Santa Fe Institute Events Wiki - User contributions [en]

Ranch Party

2018-07-01T23:02:58Z

Jenn:

{{Complex Systems Summer School 2018}}

Cows! Green grass! A bunch of water! Come on down for a barbecue and general all-round hangout.

It's going to require some self-organization and coordination to pull this off. Sign up if you feel like bringing something or driving a car.

Sign up in a car and then we should probably make a stop at a grocery store for things you may want. JP will have the basics covered like chicken, bratwurst, tortillas, burgers, water, etc.

''''***NOTE THAT DWI ENFORCEMENT WILL BE ACTIVE DURING THIS EVENING AND DESIGNATED DRIVERS ARE A MUST***''''

=Cars=

===JP's "These seats are really nice!" GTI (5 seats)===

# JP
# Sanna
# Ricky
# Ariadna
# Ben (designated driver if you want)

===TG's car (5 seats)===
# Thushara (designated driver)
# Sandra!
# Xindi
# Ada
# Andrea

===Sasha's car (5 seats) Leaving at 6!!===
#Sasha (dd)
#Stephanie
# Neil
# Alan
#Ana

===Runner (5 seats)===

# Sarah B. (can be the dd if someone knowing the US American traffic rules sits nearby ;)
# Cat
# Shantal
# George
# Alice

=== Taos car (5 seats)===
# Rishi
# Saska
# Jonas
# Pete
# Javier

=== MITRE car (it's pronounced "MY-ter")===
# Kevin (DD)
# Duy
# Conor
# Anastasiya
# Alex

===Kasha Katuwe car===
#Chathika
#Cesar
#Gianrocco
#Maria
#Rosalba

==Riding with Lorenzo (multiple runs possible) ==
# Vandana 
# Maria 
# Jarno 
# Nam 
# Subash 
# Chris 
# Tom 
# Josefine 
# Magdalena 
# Matt 
# Zohar 
# Yanchen 
# Caroline Alves 
# Carlos 
# Louisa 
# Eleonora 

===Still Needs A Ride===

# Simon 
# Evgenia (probably rents a car to go to Taos, could be dd) 
# jenn 
...

=Supplies=

==Non-Vegetarian Things==

JP: Burgers, Brats, Chicken
jenn: more chicken

==Vegetarian==

* TG: fruit to grill
* Dave: veggie burgers
* Peter: asparagus to grill
* Sarah B: mushrooms to grill
* Alex & Shantal: green chili mac & cheese

==Snacks==
JP: Tortillas, Chips
Priya: Chips, dips
Sanna: free-from dessert

==Drinks==
Marina: non-alcoholic fizzy drinks 
Ricky and Xindi: Wine * 2
Alice: Cider

==Dessert==
Marina: Fruit tart

==Other==

JP: Grilling Supplies (Charcoal)

Stephanie: 1 deck of cards, 1 deck of SET

Larremore Workshop 2: How To Rank Things & Pairwise Comparisons (limit 40 people)

2018-06-25T15:22:03Z

Jenn:

{{Complex Systems Summer School 2018}}

Dan Larremore Workshop 2

Tuesday June 26 @ 3:15pm in the conference room.

1. Carlos Marino 
2. Jared Edgerton 
3. Maria 
4. Simon 
5. JP 
6.Thushara 
7.Gianrocco 
8. jenn 
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Complex Systems Summer School 2018-After Hours

2018-06-19T17:37:58Z

Jenn:

{{Complex Systems Summer School 2018}}

Please use this space to plan social events.

==current events==

=== '''White Sands National Monument of New Mexico''' ===

Anyone up for visiting the White Sands national monument of New Mexico? It's a roughly four-hour drive and maybe we could drive down on the second or third weekend?

This is an excerpt form the website: "Rising from the heart of the Tularosa Basin is one of the world's great natural wonders - the glistening white sands of New Mexico. Great wave-like dunes of gypsum sand have engulfed 275 square miles of desert, creating the world's largest gypsum dunefield. White Sands National Monument preserves a major portion of this unique dunefield, along with the plants and animals that live here."

