Complex Systems Summer School 2012-Projects & Working Groups

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Complex Systems Summer School 2012

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Project proposals

Nonequilibrium game theory

My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday. Also, here's a fun 3-minute video of the effect I'm personally the most interested in, with a special appearance from The Princess Bride.

Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions?

Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).

Traffic pattern analysis - Can we estimate car velocity by only observing car counts?

Problem statement

Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.

Ideas on how to approach this

I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

Update 06/05/12: Just today we saw Takens theorem about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the SFI Project subsection on Georg M. Goerg or email me to Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

Cultural Evolution - things that look like drift but aren't

Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!


vanferdi [at]

"Small Steps and Big Ideas" Group

Christa Dan Xin and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

Dan: Here's a really relevant paper I've come across "intervening to achieve cooperative ecosystem management" JASSS

Using twitter data tutorial:

107 Proteins in 10-15 cubic meters

Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example Weigel et al., PNAS 2011). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]

Innovation and Technological Progress

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link:

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam [at] or find me in class. Innovation Group Project

Space, Stochasticity, Stability; Speciation?

Xue, Chloe and Xiaoliare all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. Si is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

The Spatial-Stochastic group is writing up their ideas to share here and look for overlap and coupling.

Entrenchment and rhythms

One idea that I had was to look at the entrenchment properties of various systems. This is an universal phenomena that arises from nonlinear mechanisms interacting with a fluctuation environment and appears most often in animal and plant physiological rhythms (e.g. circadian rhythms, sleep cycles) and result in predictable oscillations that can also sometimes be forced into stable/unstable states by noise (in the case of humans, this can result in disease). I would like to see if there are any mechanisms that produce similar behavior at the ecosystem level based on structural or species/functional diversity, especially in climates where the energy/water input is non-uniform. The "noise" in this case could be natural or anthropogenic disturbances. I think this can be generalized into many different types of systems. If you have an idea on this, please shoot me an email at

Plasticity in Neural Networks

I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output. I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal. Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email [1]

Robustness of complex networks

Systemic Risk in Financial Networks and/or an ABM of money/liquidity

Systemic Risk in Financial Networks: Hypothesis: the motive to diversify risk at the level of the individual agent (i.e., for an agent to increase its connectivity) will increase systemic risk (by systemic risk I mean vulnerability of the system to widespread collapse). Point of departure is the Forest Fire model from statistical physics.

Key difference(s) between the physics version of the Forest Fire model, and the "economics" version of the Forest Fire model I have in mind are:

  • Tree growth probability (which determines network structure) must be endogenous. Agents must be able to choose which other agents to link with.
  • Probability of lightning strikes (i.e., defaults on specific loans) must also be endogenous.

I think that financial networks might exhibit self-organizing criticality in the sense that diversification will reduce the probability of lightning strikes (i.e., defaults), however over time systemic risk builds up as a result of the diversification which means that eventually a small number of lightning strikes might be enough to bring the entire system down.

ABM of the emergence of Money: Basically, I would be interested in building an ABM of the emergence of money based around the following economic models of money developed by Nobu Kiyotaki and John Moore:

These models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange. In these models agents manage projects which require capital investment now in order to generate a return at some point in the future and agents must trade financial promises (think debt contracts) in order to obtain the needed investment. Two key parameters of is these models (which are assumed COMMON to all agents in the above models in order to maintain analytical tractibility) are 0 < theta < 1 and 0 < phi < 1. Theta is the fraction of the future return from the project that an agent can promise to repay in the future in exchange for investment now. Phi is the fraction of the face value of a debt contract (which by construction is a contract between two agents) that can be re-sold to a third agent.

Hypothesis: In an ABM where agents differ in terms of both theta and phi, the promises of only a small number of agents will be widely traded (i.e., will serve as money).

For more information about the project check out the Emergence of Money and Liquidity.

Price-time dynamics of contracts traded on prediction markets

Prediction markets have been shown to outperform traditional methods of polls and opinion surveys in forecasting future events. I am interested in exploring the price-time dynamics of contracts traded on prediction markets to better understand how they are able to aggregate individual opinion to establish collective insight.

Several questions that I’m curious to probe further:

  • How do ‘information shocks’ generated by news sources influence price-time trajectories?
  • Can features of the underlying dynamics be characterized using a simple model?
  • What is the minimum number of traders required for an accurate prediction?

