View source for Complex Systems Summer School 2016-Projects & Working Groups

{{Complex Systems Summer School 2016}}



=Projects=

==Cities as Ecologies of Plans==
===Keywords===
ecology, economics, cities, civil engineering

===Summary===
The idea that the macro economy is an ecology of interacting plans has been gaining force among heterodox, complexity-aware economists. Similarly, cities are increasingly understood as not only having ecologies, but as ecological systems themselves. The project would be to create an agent-based model (likely in NetLogo) that bridges between the literature in economics and civil engineering, envisioning cities as ecologies of interacting plans.

Plans are created by individuals as a kind of dynamic chain of actions and strategies to attain some kind of goal. Goals can be anything: money, love, power, career, altruistic ends. Plans interact with the plans of others: if you want to move into a city, all other things being equal and assuming no vacancies, you need someone else to move out. In that sense, your plan to move in are coordinated with someone else's plans to move out. The plans and goals of people who move in/out of and live in cities directly affect the city as an ecological system. A suggested model: look at how a city's resources, spatial distribution, and other metrics are affected by the plans (dynamic strategies) and goals of its inhabitants. Is a lack of overall coordination (disharmony with the plans/goals of other inhabitants) with negative consequences for other inhabitants reflected in the ecological system of the city?

===Group Contact===
Abigail Devereaux (abigail.devereaux@gmail.com)

===Interested Participants===
* Ben Zhu (b.zhu@tudelft.nl) (I am from the field of Industrial Ecology, using biological metaphors to solve social/industrial sustainable problems)(I am interested in applying universal Darwinian thinking on economy) <br>
* [[Usama Bilal | Usama]] <ubilal@jhmi.edu>

==Inferring the Structure of a Network==
===Keywords===
critical infrastructure, information theory, network theory, agent-based model

===Summary===
Deregulation of the US power grid has opened the door to speculators who trade futures contracts that are based on the minute-by-minute price difference for a megawatt hour between two generators (think nodes). The generators are connected by transmission lines (think edges) that must, in general, follow Kirchhoff's laws of current and potential. We want to explore what information exists in these location based prices regarding the structure of the underlying network of generators, consumers, and transmission lines. Public historical data on the price at each generator and the power both forecasted (24 hours ahead) and realized at each generator exists for certain regions of the US and I have built a small database of 2 years worth of this data for roughly 15 zones of the New York interconnect. An interesting side note, the power forecasted and demanded at one particular node tends to be periodic and easy to forecast. The price (which theoretically is driven by demand) at that same node exhibits multifractal properties, apparent heavy-tails in the increments, and long memory. The process that converts bids, offers, and the physical constraints of Kirchhoff's laws is usually a linear program that  minimizes cost across all generators that can contribute to the demand.

===Group Contact===
James Thompson (j7sthompson@gmail.com)

===Interested Participants===
* Catriona Sissons c.sissons@auckland.ac.nz
* Daniel Biro (daniel.biro@med.einstein.yu.edu)

==Acquiring Information: to learn an existing language or to develop your own?==
===Keywords===
TBD...

===Summary===
In situations where a problem's solution is presented in an unrecognizable language, we are interested in the trade-offs between learning that language and finding a solution in your own language. We intend to study several forms of language and information; the project could take many possible directions of widely varied scope:
*Linear algebra: is it "better" to diagonalize a matrix or to represent eigenvectors in a non-eigen basis?
*Computational mechanics: is it easier to learn/synchronize to an existing (perhaps non-optimal) model or to develop your own e-machine?
*
*<i>(motivation)</i> Indigenous lore: Is it better to learn about an ecological system by listening to the lore of a native population or by strictly adhering to western scientific methods?

===Group Contact===
Jeffrey Emenheiser (jemenheiser@ucdavis.edu)

===Interested Participants===
* Santiago
* Jesús Arroyo (I'm interested specially in the first question about eigenbasis)
* Moriah 

==Resource Curse: paradox of plenty==
===Keywords===
resource curse, agent-based models, economics, competition, self-similarity
===Summary===
Economist have noted that an abundance of natural resources (usually non-renewables) is apparently linked to worse economic indicators, an absence of democracy, and a number of other negative effects regarding the general health of the nation or region. A few of us have discussed exploring the dynamics of this alleged curse using the tools of complexity science. One possible question to explore is does the resource curse exhibit self-similarity at smaller scales of time, resource quantity, and competitors for the resource? Anecdotally one could think of this as the difference between say a mineral rich African nation and a Walmart store on Black Friday. Both have finite resources and competitors attempting to reap the resource while it exists. And both have been known to invoke violent responses. 

We are open to exploring any other ideas related to the resource curse. 

===Group Contact===

James Thompson (j7sthompson@gmail.com)

===Interested Participants===
Catriona Sissons (c.sissons@auckland.ac.nz)
Daniel Biro (daniel.biro@med.einstein.yu.edu)

== Emergence of symmetry==

I'm super fascinated by the question of why there is so much symmetry in nature?  For example, in the biological system, what are the evolutionary models that can explain such high percentage of species which exhibit certain type of (approximate) morphological symmetry? What can we learn from this mechanism? 

Inspired by different examples in nature, we can try to invent network models that exhibit emergence of certain symmetry and structure formation. We can also  analysis the chaotic behavior and entropy flow in the model we invented, and might discover some new phenomena of emergence!

All ideas and discussions are welcome!

===Group Contact=== 
Lin-Qing  (lchen@ptip.ca)

=== Interested participants, please sign up below ===

[[ Marcus Nordström ]] manordst@gmail.com

== Language evolution in online fanfiction communities ==
=== Summary ===
I have a dataset containing fanfictions in the top ~40 most popular fandoms from Archive of Our Own(archiveofourown.org/media). The data includes:

(1) Fanfiction texts (original and moderately preprocessed), within a time range between 2010-now (since AO3 was founded).

(2) ~ 25 metadata fields, some of which include: Kudos, comments, bookmarks, publish date, tags, rating, categories, etc.

(3) The texts represented as topic distribution by buiding LDA models (ongoing, will be completed soon).


My current work with it focus on studying the influence of readers' preference on the writings being produced. Meanwhile, I will be happy to share the data and looking for other ideas/questions that may be associated with it.


===Group Contact=== 
[[ Yizhi (Elise) Jing ]] (jingy@indiana.edu)


=== Interested participants, please sign up below ===

== Movie citation network ==
=== Summary ===
IMDB has an open access dataset at: http://www.imdb.com/interfaces. Our project is based on the dataset and we have current several potential research questions:

1) The sleeping beauty of movies. Based on the "shoutouts" of one movie to another,  a "citation" network exists that connects millions of movies. The "sleeping beauties" had been identified in scientific paper citation networks, which are papers that became frequently cited a long time after publication. We are interested in finding similar (or different) patterns in the movie citation network.

2) If atypical combination of movie properties/citations/genres could predict its popularity.

3) A more general idea is the gender issue in movie industry, such as the different network structures of actor and actress.


We welcome any feedback! 

===Group Contact=== 
[[ Yizhi (Elise) Jing ]] (jingy@indiana.edu)

[[ Lu Liu ]]

=== Interested participants, please sign up below ===
Donovan Platt (donovan.platt@students.wits.ac.za)

==Emergence of power structures in networks [overlap with project on prestige goods]==
===Summary===

High-level questions: How do (social) power structures emerge in complex networks? How do hierarchies restrict/modify information flow through networks? How does structural power (e.g., due to network hierarchy) differ/relate to intrinsic power (e.g. some nodes/people have more "skill/worth" regardless of their network position).

One idea I have is looking at the interplay of these dynamics by simulating (social learning) games on networks; for example, games where agents have to share information to estimate the worth of external items (like stocks, prestige goods etc.).
Other ideas would involve analysis of how information flow is affected by hierarchy etc.