And just to tease you, here's a photo:

[[File:white-sands-national-monument.jpg|500px|]]

Interested to visit the White Sands (feel free to add more numbers - we can source cars and drivers accordingly):

====Preliminary Schedule====

Late Friday OR early Saturday - Leave IAIA for Carlsbad 
~11:00am Saturday - Tour Carlsbad 
~2:00pm Saturday - Travel to White Sands 
~5:30pm Saturday - Arrive at White Sands 
Camp at White Sands (some people will need to rent camping gear from REI) 
Early Morning Sunday - Travel back to IAIA 

====Cars and Passengers====

1. Kevin (leaving Saturday EARLY morning) 
2. Shantal 
3. Alex 
4. Ariadna 
5. Vandana 
 
1. Chathika 
2. Anastasiya 
3. Neil 
4. Simon 
5. Cesar 

====List from before====
1. Sanna 
2. Sarah B. 
3. Xindi 
4. Yanchen 
5. Chathika 
6. Eleonora (2nd weekend) 
7. Ricky 
8. Cedric 
9. Louisa 
10. Anastasiya 
11. Ariadna 
12. Shantal 
13. Alex (2nd weekend) 
14. Yuki 
15. Cesar 
16. Inga (3rd weekend only) 
17. Duy (3rd weekend) 
18. Andrea 
19. Magdalena 
20. Carlos 
21. Guillaume 
22. Neil 
23. Maria 
24. Alan (2nd weekend) 
25. Rosalba 
26. Gianrocco 
27. Evgenia (would be cool if it is on the different weekend than Taos trip; happy to rent the car and drive) 
28. Josefine 
29. Laura (has a car) 
30. Kevin 
31. Luca 
Website: https://www.nps.gov/whsa/learn/photosmultimedia/photogallery.htm

=== '''Great Sand Dunes National Park''' ===

POSTPONED due to weather! Saturday June 23rd-24th a few of us are planning a day hike out to Great Sand Dunes National Park if it ends up raining or even camping overnight to go climbing the next day newrby. It'll be a 3.5-hour drive and there's a local REI to rent gear if you need.

We have two cars:

1. Duy 
2. Chathika 
3. Gianrocco ( I prefer 1-day hike, rather than 2 days) 
4. Jordan 
5. 

1. Amy 
2.Neil 
3.Laura 
4. 
5. 

=== '''Rodeo!''' ===

Apparently the 69th Rodeo dear Santa Fe is happening from June 20-23, so we should go! Tickets are about $20.

http://rodeodesantafe.org/

[[2018 Rodeo De Santa Fe | Rodeo Sign Up Page! ]]

=== '''Santa Fe Wine Festival''' ===

It's not soon, but wine not???

https://golondrinas.org/festivals/santa-fe-wine-festival/ 
1. Sandra 
2. Magdalena 
3. Anastasiya 
4. jenn 

=== '''Santa Fe Brewing Tours''' ===

On Saturdays at 12pm — who’s in?
 
1. R Maria 
2. Sandra 
3. Alan 
4. Shantal 

=== '''Santa Fe Opera ===

There are a number of shows going on at the Santa Fe Opera.
https://www.santafeopera.org/calendar

The ones occurring during CSSS 2018 are

1. Candide, June 29, 8:30pm 
2. Madame Butterfly, June 30, 8:30pm 
3. Candide, July 4, 8:30pm 

Tickets begin at $54. Get tickets soon, before they sell out.

Candide, July 4th 
1. Kevin Comer, Seat FF-54 
2. Magdalena Klemun, Seat MC-42 
3. 
4. 
5. 

Madame Butterfly, June 30, 8:30pm 
1. Alan Pacheco 
2. Andres Ortiz 
3. 
4. 
5. 

=== ''' Dirty Projectors at Meow Wolf ''' ===

A few of us are buying tickets to see the Dirty Projectors at Meow Wolf after dinner on Thursday, June 28.
Tickets are here: https://meowwolf.com/event/dirty-projectors/ and will be cheaper ($20) if you buy them soon, $25 day of.

=== "Spiritual retreat" near Taos and Hot Springs ===
We're planning to go the weekend of the 30th (leaving Friday from the SFI institute) to a place a couple hours north. We have an airbnb for ~22 people, with 16 beds + rest on sleeping pads. There are two buildings so people can sleep or party. During the day we can check out Taos Pueblo, go hiking or to ojo caliente. We're also 20min from the border with Colorado. We could even go Friday night to the hot springs.

