting list system
**Exit rate from the waiting list (transplant, death, other)

There is a Google Doc with space for notes. Please email Christine Harvey (ceharvs@gmail.com) for access.

==Multi-dimensional social networks in the evolution, development and resilience of informal economies. ==
Informal economies are defined as economic activities that occur outside the purview of corporate public and private institutions. These types of economies proliferate where traditional economic actors are unable to productively exercise their activities, especially due to costly constraints (De Soto 2003). Firms may face adverse incentives to expand production by hiring more workers or incorporating more capital due to the low productivity of their workers or the predatory practices of rapacious elites or corrupt governments. Labor itself, i.e. people, may face hurdles in trying to make the jump towards entrepreneurial activities or jobs in the formal economy which allow the accumulation of experience, retirement savings, access to insurance or precautionary savings to face unexpected events (like disease, disability, etc.) due to lack of capital access (whether human and financial).

'''For this project, we propose a different interpretation: We seek to understand and model how multi-faceted social networks provide robust alternatives to formal economies, which far from being seen as degenerate forms of social organization, in many instances co-exist, challenge and compete with formal employment and economic activities. While some types of informal economies operate under adverse contexts, their resiliency may be understood as a type of adaptive fitness and not the mere result of stubborn cultural path-dependency. '''

For the most part, informal economies have been analysed as counterpoint to an ideal type of a formal economy with low levels of trust, respect of property rights, access to credit and public institutions - like the court systems (see Losby et al. 2002 for a review). However, informal economies have been coupled with their formal counterparts since the development of capitalism in the 19th Century. To name a famous historical example, Old London's East End's informal sector was described harshly by Engels (1844) in his famous tract, "The Condition of the Working Class in England". But almost fifty years later, in a new preface to the book, Engels recognized the progress that took place there under the aegis of working class organizations in the area.

Research has begun to catch on to this idea – developing a literature in the areas of informal risk sharing and remittances, which are in some sense informal counterparts to insurance and banking. Consumption patterns in poor, rural villages are remarkably smooth suggesting that risk-sharing measures are prevalent, although imperfect (Townsend). Field studies of networks underlying village risk sharing systems have found that households primarily receive help from existing social connections, such as friends and relatives, in the form of informal loans or transfers. Theoretical work in network economics, by authors such as Matt Jackson, found that certain empirically prevalent network structures may directly benefit the stability of favor exchange systems. Another line of inquiry focuses on remittance income, generally informal transfers across kinship ties. World Bank researchers have found that remittances from overseas migrants respond dramatically to regional income shocks, replacing upwards of 60% of lost income in households with international migrants. Further studies, cited below, have generalized those results. Furthermore, reductions in the cost of sending remittances, which occurs with the advent of mobile money, further mitigates exposure to risk in receiving households.

Also recently, authors have written on how communities come together in situations where formal economies are unavailable by external shocks (like disasters) or due to lack of access (due to conditions of abject poverty). In the case of the latter, Venkatesh (2008) provided gripping testimonies of how informal economies arose and developed in the South Side of Chicago in low trust environments via cash transactions and informal service contracts enforced by (and for the benefit of) local criminal gangs. These gangs were seen, surprisingly, as alternative coordinating mechanisms to settle claims between neighbors. In the case of the former, Storr and Grube (2013) in a series of papers has argued how "shared histories and perspectives, and the stability of social networks within the community" allows communities who suffered disasters to cope and endogenously resolve immediate and complex problems. Providing an example of these social networks in action, research has found that remittances flow quickly into areas affected by natural disasters when there is a technology in place for it to happen.

Taking these lines of inquiry together, the findings suggest that informal handling of risk takes place on a large scale and that social networks informally connect communities in ways that impact their economies. Recent trends suggest that the interaction of formal and informal economic processes is growing on a nationwide scale. First, the aggregate value of remittance flows is large and growing, nearing the value of foreign direct investment. Second, advancements that formalize previously informal transactions are expanding dramatically in emerging economies through various forms of branchless banking. National economies may be embedded in profoundly influential informal systems that have never been holistically studied.