===Group Contact=== 
Lula Chen (nchen3@illinois.edu)<br> 
Will Hamilton (wleif@stanford.edu; wrote this is description based on chat with Lula; not my area of research)

=== Interested participants, please sign up below and join the Slack channel [sfi-csss-2016.slack.com/messages/power, email Will if not yet on Slack] === 
[NOTE: this is semi-duplicate with prestige goods project below; I just wanted a separate space to gauge interest in more general power dynamics questions]

Jeffrey Emenheiser (jemenheiser@ucdavis.edu)<br>
Santiago Guisasola (sguisaso@uci.edu)<br>
Ellen Badgley (flyingrat42@gmail.com) - as you said this overlaps with the prestige goods project, and I think we can do some interesting network models that address both! [Will: Indeed :)]<br>
Joris Broere j.j.broere@uu.nl<br>
Crosato Emanuele (crosato.emanuele@gmail.com) - I can use information theory to investigate directional dependencies, might be useful? <br>
William Leibzon (william@leibzon.org) - partially my area of research. question is what you mean by "power" - I'm not on slack, pls send email<br>

=== Meetings ===
<br>
We're meeting in the coffee shop at 6:30, June 15th.

== A genetic model of music evolution==

===Keywords===
mutation, selection, recombination, cultural evolution, market ecology, music

===Summary===
We would like to build a genetic inspired model to describe the mechanism of music evolution in history.  Basic idea is that different elements of characters in music is analogous to genes. Mutation creates new music character and language material, hence it is the source of musical diversity (genetic diversity). Migaration and cultural exchange propagate a music style to different community. Recombination allows different elements to form certain combinations. Natural selection, which is influenced by audiences' musical culture background, aesthetic values and market ecology. Selection chooses certain combination over others and when the number of differences reaches a critical point, it can be identified as a new style. 

All ideas and discussions are welcome!

===Interesting addition and comments to the idea===


===Group Contact===
Lin-Qing Chen (lchen@pitp.ca) ;
Chenling Xu (chenlingantelope@gmail.com)

===Interested Participants===
*Jesus Mario Serna (very interesting!)
* Jesús Arroyo
* Santiago Guisasola
* Devrim ikizler (devrimikizler@gmail.com)
* Donovan Platt (donovan.platt@students.wits.ac.za)
* Marcus Nordström (manordst@gmail.com)

===Meeting time===
1) June 15,  8:15~9:00 pm in the first floor coffee shop.

== Quantifying Trust==
===Keywords===
Agent Based Modeling, Information Theory, Game Theory
===Summary===

To me there are some simple rules of trust:

# The more we trust someone the less questions we are asking about their activity
# By trusting someone, a person reduces the amount of information he needs to process. e.g. If a pedestrian does not have a clear view of the road then he might follow other pedestrians in front of him. By following them he is reducing the amount of information he needs to process.
# If there is a "perfect trust" in a social system, then the system seems to be more ordered. For example in a military system a soldier blindly obeys the order of its officers. This makes the system more ordered.
# On the other hand  the less there is trust the more disorder in the social system. So quantifying trust could be a way to quantify the disorderedness or entropy of a social system.
# But there is a catch. It is easier to propagate a misinformation in a system where everyone blindly trusts each other.

My intuition is that we can use both information theory and game theory to quantify trust this way. The problem is I don't have a dataset yet that can validate the rules above. But we can use the above observations  to formulate an agent based model where we can tune the parameters to see if we can get any interesting observation.

===Interesting insights to the idea===

Please add your comments and idea about the project here.

Are there shortcuts to building trust?  For example, in the area of financial instruments, can the use of credit ratings substitute for more detailed due diligence?  What are the implications of such short cuts?  [Nai Seng]

===Group Contact===
Syed Arefinul Haque, haque.s@husky.neu.edu

===Interested Participants===
Donovan Platt (donovan.platt@students.wits.ac.za)

Devrim Ikizler (devrimikizler@gmail.com)

[[Frank Marrs]]

Nai Seng Wong (fsgwns@gmail.com)

[[Mika Straka]]

Lu Liu (luliu@psu.edu)

Xiongrui (John) Xu (xuxr007@gmail.com)

Ellen Badgley (flyingrat42@gmail.com) - I'd be interested in following along, and FYI this might be a research area that MITRE is interested in too.

Marcus Nordström (manordst@gmail.com)

===Meetings===

We have created a public group #trust in slack for the online conversation. We will also post the face-to-face meeting schedules below:

Wednesday 7:30, June 15, 2016

We are meeting in front of the coffee shop at 7:30 for a general discussion

== Modeling cultural evolution through 150 years of language change ==
===Keywords===
cultural evolution, language, multilingual 
===Summary===

I have a clean dataset of word evolution for 3 languages (English, French, German); this data is built from historical text (books mostly) and includes data for all words that are reasonably frequent/popular in that decade (more than 10000 words per decade). For every decade from 1850-2000 I have:
# word frequency/popularity
# historical "word vectors" (capture word meaning/semantics)
# word sentiment scores (whether a word is has a positive or negative connotation; English only)
# word part of speech tags (e.g., is a word used more as a noun or a verb in a given year). 

I also know of a bunch of other datasets on language change etc. that might be useful, depending on the groups interest. 

I am totally willing to give all this data away freely (even if I am not actively involved in the project). 
The project website with my data is: nlp.stanford.edu/projects/histwords/ 

Also, this is closely related to my thesis, so I would be relatively hands off, but I know of lots of data, references, etc. that can get everyone going. 
I work on more of the observational/data-driven side of things, though, and it sounds like there is a lot of interest in building more of a cognitive and/or ABM model that explains/predicts the patterns we see in real data (ambitious goal). 
I think that is an awesome idea!

===Group Contact===
Will Hamilton (wleif@stanford.edu)

=== Interested participants, please sign up below and join the Slack channel [sfi-csss-2016.slack.com/messages/language-evolution, email Will if not yet on Slack]  ===
[[Matteo Morini]] I've always wanted to work on evolutionary linguistics. You have the (coolest) dataset; now we need to devise a model. Perhaps an ABM (w/geographical, metaphorical, network-based space) where people settle, meet, migrate, adjust to new words/morphemes. The (ambitious) goal would be to trim the model as to mimic the empirical data. Sounds feasible? Btw, we will need to figure out (a.k.a. fake ^_^) a way to deal with spatializing the non-geographically referenced dataset. Who's in?
[[Juste Raimbault]] @Matteo would be totally interested in tackling this question of generating synthetic data (hybrid data) of spatial distribution for populations at given time periods (but already involved in two projects, so maybe ok if it is not intricated into the ABM and just the parametrization step) ; have at least historical data for French cities 1830-2000.


Anjali Tarun (anjalibtarun@gmail.com) I've encountered this paper (http://science.sciencemag.org/content/331/6014/176.long) that quantitatively analyzes culture using millions of digitized books. It would be interesting to see how language affect and distinguish each one's culture. [Will: The data on my web page is actually based on an updated version of the data in that paper.]

[[Danilo Liuzzi]]

Harrison Smith

Sina tafazoli (tafazoli@princeton.edu)

[[Ben Zhu]]

Hamza GIaffar

Lu Liu (luliu@psu.edu)

Elise Jing (jingy@indiana.edu) I will be very interested in looking at the evolution of meaning of words as embedded in their contexts (e.g. using an word2vec model).

Catriona Sissons (c.sissons@auckland.ac.nz)

== Semantic word game ==
===Keywords===
cognitive science, language, games, network routing, free association 

===Summary===
See http://pybossa.socientize.eu/pybossa/app/Semantics/ to play the game. The goal is, given a starting word, navigate through forced choices to a known target word. The global idea is to understand the effect of context and how individuals can manipulate semantic space online (like we constantly have to do in natural conversation. A project idea is to construct random models where we can see influence of the target word. For example, if we have directed search from the start node to the end node as well as a directed search path from goal to start, can we combine these paths to capture human performance.

Data is available for download at https://pybossa.socientize.eu/pybossa/app/Semantics/tasks/export and looks like (JSON)
{"info": "ILLUSION~FANTASY~16~FANTASY~ISLAND~6~ISLAND~OCEAN~4","user_id": 1732, "task_id": 13792, "created":"2014-04-01T11:26:06.479946", "finish_time":
"2014-04-01T11:26:06.479966", "calibration": null, "app_id": 430,"user_ip": null, "timeout": null, "id": 3211605},
This user went from illusion to fantasy (16 seconds), then from fantasy to island (6 seconds) and so on. 