~$45/person

1. Pete 
2. Saska 
3. Javier 
4. Catriona 
5. JP 
6. Jonas 
7. Allie 
8. Niccolo 
9. Jordan 
10. Duy 
11. Elan 
12 R Maria. 
13. Zohar 
14. Guillaume 
15.yuki 
16.cedric 
17. Chathika 
18. Cesar 
19. Neil 
20. Ana 
21. Andrea 
22. Simon 
23. Evgenia 

Available cars (to see how many we need to rent) 
1. JP (one way likely) 
2. Duy 
3. Cat 
4. Jordan 

Waiting list : 
1. Maria 
2. Louisa 
3. Alan 
4. Eleonora 
5. Kevin 
6. Anastasiya 
7. Luca 

=== '''Lazy Yak Ranch and Colorado Hiking''' ===
As many of you have heard, Alex's 5-year quest to track down yak butter in the US (and Europe) is about to come to a close. We will drive up to Del Norte, Colorado early on Saturday, June 30th (~3hr drive, going up through Carson National Forest), hike in some of the canyons in the area, and visit Lazy Yak Ranch in the afternoon, where we will get to meet Amy Archer, who runs the place, and spend some quality time with her yaks. We will get a full yak-milking tutorial (though, much to my disappointment, because a lot of the process is mechanized, we likely will not be touching yak udders). From here, we will likely get dinner in town, before driving back for the evening, getting some star-gazing in on the way. 

Here's a description of a proposed hike (open to other suggestions!): 
"Penitente Canyon is a 5.4 mile lightly trafficked loop trail located near Del Norte, Colorado that offers scenic views and is rated as moderate. The trail offers a number of activity options and is accessible year-round. Dogs and horses are also able to use this trail." 
https://www.alltrails.com/trail/us/colorado/penitente-canyon 

And here's a link to the Yak Farm: https://www.facebook.com/lazyyakranch/ 

Here's a sign up for the event... Please note if you have a car, as this will be used to figure out our car-rental situation. No hard cap on how many can come, so feel free to add more numbers! 
1. Alex 
2. Louisa 
3. Xindi (a maybe) 
4. Carlos 
5. 
 

=== '''Jemez Hot Springs''' ===
Maybe try again when there is no fire danger.

==Completed Events==

Duy's is going into town for the dinner event instead of taking the shuttle if folks want to stay later in town afterwards. Meet at the 1st floor at 5PM

Duy's car (5 seats)

1.Duy 
2.Kevin 
3.Shantal 
4.Sanna 
5. 

=== '''Meow Wolf ''' ===

Meow wolf is an immersive art thing and it has story in it as well (some mystery of Selig family)!

See: https://meowwolf.com/the-thing-to-do-in-santa-fe/

With the chaos of going to Meow wolf, let’s make time slots and get a head count (there is group discount price)

'''Early in the morning, tentatively leave at 9:30AM (it opens at 10:00AM, it will be less crowded around that time), also after that, we could head downtown'''

WE ARE GATHERING AT 9:15AM in the front!

1. Xindi 
2. Yanchen 
3. Sasha 
4. Ricky 
5. Jacob 

'''Go to Santa Fe in the morning/at noon to spend time in the city and then enter meow wolf in the late afternoon and stay until the DJ thing'''

1. Sarah B. 
2. Xindi (could also do this) 
3. 
4. 
5. Kevin 
6. Cesar 
7. Yuki 

"Evening, 7:00PM (depart from iaia at 6:30pm after short dinner)'''

1. 
2. Chathika 
3.Cedric 
4. Guillaume 
5. Cesar 
6. Sandra 

'''Late at night, 9:30PM (mainly for the DJ thing, the Meow wolf closes at 10:00PM)'''

1. 
2. 
3. 
4. 
5. 

'''People with cars'''

1. Laura 
2. 
3. 

'''List from before'''

1. Laura 
2. Xindi 
3. Yanchen 
4. Sarah (Berkemer) 
5. Niccolo 
6. Ricky 
7. Allie 
8. Kevin 
9. Zohar 
10. Cedric 
11. Alan 
12. Sanna 
13. Andrea 
14. Kostantinos 

=== '''Jemez Hot Springs''' ===

New plan: since the free hot springs are on public land, they are all closed due to fire danger :( instead go on a hike to Diablo Canyon ( 8-ish mile trail, an hour drive from IAIA). Definitely need water, good shoes, sunscreen! Find Sasha or Amy at brunch to figure out car assignments.