On a separate note, I invite you to share your opinion regards whether “China will win the most medals at the 2012 London Olympics”, by logging into the following site (please send me your email address and I will send you the login details).

If you haven't used a prediction market before don't worry -just follow the instructions provided in the site to 'buy' and 'sell' contracts according to your expectation.

If you are interested in the discussing any of the above questions or have other ideas related to prediction markets please get in touch with me at: sanith at mitre dot org

Useful Links:

Internal models: what do they do and how are they built?

In the past decade(s) Bayesian statistics has come to dominate empirical science. Consequently, the significance of prior beliefs for guiding inference has become widely accepted. But how do we map the concept of prior beliefs onto natural systems? I argue that the composition of organisms realize internal models of their environment. These internal models manifest as structured behavior, which we scientists describe as reflecting prior beliefs or bias. In humans you have reflexes at one extreme and the influence of memories upon behavior at the other. It is an open question how these internal models are instantiated in biological systems. Are they structural motifs in neural networks? Protein networks within cells? Concepts such as memory, storage, and recall provide relevant bridges between the statistical formulation of these ideas and the physics of computation, but these are jumping off points at best.

My suspicion is that part of the challenge is we don’t have a clear understanding of what benefits internals models impart to organisms beyond some general statement about resolving uncertainty. This is compounded by the fact that we probably wouldn’t recognize an internal model if we saw one. This is why I find work over the past decade upon self-localizing and mapping (SLAM) systems to be so interesting. To my knowledge, these are the first man-made systems designed with the objective of imparting complex internal models to artificial systems. The Mars rover is a SLAM system. The driverless car depends critically upon a SLAM system. The successes, and failures, of these systems have exposed the complexity of functionalities we use so naturally that they evade our notice. These include differentiating between static and dynamic elements of our environment as well as ascribing our sensations to external or internal causes. In the least, the design of these systems offer first order models for what an internal model of non-trivial complexity might look like.

Here are a few files on SLAM which you might enjoy. The first is a general tutorial on the concepts involved and the second and third are about a particular implementation which I find fascinating: DP-SLAM.

Update: Below is what I take to be an excellent example of how chaotic attractors, sensorimotor transformations, and robotics can come together:

Update: as I've been digging into how different data structures are more or less suited for the processing of distinct information domains, I keep coming back to the work of Tenenbaum, Kemp, and Griffiths. Their work is definitely worth knowing about.

I’m interested in exploring the role of internal models as well as how they are embedded in natural systems. The project group is currently converging, but I am interested in cross-over between projects as well as intermittent discussions. In the next few days I will have my SLAM framework up and running. Feel free to email me at: (John Long)

Rain-Climate-Agriculture Interactions

We are trying to think about the influence of different precipitation/climate regimes on farming strategies and crop prices. In places where water is the limiting factor for crop growth, changes in precipitation intensity, or in precipitation patterns are likely to dramatically affect the choices that farmers could make in order to produce more, or simply to survive. How will they react under different regimes? Which are the best strategies? We are meeting tomorrow afternoon (Sunday): if you're interested in it, you're very welcome to join us! and

Is there a method in the madness? the dynamic structures of human language use

In this project we explore the dynamics of human voice. What is the structure of voice at different level (fluency, prosody, words)? How do we convey information through these levels and how are they related? How do we combine our individual linguistic behavior in conversational dynamics?

We have two possible ways of exploring these issues

1. Psychiatric anecdotal reports point to the monotony, lack of emotion and sometimes intelligibility in clinical populations. We have recordings and transcriptions from 4 clinical population (Asperger's, Schizophrenics, Depressed and Right Hemisphere Damage patients) as well as from healthy controls describing 10 simple videos. This allows us to contrast "healthy" and impaired use of voice.

2. We have recordings and transcriptions from 16 pairs of individuals repeatedly solving a joint decision problem with precise measures of performance. This allows us to compare: - different degrees of effectiveness in conversational dynamics - but also the dynamics in the individual contributions to the dynamics of the overall conversation

We are interested in applying different methods to these data, from measuring the amount of information and complexity in these data, to fractal dimension and phase reconstruction. If you are interested contact Priya and Riccardo