Research question is still a bit unclear but a while back there was more than 10478 games. Additionally something similar has been done using wikipedia http://snap.stanford.edu/data/wikispeedia.html

===Group Contact===
Nicole Beckage nbeckage [at] gmail.com

=== Interested participants, please sign up below ===
* [[Frank Marrs]]
* [[Mika Straka]]
* [[Lu Liu]]
* [[Jacob Hunter]]

== Cooperative game: Modeling communication and innovation ==
===Keywords===
agent based models, network science, cooperation, iterative games

===Summary===
Data available.
Game design:Each player is tasked with building a 'team' of 5-6 characters out of a league of 40-60 players. The team receives a numeric (deterministic) score. This is played iteratively for a fixed (known) set of rounds. Cooperation comes in because at any time each player can see the teams and scores of all other players and may copy, select characters from or otherwise utilize all other players teams. The goal is to maximize the score of the group.
Group size varies from 1 to 9 people in a session. Repeated games are played between the same team and difficult varies systematically within a full set of games.

Research question is still not nailed down but some ideas
model the communication structure  (in terms of copying other peoples choices)  in a network and look if certain structural elements are correlated with improved performance
model learning and improvement in terms of sharing information over iterative runs and across group size
look at instances of sweeping change as innovation and study propagation of this event through the group.

===Group Contact===
Nicole Beckage nbeckage [at] gmail.com

=== Interested participants, please sign up below ===
* Mika Straka
* Joris Broere, j.j.broere@uu.nl
* Jesús Arroyo jarroyor@umich.edu

== Effect of observer on their environment ==
=== Keywords ===
uncertainty, observation
===Summary===
I'm interested in looking at how an observer affects their environment or the system they are studying.  I've talked to a few people about this, but the idea may need more refinement to make a good project.

===Other Ideas===
If anyone has more specific ideas, please add them here.

JE: My perspective on this is through information theory: information transfer from self-past to self-future, conditioned on self-present. or something. This would require that information to be transferred through the environment. This perspective does not require the "self" to be an "observer" per se, but I expect they are intimately related.

===Group Contact===
Jacob Hunter, jacob.hunter@pnnl.gov

===Interested===
*Lula Chen (nchen3@illinois.edu)
*Jeffrey Emenheiser (jemenheiser@ucdavis.edu)
*Marcus Nordström (manordst@gmail.com)

== Temporal social networks (and dialogues) in film ==
===Keywords===
movies, films, social network analysis, pop culture
===Summary===

I have (or know of) three cool datasets for analyzing social networks and interactions of characters in films (i.e., the "social" networks of the characters in the movies, based on who appears with and talks with who). 
* Dataset 1: pre-processed dialogues from around 300 movies + interesting IMBD metadata (this dataset is public; www.mpi-sws.org/~cristian/Cornell_Movie-Dialogs_Corpus.html). 
* Dataset 2: detailed information on the temporal scene structure (who is on screen when + scene breaks etc.) for thousands of movies + metadata (e.g., rotten tomatoes scores); this data is more private/sensitive and is currently somewhat messy, but we can definitely use it for this project. It should be easy to extract dynamic networks from which characters are on screen together. 
* Dataset 3: high-level static (i.e., non-temporal) network structures for character interactions in 700 movies (this dataset is public; www.moviegalaxies.com; not totally clear how the data was constructed).

I think this data could give some really interesting insights into the art of storytelling and motifs/archetypes that re-occur. 

This is only tangentially related to my usual PhD work (I just happen to know of the data), so any high-level thoughts on what would be interesting to do with this data are welcome! (Feel free to edit/add things here). A couple things I thought of:
* How do structures of movie social networks predict genre, rating, box office success, country of production, etc.
* (Temporal) network motifs: what are the common patterns of different types of films? 
* Movie social networks vs. real life social networks.

===Group Contact===
Will Hamilton (wleif@stanford.edu)

=== Interested participants, please sign up below and join the Slack channel [sfi-csss-2016.slack.com/messages/movie-networks, email Will if not yet on Slack] ===

* Juste Raimbault (juste.raimbault@polytechnique.edu) [definitively in, but not as main project. Can propose analyses such as NLP on dialogues or spatial analysis if there is spatial metadata.]
* Jesús Arroyo (jarroyor@umich.edu)
* Andy Mellor (mmasm@leeds.ac.uk) - Very interesting project but perhaps a little to close to my PhD work. Definitely interested in the tempora network aspect/temporal motifs.
* Michael Schaub (michael.schaub@uclouvain.be)
* Marius Somveille (marius.somveille@zoo.ox.ac.uk)
* Moriah Echlin (echlinm@uw.edu)
* Nicole Beckage (nbeckage@gmail.com)
* Elise Jing (jingy@indiana.edu)
* Thomas Zhang(tomzhang@umich.edu) (Would very happy to join the discussions and contribute ideas. Probably would not do this as my main project here at SFI due to similarity to my own research.)
* [[Lu Liu]]
Catriona Sissons (c.sissons@auckland.ac.nz)

==Emergence of project groups in CSSS 2016==
===Keywords===
subgroups, team participation, satisficing, networks

===Summary===
We want to model team emergence in cross-disciplinary academic research.  
 - Is diversity important? 
 - What makes teams productive? What makes teams fun?
 - How do teams form information networks?
 - How do members satisfice?
 - How do members select their teams and how does that align with success of the group?
 - What are the conditions for maverick rigor?

Merged from another project on same topic
field study of group formation in Summer Schools
we can use data on sex, nationality, profession and bio sketches
data on previous summer schools could be available as well

There is a nice paper about collective intelligence http://science.sciencemag.org/content/330/6004/686 
which can also be useful

We checked with Juniper and she can provice data for sex,nationality, science interest, but probably only going back to 2011

===Group Contact===
Ross Buhrdorf rbuhrdorf@gmail.com

===Interested===
Jacob Hunter, jacob.hunter@pnnl.gov

Julia Adams

[[Justin Williams]]

Will Lee, wilee@vt.edu or wlee@mitre.org

Andy Mellor (mmasm@leeds.ac.uk)

[[Ulya Bayram]] (ulyabayram@gmail.com) This could be an interesting side project for me, I'm definitely interested! And I think this project can be merged with the one up above I think.

Anjali Tarun (anjalibtarun@gmail.com)

Dmitry Alexeev (exappeal@gmail.com)

Devrim Ikizler (devrimikizler@gmail.com)

[[ Mark McCann ]] Mark.McCann@Glasgow.ac.uk

[[Fabio Correa]] <facorread@gmail.com>

[[Rana Azghandi]] <rana.azghandi@gmail.com>

[[William Leibzon]] <william@leibzon.org> - similar to others I have interest in this area but don't see as being a main project

===Resources===
An interesting lecture on diversity and similarity from an agent based modeling perspective: [https://ocw.tudelft.nl/course-lectures/diversity-self-similarity/?course_id=14068]

== NY subway microbiome ==
===Keywords===
networks, ecology, subway , city , communities 
===Summary===
I am working with international consortia - Metasub
one of the projects is well known NY subway map of microbes 
http://d2zahwnsqpmout.cloudfront.net/map/

Chris Mason from Cornell is willing to share the data they have collected in different stations and different parts of the stations

Microbial communities reflect history of the stations as well as people migration - interesting would be to create a model for microbe spread along the subway and exchange

Other data on NY could also be applied

Original paper from Cell Systems
http://www.cell.com/pb/assets/raw/journals/research/cell-systems/do-not-delete/CELS1_FINAL.pdf

===Group Contact===
Dmitry Alexeev (exappeal@gmail.com)
=== Interested participants, please sign up below ===
*Daniel Biro (daniel.biro@med.einstein.yu.edu)
*Michael Schaub (michael.schaub@uclouvain.be)
*Ruichen Sun(r5sun at ucsd dot edu)
*Nicole Beckage (nbeckage@gmail.com)
*Chris Revell (cr395@cam.ac.uk)
* Emanuele Crostao (crosato.emanuele@gmail.com)

==Genome of Law==
===Summary===

Off-the-cuff discussion about how a given set of laws/statutes/legal regulations might have similarities to genetic regulatory networks. More of an exploratory look, but might have some compelling things to be found. James has study from a colleague at MITRE looking at networks of laws and how they reference each other, and there are similar toe-dipping studies, but nothing rigorous. 