Any chance we could go to the Giggling springs instead to keep the plan for a relaxing activity? Also, is Lavender Fest still part of the plan, as a bunch of us wanted to see that before the hot springs? - Let's coordinate over brunch!
--------
(Planning a trip to Jemez Hot Springs this Sunday 6/17 (about 40 mins away). It might be cooler on Sunday after the storm, so it's a good time to get into hot pools of water....

We can go to the more official spa place "Giggling Springs" ($25/hour) or to primitive hot springs nearby (free, no facilities, bit of hiking required on the approach) 
http://www.jemezsprings.org/attractions/hot-springs-spas/ 
http://www.gigglingsprings.com/ 

please indicate interest below :) tentatively planning to leave before noon.)

Anyone up for visiting the Lavender Festival before? Maybe around 11am?

Hi guys - I'm flexible on leaving time and would enjoy the lavender festival, but want to try the hiking hot springs (not the pay ones). If that sounds good, let me know. We can always split up the cars too. Also, since brunch/breakfast starts at 11, I would like to eat, so maybe leave at 11:20? --Amy

1. Sasha 
2. Sanna 
3.Maria 
4.Sarah B. 
5. Anastasiya 

Did someone say hot springs?!?! I'm there.

JP's Car 
1.JP 
2.Ariadna 
3. Cesar 
4. Elan 
5. Inga 

More people interested? Shall we rent a car? 
1. Eleonora 
2. Chathika 
3. Nam Le 
4. Carlos 
5. Amy *I can drive!* 
6. Rosalba 
7. Evgenia 

=== '''Saturday Walkabout''' ===

[[JP]] is going on a walkabout: Farmer's Market, lunch somewhere downtown, then out to El Rancho De Las Golondrinas for the Lavender Festival.

JP's VW Golf-ish

1.JP 
2.Maria 
3. Sanna 
4, Anastasiya 
5. Ariadna 

JP's "This thing is a beast!" 4Runner.
 Cat is keen to go to the Farmer's market then to an easy hike (weather permitting). There is also a big summer solstice festival at the Sikh community with dance and music that could be interesting to check out.

1.Catriona 
2.R Maria 
3.Rosalba 
4.Simon 
5. Yuki 

Duy's Car Planning on leaving at 9:30

1. Duy 
2. Shantal 
3. 
4. 
5. 

Still needs a ride. 
1. Javier 
2. Inga 
3.Saska 
4.Zohar 
5. Jonas 
6. Eleonora 
7. Andrea 
...

2018 Rodeo De Santa Fe

2018-06-19T17:30:53Z

Jenn:

{{Complex Systems Summer School 2018}}

Watch in awe as cowboys climb on top of raging livestock for your fun and entertainment. It's Rodeo de Santa Fe!

Departing 6:00pm, <strike>Wednesday June 20</strike> Thursday June 21. Tickets are $17 for general admission.

==JP's Super Cool GTI (5 seats)==
 
1.JP 
2.Kevin 
3.Alan 
4.Maria 
5. jenn 

==JP's Off Road Haulin' 4Runner (5 seats)==
1.(Driver Needed) 
2. 
3. 
4. 
5.

Elliott Workshop 1 Sign Up

2018-06-18T06:07:05Z

Jenn:

{{Complex Systems Summer School 2018}}

Monday, June 18th Sign-Up for Jorael Elliott's Workshop.

1. Luca Rade 
2. Louisa 
3. Ana 
4. Ada 
5. Ore 
6.yuki 
7. Amy 
8. Patricia 
9. Kofi 
10. Inga 
11.Konstantinos 
12. Rosalba 
13. talia 
14. Carlos Marcelo 
15. George 
16. Dave F 
17. Ariadna 
18. Sanna 
19. Jenn 
20.

