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Complex Systems Summer School 2017-Projects & Working Groups

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Complex Systems Summer School 2017

Evolution Working Group/Microbial Systems are Complex

BRAINSTORMING MEETING AT 8PM on 6/13 (TUESDAY=TODAY). Meet by Koi pond (then we will migrate).

Email Jake w/ questions (jw4336(at)umd.edu).

Summary

Several of us seem to have shared interests in microbial ecology/evolution/genetics and/or evolutionary ecology/evolutionary theory in general. It might be a good idea to meet up and brainstorm a project (maybe over beer?). Some potential ideas that were discussed during speed dating:

  • The evolution of the bacterial pangenome
  • Development/adaptation of classical population genetic techniques to microbes (maybe via a network describing HGT interactions)
  • The evolution of metabolism/the interaction between metabolism and community structure
  • How interspecific interactions (e.g. crossfeeding, competition) influence the rate of decay of community similarity in a homogeneous environment (scale of spatial organization)


Interested Participants

  • Jake Weissman (jw4336(at)umd.edu)
  • Makoto Jones (makoto.jones(at)hsc.utah.edu)
  • Hilje Doekes (h.m.doekes(at)uu.nl)
  • Basak Taraktas (basak.taraktas@northwestern.edu)
  • Alicia Kraay (amullis (at) umich.edu)

EU: responding to migrant crisis

Keywords

cooperation, migration, consensus-reaching, supranational unions

Summary

How does an organization of independent countries, such the European Union, deal with a migrant crisis? How can a consensus on a response be reached? Why did the EU fail to deal efficiently and fairly with the 2015 migrant crisis (>1m people)? Why isn’t the migrant quota system not working? (Or is it?)

Yes, these are all pretty big questions, but I believe very exciting and timely. Haven’t figured out details obviously, but I think these could be explored with agent based models (possibly NetLogo?). It would be particularly interesting to see how a few factors influence the chances that a consensus is reached and what type of consensus:

  • country on coast/external border vs landlocked
  • how far from coastal border
  • economic power
  • diplomatic power
  • Union organization: continuum - federal government can impose policies —> unanimous consensus must be reached every time

If you find this interesting, include your name and email on the list below & let’s meet up and discuss!

Resources

Interested Participants

  • Alberto Micheletti (biology, social evolution, ajcm2[at]st-andrews[dot]ac[dot]uk) - group contact
  • Andrew Johnson (biology, ecology, competition, collapse, afjohnson[at]ucsd[dot]edu)
  • Ella Jamsin (sustainability, design, tipping points, e[dot]jamsin[at]tudelft[dot]nl)
  • Madison Hart (psychology, social evolution, collapse, madisonkhart(at)gmail.com)
  • Junfu Zhao (economics, Marxism, Imperialism, zjf18810688936@gmail.com)
  • Basak Taraktas (basak.taraktas@northwestern.edu)
  • Martina Balestra (management, human computer interaction, mb5758@nyu.edu)

Gut-Brain Axis: Impact of gut microbiota on mental health

Keywords

public health, microbiome, metabolism, network analysis, neuroscience

Summary

The term "gut feeling" is not without a scientific basis. Recent literature has emphasized the connection between the brain and intestinal microbes. Studies are beginning to link neurodegenerative disorders, such as Alzheimer's disease and multiple sclerosis, to gut dysbioses.

Other relationships between the brain and the gut are waiting to be explored. Also, on a broader level, relationships between microbial metabolism and neurotransmitter levels could be investigated.

Open source data sets are available, such as the ones mentioned in this paper: bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-016-0307-y

If you find this interesting, include your name and email on the list below

Interested Participants

  • Bleu Knight (bleu AT nmsu.edu; happylittleaccident AT gmail.com)
  • Aaron Schwartz (aschwa15 AT uvm.edu)
  • Madison Hart (madisonkhart(at)gmail.com)
  • Deepa Rao [drao@mit.edu]

Beer: evolution through the ages

Keywords

beer, evolution, genetic elements, history

Summary

Brewing beer is an ancient art that has experienced a renaissance in the last few decades. Can we model beer recipes in an "evolutionary" sense? Can ingredients like hops and malt be modeled like genes that are expressed more in some beers than in others? Can we create phylogenetic trees that delineate relationships between beers? Can beers be considered 'species', and if so, what has contributed to the diversity that we see in the US today?

If you find this interesting, include your name and email on the list below

Resources

Media:5000_yr_old_Chinese_beer_recipe.pdf
Media:Early_Iron_Age_and_Late_Mediaeval_malt.pdf
beer recipe database: beerrecipes.org
medieval english ales: cs.cmu.edu/~pwp/tofi/medieval_english_ale.html
about food (not beer): Flavor network and the principles of food pairing DP: I think it is a great reference to start with!