On a more general note, this project touches lingering questions in economics. For example, some might argue that more stringent labor regulations should have increased informal employment, but in fact the opposite happened. And while the economic rationale for the former statement remains valid, we suggest that unions, as other types of social organizations, as examples of ways whereas people interact via organized networks, provide richer dimensions than those suggested by their interaction as mere economic agents. Hence, unions, as well as other types of faith-based, ethnic, community and other interest groups may provide ways of interaction that escape narrow economic outcomes.

====References====

• De Soto, Hernando (2000/2003) The Mystery of Capital. Basic Books.
• Losby, Jan; Else, John; Kingslow, Marcia; Edgcomb, Elaine; Malm, Erika and Vivian Kao (2002) The Informal Economy: A Literature Review. ISED Consulting and Research and the Aspen Institute Working Paper. http://www.kingslow-assoc.com/images/Informal_Economy_Lit_Review.pdf
• Townsend, Robert M. "Risk and Insurance in Village India." (1994)
• Fafchamps, Marcel, and Susan Lund. "Risk-sharing Networks in Rural Philippines." (2003)
• Weerdt, Joachim De, and Stefan Dercon. "Risk-sharing Networks and Insurance against Illness." (2006)
• Matthew O. Jackson, Tomas Rodriguez-Barraquer and Xu Tan. “Social Capital and Social Quilts: Network Patterns of Favor Exchange.” (2012)
• Yang D and H Choi."Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines"(2007)
• Kurosaki, Takashi. "Consumption vulnerability to risk in rural Pakistan." (2006)
• Jack, W, and T. Suri. "Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution” (2014)
• Engels, Friedrich (1844/1892) The Condition of the Working Class in England. http://www.gutenberg.org/files/17306/17306-h/17306-h.htm
• Venkatesh, Sudhir (2008) Gang Leader for a Day: A Rogue Sociologist Takes to the Streets. Penguin Books.
• Storr, Virgil and Laura Grube (2013) The Capacity for Self-Governance and Post-Disaster Resiliency. George Mason University, Department of Economics Working Paper 13-37. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2350830##
• Blumenstock, Joshua Evan and Fafchamps, Marcel and Eagle, Nathan. “Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda” (2011).
• Ratha, Dilip. "Workers’ remittances: an important and stable source of external development finance." (2005).
• Pénicaud, Claire, and Arunjay Katakam. State of the Industry 2013: Mobile Financial Services for the Unbanked. Rep. N.p.: GSMA MMU, 2013.

If interested, please list your name below.

'''Interested:'''
<li> Eloy Fisher (eloy.fisher@fulbrightmail.org) </li>
<li> Carolina Mattsson (carromattsson@gmail.com) </li>
<li> Sharon Greenblum (sharongreenblum@gmail.com) </li>

<li> Jakub Rojcek (jakub.rojcek@gmail.com) </li>

==Exploring Community Formation through Analysis of Scholarly Corpus (ArXiv)==

The Arxiv [http://arxiv.org/] is a free online repository of scientific preprint articles, mostly from physics and mathematics. It currently contains over 800,000 articles, dating back to 1991. Currently, a lot of the data associated with this repository is completely available: paper submission dates; full texts of papers; author names and coauthorship information. Additionally, there is some citations data available. (If necessary, I can also find papers' subdiscipline labels; submitting authors' email address domain names; other things.)

In the past I have been using these data to try and explore how communities of authors who have shared interests grow over time. For example, we can pinpoint the first ever paper about topological insulators, and search for all subsequent papers on that topic. As more papers are written, authors begin to join the field of research and to form strong ties by collaborating with one another. We can use the ArXiv data to visualize and analyze exactly how these communities form and grow.

===Brainstorming notes===
Isolation between topics, crossing interdisciplinary boundaries, finding (topological) separation distance between disciplines or research groups

Tipping points - can we look for separation of or mergers of two groups or disciplines?