Complex Systems Summer School 2018-Projects & Working Groups

2018-06-15T17:31:24Z

Jenn: /* Recommended Papers */

{{Complex Systems Summer School 2018}}

=Projects=

== Characterizing the spatiotemporal transmission dynamics of smallpox in the United States prior to eradication ==

 Small pox is a highly contagious infectious disease eradicated through vaccination and social-distancing interventions. However, the city-to-city spatial transmission of smallpox is not well characterized. Understanding how smallpox moves between cities can have important implications for understanding how re-emerging vaccine-preventable infections, such as measles, can potentially spread, and subsequently controlled in the future.

This project aims to apply a metapopulation model to weekly case data from a number of cases in the US to estimate the rate of transmission between cities, determine if certain (i.e. larger) cities seeded epidemics to others (i.e. traveling waves), characterize any synchrony of epidemics across geographic regions, and to examine the effects of vaccination on transmission. 

=== Suggested papers ===
Grenfell BT, Bjornstad ON, Kappey J. Travelling waves and spatial hierarchies in measles epidemics. Nature 2001;414:716- 23.

=== Potential data ===
Project Tycho (data repository of MMWR notifiable diseases: https://www.tycho.pitt.edu/dataset/US.67924001/)

=== Interested Participants ===
* talia

=== Meeting time ===
friday @ SFI after 1st lecture (10:00 am)

== Understanding and creating music ==
This project has two direction:
* 1) Understanding music from a complex system point of view
* 2) Creating new music via neural style transformation

The two directions are not separated, if lucky enough, we hope to see them feeding each other :)

=== '''Understanding music from a complex system point of view'''===

===='''General Idea'''====

Music is definitely very complex. It is a combination of time (eg. melody) and space (eg. harmony structure across instruments). With all beautiful music in the world including profound and somewhat mathematic ones like Bach as well as inspiring ones as Beethoven, from rock and roll to electronic music, we don’t have a lot of understanding in them.

In this project, we aim to understand music from a complex system point of view, whether we could define the “style” for each music genre or era and composer, or whether we could quantitatively analyze the structure of a music piece. Music is composed with note sequences of different “layer”, including temporal information as well as notes interacting each other in time. Though there are only finite number of notes available, but the sequence it generated is infinite. Mathematically, music could potentially be described as a “network”, but a very complex one which is temporal, multilayer, higher-order(dyad may not be the best representation here).

'''One kind of a detailed idea/question''': Using network theory including multilayer networks, higher-order networks and temporal networks, could we figure out how each music genre differs from others and how each composer become characteristic?

'''Novelty''': Representing music as a network is not new, however, among the literatures, there is not many representing music as a network which is temporal, multilayer, potentially higher-order, which would add a whole new level of complexity in the study.

==== '''Relevant papers''' ====

* Me and my friend have done a very simple course project related to this, where we cluster 330 classical music pieces and found they corresponds to music era. We also found Bach fugues has distinct look using some matrix: [https://arxiv.org/pdf/1706.08928.pdf link to paper]
* Some one in Italy did this, the thing I don’t like is that he abandoned the time information in music, which is vital: [https://link.springer.com/content/pdf/10.1007%2Fs11042-017-5175-y.pdf link to paper]
* [https://pdfs.semanticscholar.org/caaf/a8e510525e7c5aca166f2bdd38e0660af6d8.pdf Complex network structure of musical compositions: Algorithmic generation of appealing music]
* There are also work done on relationship between music and psychology: [https://www.nature.com/articles/srep06130?_ga=1.190664162.812389991.1404656570 link to paper]
* Scaling in music! [http://rsos.royalsocietypublishing.org/content/royopensci/4/12/171282.full.pdf Multiple scaling behaviour and nonlinear traits in music scores]
* [http://www.computationalcreativity.net/iccc2016/wp-content/uploads/2016/01/A-Music-generating-System-Based-on-Network-Theory.pdf A Music-generating System Based on Network Theory]
* [https://link.springer.com/chapter/10.1007/978-3-319-08672-9_32 Complex Networks of Harmonic Structure in Classical Music ]
* [http://www.physics.fudan.edu.cn/tps/people/jphuang/Mypapers/EPL-6.pdf Complex network approach to classifying classical piano compositions ]
* [https://www.sciencedirect.com/science/article/pii/S0020025515006842 Musical rhythmic pattern extraction using relevance of communities in networks]

=== '''Neural style transfer in music styles via interacting agents'''===
===='''General idea'''====
*A) learn generative models of different music styles using neural networks.
*B) let these networks ('agents') interact and see what `fusion' music styles result.