===Group Contact===

Temporary: JP (JP@santafe.edu) <br>


===Interested Participants===
Catriona <br>
James Thompson <br>
Moriah  <br>
Charlotte <br>

==Network and language dynamics of Reddit (Correlations between cultural and structural properties of communities))==
===Summary===

'''If someone (not Will) wants to lead/organize this that would be great! I would love to provide assistance, but it is very related to my PhD, so I want to try to branch out a bit :)'''

I have a cleaned dataset of all Reddit (www.reddit.com) comments from 2009 through 2014 (Reddit is a very popular social media forum with >30 million users; it is organized into thousands of user-moderated "subreddits", which are topically-focused communities).
The data includes all the text of the comments, usernames, the upvotes, and the thread structures (so networks can be constructed). 
There are many interesting questions that could be investigated with the data, and I would love to hear ideas! (The data is totally public, and I am willing to share even if I don't work on the project).<br/>

I have done a lot of pre-processing and preliminary analysis on a manageable and very clean subset of all 2014 comments for 1200 mid-to-large subreddits (about 50Gb).
My background is in natural language processing and network mining, and I have lots of preliminary analysis/machinery for measuring linguistic signals in the data.

One high-level idea I have is to look at the relationship between sociolinguistic/cultural features of a community and the dynamic network interaction structures (e.g., clustering of comment-reply networks). 
I have a ton of sociolinguistic variables that could be used, e.g. how "polite" the language is or how much people use "we" vs "I".

Another cool project would be looking at conflict and/or anti-social behavior (again, I have some linguistic features that I think could be helpful for this).

===Group Contact [Another organizer needed]===
Will Hamilton (wleif@stanford.edu)<br>

=== Interested participants, please sign up below and join the Slack channel sfi-csss-2016.slack.com/messages/reddit/ ===
* Dima Alexeev (exappeal@gmail.com)
* [[Matteo Morini]] Cool! We need to discuss this too around a couple of beers.
* [[Danilo Liuzzi]]
* Harrison Smith
* Nicole Beckage (nbeckage@gmail.com)
* William Leibzon (william@leibzon.org) - no gurantee on participation yet but I want to come to first meetings. also I've been to reddit datathon, and so have done some analysis of it before. this is probably not same data.

== The assembly of plant-pollinator networks==
===Keywords===
networks, mutualisms, ecology, restoration, succession, assembly 
===Summary===
I have a 10 year dataset of ~1500 observations of pollinators (bees, flies, butterflies, wasps etc.) visiting plants in a native plant restorations (hedgerows)  in the Central Valley of CA. The assembling communities are paired with unrestored field margins (controls) and mature (non-assembling) hedgerows. The goal would be to examine how and why the structure of the network is changing through time. How are the individual species changing their interaction patterns? What does this mean for the topology/resilience of the network? There is also a spatial dimension (the meta-population dynamics of the networks?) that could be explored. 
 
For more information on the dataset, please see https://nature.berkeley.edu/~lponisio/wp-content/uploads/2014/12/ponisio-2016-704.pdf

===Group Contact===
Lauren Ponisio (lponisio@gmail.com)
=== Interested participants, please sign up below ===
Ryan McGee (ryansmcgee@gmail.com) <br>
Daniel Biro (daniel.biro@med.einstein.yu.edu) <br>
Dima Alexeev exappeal@gmail.com <br>
Lindsay Todman (Lindsay.todman@rothamsted.ac.uk)<br>
Chris Revell (cr395@cam.ac.uk)<br>
Julia Adams (jadams@wellesley.edu)<br>
Marilia P. Gaiarsa (gaiarsa.mp@gmail.com)<br>
Asher Mullokandov (asher.mullokandov@gmail.com<br>
Harrison Smith (hbs@asu.edu)<br>
Christine Moore (christine.moore@ouce.ox.ac.uk)<br>
Evelyn Strombom (evelyn.strombom@gmail.com)<br>
Michael Schaub (michael.schaub@uclouvain.be) (I am interested, but I feel there might be too many people already..)<br>
[[Frank Marrs]] <br>
Anjali Tarun <br>
Nicole Beckage (nbeckage@gmail.com, I am interested in hearing about the project and method ideas but would want to be more in the background and slightly less involved)<br>
Catriona Sissons (c.sissons@auckland.ac.nz) (Interested in hearing more about the project - sounds really cool - but as Michael says looks like there are already too many people unless it ends up becoming multiple related projects)

==Modeling prestige good economies/power structures==
===Summary===
Prestige goods, in a nutshell, are goods whose value is in conveying social status/ranking, and there is a great deal of speculation about how they contribute to increasing social complexity. It would be interesting to explore these dynamics via an ABM.<br>
Since prestige goods indicate power (in the form of social status/ranking, or however else we may define power), this project will also explore modeling power in social groups.<br>
I know some folks at SFI have been working on the dynamics of prestige/power, so we should ask Juniper for contacts!  <br>
A couple of ideas:<br>
* Kantner from the Santa Fe School of Advanced Studies has an interesting game-theory model on how people decide to invest in prestige goods.  He includes some data on turquoise in archeological contexts in the Chacoan culture (http://www.jstor.org/stable/27820883).  It would be interesting to a) implement this as an agent-based model, and b) explore some additional dynamics.<br>
* Aimee Plourde has a model of prestige goods as costly signals.
* There's the interesting question of how a prestige economy responds when the prestige goods all come from outside and the supply dries up.  (Case study would be Japan's Yayoi period - the main prestige goods were Chinese-produced bronze mirrors and swords, but when the Han dynasty started going downhill and no longer giving the good stuff to their tributary states, we see some interesting dynamics.  All I have is a high-level overview of this right now (no archeological data) and there were multiple other factors involved, so it's really messy, but might be interesting to talk about at least.)  <br>

===Group Contact===
Ellen Badgley (flyingrat42@gmail.com)<br>
Lula Chen (nchen3@illinois.edu)<br>

=== Interested participants, please sign up below ===
*[[User:Carrignons|Simon Carrignon]]: I wrote already [http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7408550&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7408550| a general ABM] to study that kind of things, but would be really happy to start from scratch something new in python or whatever!
** Awesome! I started reading your paper and it looks like a great starting point - maybe we could extend the model to distinguish between common goods (subsistence-level, go away after each season) and permanent prestige goods that convey social value, as well as looking into the implementations of prestige on agent actions.  If you would like to continue working on this for CSSS let's talk. - Ellen
Donovan Platt (donovan.platt@students.wits.ac.za)<br>
Will Hamilton (wleif@stanford.edu)<br>
Ben Zhu (b.zhu@tudelft.nl)<br>
Joris Broere (j.j.broere@uu.nl)<br>
William Leibzon (william@leibzon.org> - mostly observer/lurk mode<br>

=== Group Organization ===
We have a Slack channel on https://sfi-csss-2016.slack.com.  If you need an invite to the Slack team, email flyingrat42@gmail.com.


=== Meetings ===
<br>
We're meeting in the coffee shop at 6:30, June 15th.

== Early Warning Signals in a Disease Network==
===Keywords===
networks, disease, regime shifts

===Summary===
In 2011 a major avian influence outbreak occurred in the domestic ostrich industry in the western cape of South Africa. This outbreak resulted in massive losses to the industry, and the eradiation of a significant number of farms. I have published papers looking at how the movement of birds may have caused the network to become more vulnerable over time (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0086973), and also looked at how the system self organized following the crash (http://onlinelibrary.wiley.com/store/10.1111/1365-2664.12486/asset/jpe12486.pdf?v=1&t=ipgcjacz&s=b6f493d011a1e9cafa5849eadd8cb8d59be7b055). 

I would like to investigate whether there were early warning signals of a regime shift. This work could relate to anticipating critical transitions, and looking to see whether this could present an empirical example of the presence of early warning signals. 

I currently have data from 2005-2014 but could easily obtain data until 2016. I am really open to ideas of how to work with this data. 

===Interesting addition and comments to the idea===

Please add your comments and idea about the project here.