Can we identify key papers (or groups of papers) that initiate ties between fields.

Sentiment analysis: can we identify when a paper's citation is an endorsement or a refutation?

Tools for Analysis: Change point detection; relational event models

Can we find more comprehensive/better citation data?

Lucene and indexing tools:
- Can we re-index our text database after removing stop words?
- Can we index the titles and abstracts?
- Elastic Search - tool for interacting with Lucene

===Next meeting===
Thursday @ 9AM in Coffee Shop

== Political Speech ==

Some references:

[http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.734168 Taddy (2013), "Multinomial Inverse Regression for Text Analysis"] 

[https://www.cs.princeton.edu/~blei/papers/BleiLafferty2007.pdf Blei and Lafferty (2007), "A Correlated Topic Model of Science"] 

=Preliminary Discussion Meetings=

== Free Energy Theory ==
The Free Energy (FE) minimisation framework tries to explain how biological systems (such as a cell or a brain) self-organise in order to occupy the (often very limited number of) non-equilibrium states that minimise free energy. This is also known as active inference. A simple corollary of active inference is that agents behave as to minimise their prediction error, or the difference between prediction and reality. Thermodynamic free energy is a measure of energy available in a system to do useful work. This can be framed in an information theoretic setting, as the difference between how the world is being represented and how it actually is. A better fit means a lower information-theoretic free energy, as more resources are being put to ‘good use’ in representing the world. The overarching logic of FE theory is that a better model of the world help maintain structure and organisation, which ultimately helps the system resist increases in entropy.
 
 
This is not a set-in-stone project with any concrete aim (yet). We are a few people interested in exploring the theoretical and practical implications of these ideas, and you're more than welcome to join in!
 
 
'''Interested:'''
 
<ul>
<li> Jelle Bruineberg (j.bruineberg@gmail.com ) </li>
<li> Tobias Morville (tobiasmorville@gmail.com) </li>
<li> Susanne Pettersson (guspsusa85@student.edu.se) </li>
<li> Maggie (margaret.w.simon@gmail.com) </li>
<li> Sahil (sahilgar@usc.edu) </li>
<li> Tirtha (Tirtha.Bandy@csiro.au) </li>
<li> Anna (annza944@gmail.com) </li>
</ul>
Reading:
 
http://rsif.royalsocietypublishing.org/content/10/86/20130475 
http://arxiv.org/abs/1503.04187 
http://www.mdpi.com/1099-4300/15/1/311 

==Exchange-Company Networks (jakub.rojcek@gmail.com)==
11:30am in the coffee shop

==Decision support/network analysis of a complex socio-ecosystem in rural Zimbabwe (Melissa - mveitzel@gmail.com)==

10:45am in the senior common room (the room behind our lecture hall)

==Ebola virus disease spread (Junming - mail@junminghuang.com)==

11:00am in the coffee shop

==Scaling effects in bodies, communities, ecosystems (Cobain - mrdc1g10@soton.ac.uk)==

11:30am in the coffee shop

Also tying in prehistoric hunting populations

==Dynamics of homicide (Matthew Ingram - mingram@albany.edu)==

Integrating temporal, spatial, and multi-level concepts

1:30pm in the coffee shop

Am interested in the discussion on this project...Sola Omoju

==Organ Transplant (Christine - ceharvey@mitre.org)==

2pm in the coffee shop
Interested:
Sahil Garg

==Multi-dimensional social networks in the evolution, development and resilience of informal economies ==

2:00pm in the lecture hall

Eloy & Carolina - eloy.fisher@fulbrightmail.org, carromattsson@gmail.com

==Multiplex Adaptive Networks (Daniel - dtc65@cornell.edu)==

7 pm in the coffee shop

==Modeling brain diseases (or cancerous bio pathways) (Sahil - sahilgar@usc.edu)==
Interested people can put a meeting time as per their convenience here.
 
No meeting time indicated
 
One potential meeting time can be 3pm in coffee shop ?
 