'''Novelty''' lies in having the a) multiple agents learn multiple styles independently then letting them exchange information in a meaningful way (probably the trickiest bit) and b) letting these fusion music styles evolve in a network etc. and see what "world-music" results at the end for example.

'''Details''' will come...

===='''Relevant papers'''====
* neural style transfer for images (make images look like Van Gough paintings etc.) : https://tinyurl.com/ybpq5agm
* neural nets for music: https://tinyurl.com/yb2qdqbq and http://imanmalik.com/cs/2017/06/05/neural-style.html, https://magenta.tensorflow.org/performance-rnn
* bunch of theories of how music styles are results of combination: https://tinyurl.com/y723ugyo
* music recommendation using neural networks (from Spotify): http://benanne.github.io/2014/08/05/spotify-cnns.html#predicting, https://papers.nips.cc/paper/5004-deep-content-based-music-recommendation

=== Potential Data ===
* [https://homes.cs.washington.edu/~thickstn/musicnet.html MusicNet], lots of information for each pieces, but only 330 pieces and biased on composers
* MIDI corpus
** [https://www.reddit.com/r/WeAreTheMusicMakers/comments/3ajwe4/the_largest_midi_collection_on_the_internet/ Largest midi collection on the internet]
** [http://www.midiworld.com MIDI world]

=== Packages to handle MIDI/music (based on python) ===
* Python-based toolkit for computer-aided musicology: [http://web.mit.edu/music21/ music21]
* [https://pypi.org/project/mido/1.1.11/ Mido] is a library for working with MIDI messages and ports
* [https://github.com/vishnubob/python-midi Python MIDI], not maintained in a good way though...

===Thoughts?===
* For the generation one, we could also use text corpora instead? Shakespeare etc., creating like Shakespear + Tolstoy for example :D
* ...

===Interested Participants===
(Please denote your background and your potentially interested direction, or providing a ranking if interested in both: A.understanding B. generating) 
* Yuki
* Vandana
* Xindi (good at network science, data mining, a little bit machine learning, ranking: 1.A 2.B)
* R Maria
* Kevin
* Priya
* Ricky (multilayer networks, machine learning, data mining. ranking: 1A 2B)
* Chris

==Optimal representations of high dimensional data in deep learning and biological systems:==

What is the best way for a system to represent very high dimensional data? For example, how should the retina encode visual stimuli in neuron firing patterns? How does the immune system encode the space of antigens it might encounter? In each case, it would not be feasible (or efficient) to create a unique tag for each input. Rather, the systems in question must decide which features in the stimuli are most relevant, and trade off between specificity and generality.

Along these lines, there are two more specific questions to investigate:

-It has recently been conjectured that the success of deep learning networks is related to their optimization of a specific informational quantity in each layer https://arxiv.org/abs/1710.11324. Unfortunately this paper is not very clearly written, but basically the idea is that when binning inputs into representations, the distribution of bin sizes should be given by a specific power law, which optimizes the aforementioned information measure. Do biological systems employ the same strategy? With access to the right data, this idea should be straightforward to test. For example, if we have a list of antibodies together with the set of antigens they react to, we can compute this quantity and see whether the antigen "bins" are indeed distributed according to the predicted power law.

-A diverse collection of biological systems that are faced with this task seem to be well-modeled by maximum entropy distributions, with a constraint on pairwise correlations and parameters (i.e. lagrange multipliers) set near a critical point https://arxiv.org/pdf/1012.2242.pdf. This has been applied to the previously given examples of the retina and the immune system, as well as flocking in birds. As far as I know, it is not yet known with certainty whether this kind of encoding scheme is optimal in some sense (like in the previous bullet), or if it is an artifact of our own inference methods, but I think the answer is interesting either way. An immediate question is, if these maximum entropy models are a powerful tool for humans to model high dimensional systems, might biological systems also be producing their own maximum entropy models of environmental variables? That is, are maximum entropy models with constraints on pairwise correlations optimal in some information-theoretic sense, which can be made precise? For example, would this be a particularly useful way to model the distribution of natural images one might encounter? While less straightforward than the previous bullet, I think these are questions well-suited to the skills of the people here, and I think we could make significant progress!