Interested Participants

Bleu Knight (bleu AT nmsu.edu; happylittleaccident AT gmail.com)

  • Doheum Park (park154 AT kaist.ac.kr, innovation, network science, data science)

International Trade Patterns in Local Fisheries

Keywords: cascading effects, overfishing, international seafood trade, exploitation patterns

Summary

Seafood is an increasingly globalized food commodity with now 40% traded internationally. At the same time, the majority of global fish stocks are being fully- (58.1%) or over-exploited (31.4%). International buyers connect to local fisheries as the fishery is overexploited the buyer moves on to the next location. This pattern of serial exploitation has been observed in a fisheries worldwide such as tuna, cod, sea cucumber. We aim to explore the linkages and changes in bilateral trade networks that happen before and after a fish stock collapse to find different patterns of exploitation. A starting point for the analysis may be studying anomalies and changes in the global network of seafood trade.

Include your name and email on the list below to talk more :)

Resources UN Comtrade publishes freely available bilateral trade data: https://comtrade.un.org/

RAM legacy is a database which contains marine stock assessments (collapses): http://ramlegacy.org/

Participants

Maartje Oostdijk (group contact) Laura Elsler (group contact) Basak Taraktas (basak.taraktas@northwestern.edu)

Agent-Based Prediction Competition

Keywords

Agent-Based Models, Machine Learning, Prediction

Summary

Agent-Based Models (ABMs) have so far been used mainly for descriptive purposes. Some people (including myself) think that ABMs can unleash their full potential in making predictions. I envision this project as an open-ended test to this hypothesis. The main idea is borrowed from the machine learning prediction competitions (www.kaggle.com), in which you are given a training dataset and have to outperform the other participants in predicting the outputs of a test set. Instead of using a machine learning algorithm to approximate a target function, I propose we use an ABM. This project can take several directions:

  • The topic of the ABM and the data can come from any discipline: biology, epidemiology, economics, sociology, ecology, etc.
  • The data can be real or synthetic. In the latter case, someone could code a very complicated ABM as the Data Generating Process of a fictitious system, and the task would be to approximate this complicated ABM.
  • A humble goal would be to take machine learning as an upper bound for prediction possibilities (https://site.stanford.edu/sites/default/files/submission_kleinbergliangmullainathan.pdf), and see how putting realistic assumptions in the ABM makes the prediction score close to the machine learning benchmark.
  • A more ambitious goal would be to beat the machine learning algorithms. We know they can fail because the world is non-stationary (see Google Flu Trends failure). ABMs can account for that.
  • Any other direction!

I would be happy to discuss about any practical example with whoever is interested. I think this project can be very risky, but this is why I’m proposing it here! Still, at least a proof of principle would be a great result. I'm open to any hijacking of this project however!

Group Contact

Marco Pangallo (complexity economics, marco[dot]pangallo[at]maths[dot]ox[dot]ac[dot]uk)

Interested Participants

  • Meghan Galiardi (meghangaliardi(at)gmail.com)
  • Elliot Nelson (enelson(at)pitp.ca)
  • Madison Hart (madisonkhart(at)gmail.com)
  • Martina Balestra (mb5758@nyu.edu)
  • Uzay Cetin (uzay00(at)gmail.com)
  • Emily S (eshouppe [at] gmail [dot] com)
  • Cigdem Yalcin (cigdem_yalcin(at)yahoo.com)
  • Chris Miles (cmiless@umich.edu)
  • Andrew Johnson (afjohnson[at]ucsd.edu)

AI-aided Graph Auto-generation

Keywords

Graph Auto-generation; Graph Characteristics; Artificial Intelligence; Combinatorial Optimization;

Summary

Complex networks/graphs appear in almost every aspect of science and technology, ranging from social systems, computer networks, biological networks to the state spaces of physical systems, even Bayesian Belief Networks. Graph auto-generation has wide applications. Just like what is done by the Google AlphaGo, this oftentimes ends up as a combinatorial optimization problem. The prohibitive number of possibilities calls upon the Artificial Intelligence to determine better strategic choices at every turn. There are several interesting topics.

  • As a starter, we would like to determine the topological structure of given networked systems. A variety of basic measures and metrics are available that can tell us about small-scale structure in networks, such as correlations, connections and recurrent patterns, but it is considerably more difficult to quantify structure on medium and large scales which can help us understand the ‘big picture’.
  • Next, a fundamental property of interdependent networks is that failure of nodes in one network may lead to failure of dependent nodes in other networks. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of several interdependent networks.
  • Thirdly, assume we have a collection of node and a defined dependency model. Can we auto-assemble these node to form a network which satisfies performance requirements. The dependency model can be defined in the following way
   1. Each node has a defined interface, which specifies what kind of supplies it needs and what kind output it can produce.
   2. The output of a given node can be computed based on its chosen suppliers and available supply levels.
  • Finally, given a set of nodes, assuming the edge length between any pair of nodes is computable, can we find a shortest path between two given nodes such that the path consists of edges with bounded length. Moreover, given multiple measures of the edge length, can we find a Pareto front of shortest paths? In other words, a multi-objective optimization of such paths.