===Group Contact===
Christine Moore (christine.moore@ouce.ox.ac.uk)

===Interested Participants===
* Jeffrey Emenheiser (jemenheiser@ucdavis.edu)
* [[Frank Marrs]]
* [[Usama Bilal]] <ubilal@jhmi.edu> . This sounds very interesting! My experience is restricted to humans, but many of the things that interest me include regime shifts, so I think I'll enjoy studying regime shifts in a different organism
* Jesús Arroyo (jarroyor@umich.edu)
* Thomas Zhang (tomzhang@umich.edu)
* Marilia Gaiarsa (gaiarsa.mp@gmail.com)
* Charlotte James (charlotte.james@bristol.ac.uk)


=== Meetings ===
We're meeting over breakfast at 8:15 on June 16th.

==Tracking the migrations of urban hipsters (aka spatiotemporal analysis and scaling of labor)==
===Summary===
This is linked to the "Viscosity of Labor" question on the MITRE Challenge Questions list, which is copied below:<br>
<b>Given US Census collected data can we find relationships between labor and urban scale?</b><br>

"Using US Census data (and other sources) a number of interesting scaling laws have been discovered that relate to the dynamics of urban human social systems. These scaling laws relate to such things as the generation of intellectual property, income, tax revenue, crime, and so on. What about labor? Does the scaling seen in income come from new or shifting categories of labor or simply increasing the income within an existing (static?) distribution of labor categories? Is there a spatial component?  Does the spatial distribution change with the scale of the urban area?"<br>
<br>
The above is the complete question from MITRE, but we can adjust/focus as needed depending on interest and ideas.<br>
<br>
There is plenty of US Census data available for this:
* BLS Standard Occupational Classification categories (http://www.bls.gov/soc/home.htm) - these are the 23 high-level categories used to classify occupations in Census datasets - first widespread rollout was in 2000, and the categories were revised in 2010 (but the high-level categories are straightforward to reconcile)
* 2000/2010 US Decennial Census data (http://www.census.gov/2010census/)
* American Community Survey (ACS) data (http://www.census.gov/programs-surveys/acs/) - 5-year estimates are probably most suitable
We would probably start at the county level and go finer (census tract/block group) if time allows.<br>
MITRE and SFI are actively working together to improve the 2030 Census, so we can reach out to SFI directly on this.

<br>
===Group Contact===
Ellen Badgley (ebadgley@mitre.org, flyingrat42@gmail.com)<br>
<br>
=== Interested participants, please sign up below ===
* Lorraine Sugar (lorraine.sugar@gmail.com) 
* Devrim Ikizler (devrimikizler@gmail.com)
* [[Usama Bilal]] (ubilal@jhmi.edu) Really interested in exploring this kind of data. I once cleaned data on some occupations by census tract from 1930 onwards (for Baltimore city, but can be reproducible). I also have a couple R scripts lying around to extract stuff from ACS easily. 

=== Group Organization ===
We have a Slack channel on https://sfi-csss-2016.slack.com.  If you need an invite to the Slack team, email flyingrat42@gmail.com.

== A preliminary model of the coupled human-natural system of swidden agriculture ==

=== Summary ===

Swidden agriculture, also known as [https://en.wikipedia.org/wiki/Slash-and-burn Slash-and-burn] is about as old as agriculture itself. It exists in diverse variants practiced by 200 to 500 million people in different regions of the world; all of these forms involve the slashing and burning of portions of forest, hence its name. There has been a historic controversy regarding swidden agriculture, with some publications presenting it as a destructive force that contributes to global deforestation and other publications highlighting its sustainability and ecological benefits when practiced as a means of subsistence. This controversy shows the need of tools to further research and assess the benefits and costs of swidden agriculture.

This project idea will address the following research question: How do human activities interact with the ecological landscape and the sustainability of swidden agriculture?

This project idea aims to produce a simple, preliminary model with the following components:

* A simplified social network of swidden farmers exchanging agricultural labor, inspired by [https://scholar.google.com/scholar?cluster=12140541436579423216&hl=en&as_sdt=0,32 Downey (2010)]
* A landscape in the form of a 2-D grid in which cells are patches of forest / crops

Possible model outputs include:

* Yearly harvest
* Biodiversity
* Yearly biomass production in forest and fallows
* A representative indicator of the "net production" of the complete system
* Sustainability of the complete system

Biodiversity, biomass production, and sustainability will need reasonable, simple definitions that follow modern literature on the subject.

=== Software and language ===

Every idea is worth discussing. NetLogo might be an option that fits with our limited calendar. Git and GitLab would be used to manage the source code.

=== Group contact ===

[[Fabio Correa]] <facorread@gmail.com>

=== Interested participants, please sign up below ===

* Julia Adams
* Ellen Badgley (I will lurk)
* Lindsay Todman (Lindsay.todman@rothamsted.ac.uk)
* Chris Revell (cr395@cam.ac.uk)
*Christine Moore (christine.moore@ouce.ox.ac.uk)
* Evelyn Strombom (evelyn.strombom@gmail.com)
* Santiago Guisasola (sguisaso@uci.edu)

== Modeling within-city residential migration ==

===Keywords===
residential mobility, neighborhood change, network analysis

=== Summary ===

I have a few datasets laying around that I cleaned for my thesis (and I’m not using for that purpose) and a few more that I obtained but never cleaned. All of them are data for the city of Madrid (Spain) on various geographical and time scales/resolutions, most of them related to social/environmental/demographic variables. This includes socioeconomic, demographic and other type of data at the smallest unit available (~1500 people). I also have geocoded data on all housing units in the city (including size and year of construction) and geocoded data on all commercial properties (including open/closed and licensed use). Most of this data I have from 2005 to 2015 with yearly updates. The commercial spaces area is available from 2012 to 2016 with monthly updates from 2015 onwards. I also have data at other levels (bigger areas) related to occupation and other socioeconomic variables. <br> <br>

'''The new thing I'm proposing is using a dataset I'm not using for my thesis which has information on yearly residential migration patterns within the city of Madrid. This includes information about how many people have moved from each neighborhood to any other neighborhood by level of education. If we consider neighborhoods as nodes and people moving as weighted edges, I have the feeling we can use the tools of network analysis to see which neighborhoods are the most unstable/changing (i.e.: neighborhoods that are in certain network structures [motifs], neighborhoods that are more central, etc.). This can be then easily coupled with data from the other sources described above or below.''' This data is available from 2004 to 2014 with yearly updates. <br> <br>
See an example of how this network would look here:
[[File:Network_ub_cities.png|200px|thumb|center]]

There are a few other I have not used yet (and don't plan to use for my thesis). All of them come from datos.madrid.es (let me know if you need help figuring out what's what!) <br>
'''Traffic''': dataset of traffic intensity in Madrid. This uses sensors placed in most traffic lights (~3600), and the dataset provides count of vehicles (and associated % capacity used) every 15 minutes, from 2013 to March 2016. Some of the data is messy (but they have a flag for unreliable data).<br>
'''Noise''': dataset of noise measured with ~30 static sensors, monthly averages, from 2000 onwards.<br> 
'''Polution''': dataset of air pollution  measured with ~24 static sensors, hourly averages, from 2001 onwards.<br> 
'''Complaints''': dataset of complaints (such as not working traffic light, litter on the street, etc), from 2014-2016, geocoded and with precise date/time.<br> 
'''Traffic infractions''': dataset of infractions 2014-2016, with address (geocoded in 2016) and with precise date/time.<br>

===Interesting addition and comments to the idea===

Please add your comments and idea about the project here!

=== Group Contact ===
[[Usama Bilal]] <ubilal@jhmi.edu>

=== Interested please sign up below ===

* Scott Armstrong
* Lorraine Sugar - also thinking about this more!
* Marius Somveille
* Jesús Arroyo
* Michael Schaub
* Ellen Badgley - would be interested in following along

=== Meetings ===
Let's meet Wednesday the 15th to discuss ideas! '''How about 7pm''' in the second place we did the speed dating yesterday? (That small courtyard with a wooden table) <br>
If you cannot make it but are interested in the group, email me (ubilal@jhmi.edu) and we'll try to see if we can organize a second meeting or discuss ideas in some other way. <br>
There's a Slack group for the project called #citymobility, please join it for more updates if you are interested in the group!