Information theoretic algorithms can also be explored for the problem.
 
Discovering Structure in High-Dimensional Data Through Correlation Explanation. http://papers.nips.cc/paper/5580-positive-curvature-and-hamiltonian-monte-carlo
 
Maximally Informative Hierarchical Representations
of High-Dimensional Data. http://jmlr.org/proceedings/papers/v38/versteeg15.pdf

 
'''Interested:'''
 
<ul>
<li> Emilia Wysocka (emilia.m.wysocka@gmail.com) </li>
<li> Maggie Simon (margaret.w.simon@gmail.com) </li>
<li> Laura Condon (lcondon@mymail.mines.edu)
 
 

==Analysis of UK parliament speeches 1935-2014 (Stefano - etstefano@gmail.com)==

No meeting time indicated

==Mapping Complexity/Human Knowledge as a Complex Adaptive System (radjohn@live.com)==
2pm/Wed 6/10 in Conference Room (Tentative)

== Resource allocation trade-offs (Andre - andre.veski@gmail.com) ==
Using evolutionary algorithms to investigate trade-offs in allocating goods among agents. This could be potentially done on some real dataset (if you have any) or somehow parametrized synthetic data. Also this could be envisioned as a strategic bargaining between agents, which would introduce some dynamics into the process.

Email me or sign-up if interested and we'll setup a meeting time.

Interested: Sola Omoju, Christine Harvey (ceharvey@mitre.org)

==Navigating Music, Brain and The Edge of Chaos (radjohn@live.com)==
10:45am/Wed 6/10 in Conference Room

'''Interested:'''
Sahil Garg

==Rule-based modeling for brain diseases (molecular level) (emilia.m.wysocka@gmail.com)==

Rule-based modeling features:

*biological systems as concurrent processes
*dynamics of post-translational modifications
*domain availability
*competitive binding
*causality and intrinsic structure
*binding sites
*interaction rules replace reaction equations
*infinite number of reactions with a small and finite number of rules
*reduction of parameter space
*“don't care don't write” - adjustable rule contextualization
*single reaction rule and parameters generalize classes of multiple rules
*modular and extensible language
*specification language & simulation/integration environment
*static and causal analysis

Kappa/KaSim & BioNetGen/NFsim -- specification language/network-free simulator

Meeting -> if anybody is interested - contact me.

Some refs:

*Danos, V., & Laneve, C. (2004). Formal Molecular Biology. Theoretical Computer Science, 325.
*Sorokina, O., Sorokin, A., & Armstrong, J. D. (2011). Towards a quantitative model of the post-synaptic proteome. Molecular bioSystems, 7(10), 2813–2823. doi:10.1039/c1mb05152k
*Suderman, R., & Deeds, E. J. (2013). Machines vs. Ensembles: Effective MAPK Signaling through Heterogeneous Sets of Protein Complexes. PLoS Computational Biology, 9(10), e1003278. doi:10.1371/journal.pcbi.1003278
*Chylek, Lily A. and Harris, Leonard A. and Tung, Chang-Shung and Faeder, James R. and Lopez, Carlos F. and Hlavacek, W. S. (2014). Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems. Wiley Interdisciplinary Reviews. Systems Biology and Medicine, 6(1), 13–36. doi:10.1002/wsbm.1245
*Chylek, Lily A. and Stites, Edward C. and Posner, Richard G. and Hlavacek, W. S. (2013). Innovations of the rule-based modeling approach. Systems Biology: Integrative Biology and Simulation Tools. Retrieved March 11, 2014, from http://openwetware.org/images/7/74/LosAlamosTechnicalReportLAUR1210375.pdf
*Danos, V. (2007). Rule-Based Modelling of Cellular Signalling. Lecture Notes in Computer Science (Vol. 4703). Berlin, Heidelberg: Springer Berlin Heidelberg. Retrieved from http://www.springerlink.com/index/10.1007/978-3-540-74407-8
*http://www.pps.univ-paris-diderot.fr/~jkrivine/homepage/Teaching.html