Complex Systems Summer School 2013-Projects & Working Groups

Complex Systems Summer School 2013

Project proposals

How the ocean can help us heal complex chronic disease

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

Anyone else interested? – Kristen Honey

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

GDELT

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

Research Network Formation

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

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

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

Self-consistent networks for socio-economic institutions

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

Genetic algorithms to evaluate network formation or real-world data

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

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

Caribou Management Dynamics

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

Evolving synchronized flashes in fireflies

I was thinking about how some, but not all, species of fireflies can synchronize their flashes, as was mentioned in both lectures today (June 4). The mechanism is fairly simple, it seems, so we should be able to evolve it using a simple genetic algorithm, right? This is only half-baked at the moment, and I haven't checked to see if it has been done already, but I thought it would be neat to explore the space around these biological phenomena. More of a fun project than a serious "lets publish this!" type of project. Bonus points if we can work some neural network stuff into it. Bryn_Gaertner

How do historic facts collapse into written history?

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

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

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

Meta Food Webs

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

(Evolution of) Aging

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

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

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

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

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

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

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

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

Cheers, Maarten

Quantifying Synchrony in Dynamics Occuring on Networks

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

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

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

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

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

Dave Darmon