== Can we use metabolic networks to predict the next beneficial mutation?==
===ideas===
Richard Lenski (who is also associated with SFI) has evolved an coli population to use a new sugar source, and tracked their changes for 40000 generations. This is a widely studied dataset (>200 papers published on it already...) in one of the most widely studied biological system but there is still a lot we do not understand about it. For example although we know what genes evolved during the experiments, we do not know why it is these particular genes that changed. I think it would be cool to try to use genetic network (which is also very well understood) to try to understand how new mutations rising in frequency changes the performance of a bacteria, and how the previous state of performance changes what the next mutation should be. 
The genetic interactions are pretty well mapped in e.coli.(Regulondb) 
Also they claim that the performance plateaued, but the mutations that accumulated were still beneficial in the following paper. How did that happen? (Genome-wide Mutational Diversity in an Evolving Population of Escherichia coli)
From a more computer science perspective: 40000 generation for an iterative process doesn't seem like very long. Does the network structure of metabolism pathways allow rapid adaptation? What can we learn from these networks to apply to computer science problems?
These ideas are pretty raw... but I think something evolution related, that looks not just at a property of a system but also allow the system to change would be really cool. 
My email is [[Chenling Xu]] <chenlingantelope@gmail.com>. 
=== Interested please sign up below ===

*Ryan McGee (ryansmcgee@gmail.com)
*Chris Revell (cr395@cam.ac.uk)
*Lars Hubatsch (lars.hubatsch@crick.ac.uk)
* Dmitry Alexeev (exappeal@gmail.com) - though its my field - i would like to help and watch the idea developing - as well as learn some tools)
*[[Ulya Bayram]] (ulyabayram@gmail.com) I'm interested, since I find the problem space very interesting. I have other project ideas I want to tackle for CSSS, but I can be a part of this one at the side, brainstorming etc.
* Thomas Zhang (tomzhang@umich.edu)

== Complexity insights into Circular Economy ==

=== Keywords ===

Complexity economics, Network optimization, Urban systems, Agent-based modeling

=== Summary ===
The 'circular economy' is recent approach on questions of sustainability. The current 'take, make, waste' way of producing is considered to be not sustainable anymore. Current thoughts on 'closing the loop' mechanisms on producing and consuming are booming in many different academic fields. However, not yet from an integrated point of view. The idea of 'closing the loop' mechanisms corresponds to complex system paradigms, however complexity science has hardly been utilized in this field. In this project we want to start brainstorming on how complexity science can contribute to the field of circular economy. We think there are many ways in which complexity science can contribute, both in methods and in theory. Based on the competences in the group we would like to brainstorm on suitable research questions, that can range from very practical to theoretical issues.



=== Insights ===

''Please contribute here by adding your insights''

* (Juste) From the point of view of theoretical geography, I think this question directly enters the frame of the understanding of human settlements. Distribution of population and ressources in space and time is a fundamental question for which many theories have been proposed (Evolutive Urban theory, Scaling theories, Human territoriality, etc. among others) and that many disciplines have studied (economics, geography, planning, archeology, more recently physics). To my point of view this project has a strong component linked to this issue, as it tackles the question of sustainable territorial organizations and the possibility of the existence of closed subsystems. As a consequence, the study of circular economic systems should necessarily consider them as being, among others (1) multi-scalar, (2) non-ergodic and (3) non stationary [beside buzz-words : 1) urban systems exhibit by nature multi-scalarity, and thus a circular organization of ressources management would also be as it is necessarily embedded into an urban structure ; 2) path-dependancy and geographical context in urban systems generally imply that universality is difficult to establish and makes direct comparison hazardous : urban systems all over the world do not correspond to realizations of the same process at different times (see Pumain, 2012) ; 3) urban systems as economic systems in general are never at equilibrium and the nature and positions of attractors changes in times, i.e. parameters of the dynamic do not have a stationary distribution]. For these reasons, complexity approaches are a natural way to study them, and build associated models that include these properties.

* (Lorraine) Also from a cities perspective: taking the city as a non-equilibrium system, one could say the city forms a dissipative structure (Bristow and Kennedy, 2015) - this is also the case for ecosystems (Schneider and Kay, 1994). Dissipative structures maximize destruction of energy gradients (i.e., as the structure becomes more ordered/grows, more energy flows through). This has direct implications for increasing resource consumption in growing cities. Furthermore, scaling relationships for cities show that consumption of resources scales superlinearly with population (Bettencourt, 2013; Bettencourt et al, 2007), which implies that as cities grow, more and more energy is consumed. It would be interesting to explore the concept of a circular economy in such a system, i.e., can a "circular economy" policy mechanism actually change the organic dissipative/scaling nature of cities? Further, since ecosystems are also dissipative structures, are there examples of "circular economies" in ecology that can serve as a model for cities? Just some ideas, would love to discuss more.

* (Ben) I have worked on the topic of cleaning data sets for sustainable industrial production systems. Some of the work have been documented in http://enipedia.tudelft.nl/wiki/Industrial_Symbiosis_Data_Sources. I can also access to Europe Waste/pollution registry (stored as RDF format). It covers major industrial polluters in Europe.

=== Organization ===

As many meetings are scheduled, we will meet both tonight at 7pm (Wednesday) and tomorrow at 9pm (after physics lab) at the coffee shop. People interested can join one or both. A summary of the first meeting will be updated here.

=== Group contact ===

[[Joris Broere]] j.j.broere@uu.nl

[[Juste Raimbault]] juste.raimbault@polytechnique.edu

=== People interested please sign up below ===
*Joris Broere 
*Juste Raimbault
*Marius Somveille
*Scott Armstrong
*Christine Moore
*Lorraine Sugar
*Evelyn Strombom
*Jesus Mario Serna
*Ben Zhu
*Devrim Ikizler (devrimikizler@gmail.com)
*Mika Straka
*Nai Seng Wong (fsgwns@gmail.com)

== Insights in Scientometrics ==
===Summary===
I have multiple scientometric datasets scraped from the "Web of Science" website, which include published works from different (hard sciences only, sorry) domains. Author(s), Year, Journal, ..., and citations are available. I have some code ready to pre-process those data, e.g. for linking articles by co-citations, or bibliographic coupling. I've been working on these data for some time, and am eager to offer the data in exchange for fresh, brilliant ideas.

===Group Contact===
[[Matteo Morini]]

===Interested people please sign up below===

Xiongrui (John) Xu (xuxr007@gmail.com)

== Using DOTA 2 video game data as a proxy to improve cyber intrusion detection algorithms  ==
===Keywords===
Adaptive strategies, co-evolutionary systems, cybersecurity, DOTA 2, Inductive Game Theory, time-series analysis, potential NLP, potential genetic algorithms, potential network analysis
===Key Points===

'''Main question''': how to a) detect and b) quantify the rate of change of strategies in co-evolutionary systems?

'''Motivation''': improve cyber intrusion detection algorithms

'''Method''': use the competitive, strategic, multiplayer online game DOTA 2 data as a proxy for a cyber attack.  Extract sets of strategies from game observables and develop an algorithm to detect when strategies change.  This could be from within the games or from the drafting of characters (heroes).  

===Summary===

Most metrics for scoring how well a given cyber intrusion algorithm performed involve static determinations after an attack has occurred and usually consist of simple false positive or false negative type scores.  This does not enable the granularity necessary to improve these algorithms in appreciable ways.  The goal is to develop new metrics to assess ''in real time'' when and how attackers adapt to the protective mechanisms they encounter so that the security teams can adjust their own defensive tactics more quickly.  This could decrease the long lead time for patches and stave off additional damage.  

Usable data from within the cyber domain that may help to strengthen detection algorithm scoring is nearly nonexistent.  If actual attacks are documented in the real world, this data is rarely made available and may be network specific (hence, not generalizable).   Thus, a proxy for the data is necessary.  An online battle arena video game called Defense of the Ancients 2 (Dota 2) is a rich data source of real-time adaptive adversaries.  Dota 2 is a strategic, competitive, multiplayer game where two teams of five individuals each compete against each other to complete objectives and to destroy the other team’s base in a time frame of ~20 to ~90 minutes.  The players deploy various in-game and between-game tactics and procedures to achieve a specific measurable objective.  Professional players vie for tens of millions of dollars in prize pools each year, and over 2 billion games have been played.  Currently, we have access to a Mongo database with 500 GB of data (~2.5 million games played over one year).  