Group Contact

Huang Tang (MITRE, Co., htang@mitre.org)

Interested Participants


Shared rituals, memes and group identity

Keywords

meme, rituals, group identity

Summary

Shared language or rituals are important for identification of group members from non-members, which itself is important for group cohesion and group identity. In pre-state societies, where survival was dependent on intragroup collaboration, this was of high importance. In modern culture, mere survival is often guaranteed by states, but group identity is still important, whether in meatspace or in cyberspace.

This project will try to identify origin and spread of internet memes, as a form of internet rituals, their importance for group cohesion and their dead, i.e., when popular memes spread into different communities and became common in internet, while losing their original function.

Group Contact

Jiří Moravec j.moravec [at] massey.ac.nz

Interested Participants

  • shanee stopnitzky entropy@ucsc.edu - i was going to propose the exact same project! awesome!
  • Madison Hart (madisonkhart(at)gmail.com)

Multi-Layer Networks

Keywords

networks, ecology, multilayer networks, interactions

Summary

Networks are a powerful approach to studying ecological systems; however, they typically represent a "monolayer network" of interactions in a snapshot of time. This project is interested in using a "multilayer network" approach to include more complexity in ecological networks. With a multilayer network, we can represent the same ecosystem using layers that describe the system either with several interaction types (competition, cooperation, predation, etc.), communities of species, or over time (time-series data on occurrences, for example). By using interlayer edges, we can connect the monolayers of the network.

The paper that describes this approach includes a table with ~10 available datasets (Supplementary Table 1) and leads to packages in Python, R, and Matlab (Supplementary Table 3).

Some questions include:

  • Using temporal variation in the networks -- what are the dynamics of network assembly?
  • How do different interaction types affect network stability?
  • How do interactions between food webs (via common species) affect the dynamics of each food webs separately?
  • How do processes at one level affect those in other levels?
  • How do diseases spread in interconnected populations of different hosts?
    • UPDATE** After some discussions today, it seems like there is also a lot of overlap with non-linear time series analysis. It could be possible to find a temporal dataset and do both a multi-layer network analysis and non-linear time series (using rEDM).

Resources: [1]!

Interested Participants

  • Deepa [drao@mit.edu]
  • Basak Taraktas (basak.taraktas@northwestern.edu)
  • Bleu Knight [happylittleaccident AT gmail]

Crook's hyperensembles to improve Maximum Entropy Theory of Ecology (METE) and Max Ent in other non-equilibrium systems

Summary

METE is a promising approach to detecting macroecological patterns like species abundance and size from relatively small amounts of data, but fails to accurately predict interactive phenomena like size-density relationships. The non-equilibrium nature of open ecological systems is a likely constraint for using max ent for ecological applications. These may be better resolved using Crook's hyperensemble method, which is basically taking the maximum entropy of the maximum entropy of the distribution.

This project is simple, can be applied to many different types of 'ecological systems', data is available and abundant, and we should be able to reach a publishable conclusion during the program.

Interested Participants

  • Shanee Stopnitzky entropy@ucsc.edu
  • Marco Pangallo marco.pangallo@maths.ox.ac.uk
  • Alje van Dam aljevdam@gmail.com
  • Tim Kunisky kunisky@cims.nyu.edu
  • Elliot Nelson enelson@pitp.ca
  • Chris Miles cmiless@umich.edu
  • Deepa Rao drao@mit.edu

Cryptocurrency market predictions

Summary

Can we accurately predict cryptocurrency markets using nonlinear time series analysis? Does social network 'popularity' improve predictions?

Interested Participants

  • Shanee Stopnitzky entropy@ucsc.edu


Complex phenomena in cetacean (whale and dolphin) communication

Summary

Click sounds produced by whales and dolphins for echolocation and communication are highly complex and possess a fractal structure. Are there discernible patterns in these sounds that could help us decode how these animals perceive the world, communicate with each other and other species (like us?) This needs more thought, but lots of sound data exists.

Interested Participants

  • Shanee Stopnitzky entropy@ucsc.edu
  • Elaine Bochniewicz emb@mitre.org

Human Movement Dataset

Summary

Dataset looking for a research question. Data is a time series of Linear and angular X, Y, Z of the upper limb of 25 different people completing a series of activities.

  • One possible thought, is there a way to recognize the difference between the way a control (normal) moves versus a stroke survivor?
  • Is there a way to identify the severity of an injury based upon movement patterns?
  • Can specific movement patterns be identified consistently?