This is akin to the classic ‘Red Queen hypothesis’ in evolutionary biology, but in this case we are interested in human behaviors where a strategy can be considered most generally as a ‘meme’ of sorts.  Our hypotheses include: 1) a mapping exists between game observables and a set of strategies, 2) a measurable signal can be extracted from which to ascertain the adoption, stabilization, and decay of specific strategy traits, 3) changes in strategies occur over time, and 4) strategy changes are driven (at least in part) from behaviors of the opposing team.  We postulate that testing these hypotheses will require an understanding of the co-evolutionary dynamics of the overall environment.  In particular, the human behavioral components underlying the adversary/defense team actions will need to be assessed.  The use of data from a multiplayer online game for this purpose assumes there is a valid mapping between the ‘game-space’ to ‘cyber-space’ behaviors from which useful inferences can be made.  Through this exercise we hope to understand what types of data (if any) are useful for this purpose and how one might develop proxies to characterize strategies.  Ultimately, we hope that the analysis could be applied against realistic data specific to a cyber intrusion.  

See [[Media:mitredatadota.pdf |DOTA 2 DATA OVERVIEW]] for additional details.  


===Technology===

Parsed data is in JSON format.  We should have access to two databases: one is a mongo database discussed above for aggregated information (e.g., fights, deaths (characters respawn), characters played, items acquired, positions for the first 10 minutes, league IDs, player IDs, wins), and the second is raw data for every move and every action each player took throughout the entire game.  

===Interesting addition and comments to the idea===

Please add your comments and idea about the project here.

===Group Contact===
Anne Sallaska, alsallaska@gmail.com (or asallaska@mitre.org, harder to check so slower to respond potentially).  Slack channel has been set up!  If you need an invite to Slack, let me know.

===Interested participants, please sign below===
* Harrison Smith (hbs@asu.edu)
* Ellen Badgley (flyingrat42@gmail.com) - I'm happy to provide support for the Java parser
* Will Hamilton (wleif@stanford.edu)
* Marla Stuart (marlastuart@berkeley.edu)
* Salva Duran (salvador.duran at upf.edu)
* Jacob Hunter (jacob.hunter@pnnl.gov)
* Daniel Biro (daniel.biro@med.einstein.yu.edu)

== Complexity in fruit fly learning behavior ==

=== Keywords ===
Fruit fly, learning, behavior, phenotypic space

=== Summary ===
I designed and built a high-throughput apparatus (currently in the process of patent application), in which I use laser to deliver heat stress to a single fly. When the fly moves, the laser turns on, when the fly stops, the laser shuts down. In order to avoid the heat, the fly has to learn not to move.  Some flies have difficulty in making correct decisions under this stress. Different genetic mutations related to dopamine/serotonin systems may have contributed to the incorrect decisions made.

In the following figures, the first one is a fly’s movement trajectory before training; the second one is the same fly’s movement after one training (usually one fly will experience two training sessions or more).

Before training

[[Image:Beforetraining.jpg | 500px]] 

After one training session

[[Image:Aftertraining.jpg  | 500px]]

=== Objectives ===
The goal of this project is two-fold: 
1) Quantify the complexity hidden in the learning curve of individual flies, and characterize variations from fly to fly.  
2) Create a mathematical model for the fly’s learning process, and verify it in different animals, as well as to understand how mutation in the genome affect learning behavior from the perspective of parametric analysis in mathematical models. 

My time-series trajectory measurement dataset consists of approximately 700 individual flies, including wide-type and mutant flies. It is a rich dataset ready to be analyzed.

=== Group Contact ===
Ruichen Sun (ruichensun0 at gmail dot com, or r5sun at ucsd dot edu)

=== Interested participants, please sign up below ===
Cheng Jin (chengkim@zju.edu.cn) <br>
Jacob Hunter (jacob.hunter@pnnl.gov) <br>
Chris Revell (cr395@cam.ac.uk) <br>
Kelly Finn (kfinn@ucdavis.edu) <br>

== An Interdisciplinary Approach to Morphogenesis ==


=== Presentation ===

''This is not properly speaking a project proposition as the idea is quite ambitious and the result may be more at a theoretical level than concrete and practical developments. If some are really interested but are already busy with other projects, I think this one can be done without heavy time consumption if it stays at a theoretical level.''

The concept of morphogenesis is a powerful approach to many kind of complex systems. It aims at unveiling the processes responsible for the formation of patterns, and link the emergence of these patterns to functional properties of the system. This definition would sound wrong to anyone who has been involved with morphogenesis, as each field has its own definition and use of the concept. Proposing an interdisciplinary approach to this concept has already be done (see e.g. Bourgine & Lesne 2010, available  [http://37.187.242.99/files/public/MorphogenesisBourgine.pdf here] ) but to my point of view its potential richness has not been exhausted. The idea would be to gather insights from various people having worked on morphogenesis and to study the potentialities of a self-consistent theoretical framework that would include the different views, and be autonomous as an approach to complex systems. The work would be both on an epistemological and theoretical level, but also potentially practical applications (e.g. develop an existing model someone has worked on, given the insights of other domains and of the theory).

=== Group contact ===

[[Juste Raimbault]] (juste.raimbault@polytechnique.edu)

=== Interested, sign up below ===

'' add your field and if you worked on morphogenesis, which particular approach''

* Juste Raimbault [worked on various models of urban morphogenesis, and currently trying to develop a theory of co-evolutive territorial systems which one of the core components is morphogenesis]
*  Lars Hubatsch [I work in ''C. elegans'' which is a classical biological system for morphogenesis]
* Jesus Mario Serna (familiar with geomorphogenesis and the possible relations to psychological structures)
* Emanuele Crosato (I studied Artificial Life and I've been investigating Artificial Self-assembly, and also produced a literature review on it)
* Moriah Echlin (I have some experience with pattern formation in networks and experience in theoretical biology/modeling)
* Chris Revell (cr395@cam.ac.uk - I work on computational modelling of self-organisation in the mammalian embryo)
* Aina Ollé Vila (aina.olle@upf.edu)-I study tissue organization and cell differentiation
* Chenling Xu (chenlingantelope@gmail.com) - I have not studied morphogenesis but I am interested in learning about a species of frog that has a few different skin patterns (spotted, horizontal stripe and vertical stripe) for imitating a poisonous frog. I don't know the genetic basis of how the patterns are produced but I think if we can figure out how to reproduce the natural patterns by varying parameters in a diffusion system and predict what the minimum number of genetic changes is required for producing all the variety we observe in the nature, and how much of the variation is just caused by noise.
* Daniel Biro (daniel.biro@med.einstein.yu.edu) (I work on computational models of evolution that incorporate aspects of development, particularly robustness)

==Patterns in Globalization==

===Keywords===
 patterns, globalization, trade, fractals, trends, British empire, Trieste

===Summary===

What if can we see how is the face of globalization? 
Globalization is a phenomenon lasting centuries. I believe we can begin to look better at its face from the data of trade.

Thanks to the scaling power of fractals, we can look for patterns into a small, well defined environments, such as the free ports.

I have the data regarding the flow of goods in the free port of Trieste in the 19th century and specifically those regarding Trieste and the British Empire.
Trieste was the main port of the Habsburg empire, one of the 10 most important port in the world at the time. In the 19th century we had a kind of globalization that was centred on the British financial system, so it is important to see the grade of integration with the British world System of a single free port  out of the British empire.

I have data regarding:

vessels: tons and value of goods, flag, provenance and destination

goods: amount in tons and value of import-export for each typology, 
general data regarding trade in Trieste

In addition I have the data regarding the Generali Insurance company. Based in Trieste, the company was and is one of most important insurance company in the world.
I have all its balance sheets from 1830s to World War 1. We can look  at insurance on vessels and the goods they transported as a proxy for the general development of trade in Trieste and to evaluate trade risk over the time.

We can begin concentrating on 3 goods, for example, and to see what kind of networks of countries we find out, what trends they developed.
In general we can choose from the huge data set I have what can be more interesting to our purpose.
What models we can build, what math we can use and what tools fit better to find the patterns, which ultimately will led us to begin to see on the horizon the face of globalization.

===Interesting addition and comments to the idea===

Please add your comments and idea about the project here.

* (Lorraine) It would also be interesting to explore how trade impacts the growth of the city itself. Materials entering and exiting, how materials are stored, how the city is built, etc...
               
*(Gaetano) very well, I have data about it too, for example this on number of houses (case) and population (abitanti):
[[File:Selection_097.jpg]]

===Group Contact===
Gaetano Dato, gdato7@gmail.com

===Interested Participants===
* Lorraine Sugar (lorraine.sugar@gmail.com)
* Ben Zhu (b.zhu@tudelft.nl)
* Usama Bilal <ubilal@jhmi.edu> This is a topic that really interests me. I'm mostly focused ("back home") in short-term changes but I do believe in the enormous (actually, much more important!) importance of long-term trends
* [[Lu Liu]] A paper on product space and golbal economy which might be relavent to the project : Hidalgo, César A., et al. "The product space conditions the development of nations." Science 317.5837 (2007): 482-487. (Hi Lu, I've encountered this paper as well, and I've shown it to Gaetano. They also have interesting visualizations of the product space for each country. More to see here: http://atlas.media.mit.edu/en/ - [[Anjali]] )
Catriona Sissons (c.sissons@auckland.ac.nz)

===a small gift to everybody: a link on a simulation showing a single mass on a spring===
For those who likes to play with this kind of things:
http://www.myphysicslab.com/spring1.html

== Consensus, conflict and coalitions: exploring agent interactions within health policy systems ==

===Keywords===
Decision making, Health policy, Network influence

===Summary===

Decision makers in health choose which policies to support from a range of options. These decisions are theorised to be based on; what they see is the best option for society (i.e. public health), what they see is the best option for themselves (financially or politically), what they are told is the best option by other decision makers, what they think based on previous experience, or a combination of various processes.

Health policy outcomes are determined collectively via interactions within policy systems. Policy actors (e.g. government departments, charities, businesses, media etc.) work to influence each other and final policy decisions. Policies with high levels of support are more likely to be implemented, while disagreement between actors may stop one decision and lead to another less beneficial public health outcome. 

In cases of conflict, rival policy advocacy coalitions (network components) can form. Coalition structures may affect the flow of information between decision makers for future decisions, even when levels of conflict may be lower. This could in turn influence how well the policy process benefits public health, or benefits certain actors. 

This project aims to explore how the characteristics of actors within policy systems can lead to different policy outcomes and system configurations. We aim to construct health policy networks based on existing research and policy theory, develop models to explain the evolution of policy networks over time, assess how our model outcomes compare to outcomes in real policy systems, and determine how changing parameters of the model could change the outcomes of the system. 


The project will involve the following strands:

1. Identifying policy system structure; comparing UK and US policy systems (Based on political theory, health policy literature, or other suggestions from collaborators)<br>
2. Determining the ways in which actors take decisions (Based on data envelopment analysis, or other suggestions)<br>
3. Constructing a model to show changes in policy systems over time (Boolean Network, other suggestions)<br>
4. Determining are there certain characteristics of policy systems, policy actors, or policy options that lead to certain outcomes

The findings of the models could help us better understand conflict and coalitions in policy systems, variation in the use of evidence, and  the emergence of communication breakdowns in policy enivronments.

===Group Contact===
[[ Mark McCann ]] Mark.McCann@Glasgow.ac.uk 

=== Interested participants, please sign up below ===
* Jesus Mario Serna
*  Rana Azghandi
* Marla Stuart

==Complexity in Material Stocked in Japan==

===Keywords===
cities, industrial ecology, materials, GIS, fractals, scaling

===Summary===

There is an amazing GIS dataset from a group in Japan about material stocks in buildings and infrastructure -- cell size is 1km, it extends throughout the country over multiple years: 

Tanikawa et al. 2015: http://onlinelibrary.wiley.com/doi/10.1111/jiec.12284/abstract

Looking at the map of the stocked materials, there are clear links to complexity (fractals, scaling, etc.). I have a copy of the dataset (but limited ArcGIS skills). It would be awesome to form a team to dive into this dataset using complexity tools. I think interesting things can be learned related to physical infrastructure growth and the mass of cities.

===Other Ideas/Input===



===Group Contact===
Lorraine Sugar (lorraine.sugar@gmail.com)

===Interested Participants===
* Ben Zhu (b.zhu@tudelft.nl)
* [[Lu Liu]]


==Complexity and P = NP==
===Summary===

It seems intuitive that if I were to A) give you the output (and only the output) of the logistic map with paramater 4 and initial condition 0.4, you would have a hard time recovering the function and parameter set, and B) if I gave you the logistic map and parameter values, you could quickly verify that they produced the output. This project would be about studying the P=NP problem and the relevant information theory and chaos theory.

Admittedly this is something I know little to nothing about, and whole fields of time series analysis are devoted to recovering functions and their parameters: establishing that there exists a function that produces output that is not computably invertible but is easily verified would make a big statement on the P = NP idea. The activities of this group would be reading and understanding information theory and chaos theory and working towards a paper that provides some insight on P=NP and relevant complexity theory.

JE: I think symbolic regression could actually recover the return map rather quickly, although it would certainly be difficult (impossible) to confirm the uniqueness of this map. I'm very curious about this topic, but it could be as simple as a conversation with Jim Crutchfield sometime.

===Group Contact=== 
Xander Brehm (alexander.brehm@linacre.ox.ac.uk)<br> 

=== Interested participants, please sign up below ===
*Jeffrey Emenheiser (jemenheiser@ucdavis.edu), but I have too many projects already.

==Complexity in speech patterns==

===Summary===

Have you ever: <br>
-	Been bored by listening to a monotone lecturer?  <br>
-	Been inspired by a dynamic motivational speaker?  <br>
-	Counted the number of “umms” in a conference talk? <br>
-	Thought about how lovely it is to listen to Morgan Freeman? <br>
<br>
And more importantly, are you curious about what properties in speech patterns elicit these reactions? <br>
<br>
There are many properties in speech time series (fluctuations in tone, pattern of sound/silence, word pattern, etc. etc.).  I’m interested in investigating patterns in these different properties, specifically how various complexity measures of speech relate to listener perception.  <br>
<br>
Possible data:  online lectures, political candidate debates or speeches (could be fun, especially given current politics)<br>
Possible measures of perception: “likes” or internet popularity, or a short study having CSSS participants rate short audio clips of people talking. <br>
*could also play around with speech simulations <br>
<br>
Other ideas welcome!<br>

===Group Contact:===

Kelly Finn (kfinn@ucdavis.edu)

===Interested Participants:===

*[[Andrew Christian]] (CommonTime@gmail.com)
*Ruichen Sun (ruichensun0 at gmail.com)


==Tournaments of the World==

===Summary===
Some of you may be aware of the (important) things that are going on in the world today: the NBA finals, Euro 2016, the 100th Copa America, the NHL finals, et al. A question that I am interested in is trying to deduce what the probability was that the winner of the tournament was going to win based on the data generated by the tournament (the results of all of the matches).

My ansatz is that this will involve some Bayesian analysis to create models of the various teams, then a Monte Carlo phase of simulating tournaments based on these models. Once this MC dataset is created, one can ask any number of fascinating questions (e.g., What is the predictive power of the tournament? How sensitive is it to the seeding? What is a better tournament algorithm? And, of course, how unlikely is it for the US to win a major soccer tournament?)

I feel like I need to work some more [complexity] into this idea. Suggestions are welcome. Perhaps there is something to be said regarding the information generated by sporting events (as they are not simply random events as insinuated above).

Note: This is not just about sports, but these tournaments provide a timely example of data that is akin to the human subject data that I often encounter in my work (so don't feel bad about joining in if sports aren't your thing).

===Group Contact===
[[Andrew Christian]] (CommonTime@gmail.com)

===Interested Participants===

=Data Sets=

==MITRE Data Sets==
The two data sets we have access to are Defense of the Ancients 2 (DOTA 2) and Polish Power grids. To access the data please contact Juniper she has it on a hard drive. If you have any specific questions about the data you can contact Matt Koehler at mkoehler@mitre.org. 

[[Media:mitredatadota.pdf |DOTA 2 DATA OVERVIEW]]

==MITRE Challenge Questions and Powergrid Data Overview==

[[Media:mitrechallengeq.pdf |MITRE Challenge Questions Overview]]

<br>