Interested Participants

  • Elaine Bochniewicz emb@mitre.org


From Dictatorship to Anarchism (and everything in between)

Keywords

Politics, social networks, agent based modeling, network theory, network analysis

Summary

Study different types of socio-political organizations through network theory and agent based models. Potential questions: - How does different social structures contribute affect the output of different quantities such as production, self-fulfillment, ... ? - How does the system evolve when a leader is taken down? - Does leadership emerge? - ...

If you find this interesting, include your name and email on the list below

Resources

ABM CODES: 

 MASON (Java) -- http://cs.gmu.edu/~eclab/projects/mason/ 

 RNetLogo (R) -- https://cran.r-project.org/web/packages/RNetLogo/index.html 

 Mesa (Python) -- https://pypi.python.org/pypi/Mesa/ 

 Swarm (Java and Objective C )-- http://www.swarm.org/wiki/Swarm_main_page

READINGS:


Interested Participants

Marjan Fadavi (Economist)
Rachel Gicquelais (Epidemiologyst)
Jose Coronado (Economist)
Freya Casier (Economist)
Adrian Soto (Physicist)
Martina Balestra (Management...Scientist?)
Madison Hart (Social Sciences) (madisonkhart(at)gmail.com)
Junfu Zhao (political economy)
You?


Some datasets that might trigger an idea

DataUSA

bunch of USA data (towns, cities, states, education, skills, occupations, industries...)

https://datausa.io

Pantheon

historical database of 'globally known people' with place and date of birth and death (not sure if it's publicly available though..)

http://pantheon.media.mit.edu


Ethnographic Atlas

  • database of 1167 societies and their ethnographic descriptors (see here )
  • Corrected ethnographic atlas: here
  • some links might be dead, so here is reupload of R version: here

Publicly Available Time Series Data

https://datamarket.com/data/list/?q=provider:tsdl


Bipartite Ecological Networks

Summary

Characterizing and constructing bipartite ecological networks (using machine learning or something else?) What are the differences in structure between networks of different interactions? How do nonbiological factors inform network shape (weather, location etc.)? How do they stack into a multiplex network? Can we devise a way to create realistic networks? Given a bipartite network can we determine which type of interaction they are composed of? Can we categorize bipartite networks from other fields (like economics, physics?) as most like one of these ecological networks and then use insights for both field?

Networks, metadata, and references available at web-of-life.es Mutualistic

  • 34 plant-seed disperser
  • 143 plant-pollinator

Antagonistic

  • 4 plant-herbivore
  • 51 host-parasite

You can email me at (kaylasale(at)email.arizona.edu) to chat about ideas, feasibility, whatever!

Interested Participants

  • Alje van Dam aljvdam@gmail.com (thought a bit about country-product, occupation-city networks and their applications)
  • Surendra Hazarie : shazarie@u.rochester.edu
  • Deepa Rao [drao@mit.edu]

Self-organization of cities

keywords

spatial patterns, self-organization, power-laws, traveling salesman

Summary

This project aims to look for patterns in the spatial arrangement of human settlements. Previously it has been shown that the size of US cities follows a power-law distribution, but is this also the case for other countries? If we for example look at the size and spatial arrangement of cities in the Netherlands the pattern seems to be quite different. In this project it would be cool to look at the spatial organization of cities.

Possible research question: - Are there differences between countries in terms of their spatial arrangement (more clustered vs more dispersed?) How does this relate to the traveling distance between cities? Does the traveling distance follow for example a power-law? How does this interact/correlates with the size of cities. - Is the spatial arrangement dependent on the environment? For example if a countries has less favorable places (heterogeneous environment) do we also observe more clustering (adaptive strategy)? How does the interaction between facilitation (more resources if together) versus competition (disease outbreaks etc) influence the size and spatial arrangement of cities (and how is this influenced by the environment)? - Can we observe differences between the time the areas were colonized? (Australia and US as immigrant countries vs european countries?). Are cities in 'older' countries more likely to be dispersed (means of transport at time of settlement influences the distance between cities). Older cities more likely to have 'memory/legacy' effect of competition?

If you are interested let's get together and meet me or Tom or email: valeriereijers@gmail.com.


Delayed Coordinate Mapping and Predicting Epidemics (or other time series)

Summary

The goal of this project will be to learn how to use coordinate mapping and other dynamic nonlinear models for predicting time series data. Many people have had some interesting ideas like (please feel free to add your own it's not here):

- How do predictions from a mechanistic model compare with those from a dynamic model and which is better? - Evaluating the type of disease dynamics these methods could be used on (e.g. seasonal epidemics, persistent infections, sporadic ones, etc). - Can you use these methods to predict disease epidemics at all, and what are their limits?

Reading

This paper is a good place to start our discussion: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169050


Interested Participants

  • Spencer Fox (spncrfx@gmail.com)
  • Bleu Knight (happylittleaccident AT gmail)
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