If anyone has expertise to offer, your feedback/participation would be very much appreciated! In particular, I think this project would greatly benefit from those of you that have knowledge in machine learning and biology (my own area is physics and information theory). Feel free to email me at e.stopnitzky@gmail.com

===Thoughts? Recommended Papers?===
A '''genetic model''' with the resulting '''protein products''' could also be useful here (e.g. looking at expression levels and/or variants in a particular gene or set of genes as it pertains to the protein(s) coded by the aforementioned gene(s). In sum, can we find/demonstrate an algorithmic basis for gene expression and/or protein coding? - Kofi

1. Castillo et al. "The Network Structure of Cancer Ecosystems." SFI WORKING PAPER: (2017)

===Interested participants:===
- Kofi Khamit-Kush (Background in Biology, specifically Cancer Genomics and data mining). kkhamitk@gmail.com 
- George 
-Jacob 
- Yuki 
- Alice 
- Sarah B. (experience with sequencing data/gene expression) 
- Subash 

==The Emergence and Evolution of Legal Systems as Pertaining to Water Distribution==
===General Idea===
There are numerous legal systems that have been identified, broadly categorized into large families – Common Law (Anglosphere and Commonwealth nations), Civil Law (Romance Language nations, Germany, China), Islamic law (most Muslim nations), Customary Law (India, sub-Saharan Africa). More importantly, most nations do not purely lie in one category, but tend to combine elements of multiple systems, either due to merging (i.e. German law combining Germanic tradition with Civil traditions), or through subsidiarity (i.e. Louisiana having Napoleonic law, despite being in a Common Law nation). We are interested in determining how these legal systems by nations and states emerged, influenced each other, and interact over national boundaries. 
This is an immense task, so to scope it, one idea has been to limit this project to laws pertaining to water distribution. This is of particular interest when looking at states of nations that have different legal systems, such as Louisiana in the U.S., Quebec in Canada, and Scotland in the U.K. For international interactions, sub-Saharan African nations might also be of value in assessing, as many nations border nations with different legal systems, and water is often a scarse resource in these areas. 
If anyone has interest in this topic, and/or expertise in either legal systems or water distribution, feel free to sign up or discuss. 
===Recommended Papers===
Energy and Efficiency in the Realignment of Common-Law Water Rights, Carol M. Rose, The Journal of Legal Studies 1990 19:2, 261-296 
Theories of Water Law, Samuel C. Wiel, Harvard Law Review, Vol. 27, No. 6 (Apr., 1914), pp. 530-544 

===Interested Participants===
1. Kevin Comer 
2. Cedric Perret
3. Chris Fussner
4. Jared Edgerton

== Academic hiring networks ==
=== General idea:===
I am thinking about doing something around academic hiring networks in different disciplines and to play around with idea of multilevel networks (e.g. look at the interplay between different institutional norms in various disciplines and hiring dynamics). Also, would be cool to have a look on interplay between publishing / hiring networks.
We could also explore other ideas related to the academia theme like exploring factors that excellence / equality tradeoffs, or factors that promote gender balance in science, etc. 

Who would be interested? 

I've created the channel #hiring_networks at slack. 

=== Literature:===
* A. Clauset, S. Arbesman and D.B. Larremore. 2015. Systematic inequality and hierarchy in faculty hiring networks. Science Advances 1(1), e1400005 (2015).

===Interested Participants===
1. Evgenia (Background in social network dynamics, psychology and organisation science) 
2. Ricky (Background in multilayer networks, machine learning) 
3. Allie (Background in networks, science of science, gender) 
4. Carlos Marino.(Background in network optimization) 
5 . Andrea (Background in networks, multiplex and multilayer networks, information theory) 
6. Sanna (Background in networks and various social science disciplines) 
7. Conor (Background in information theory) 
8. Subash (Background in information theory - transfer entropy in specific, experimental design) 
9. Patricia (Background in modeling dynamical systems, agent-based modeling, experience working on academic search committees) 
10. Javier 
11. Saska 

== Make deep neural networks more biologically accurate by including inter-neural travel times==
=== General idea:===
Make deep neural networks more biologically accurate by including inter-neural travel times. Train with some normal task like digit-recognition.

=== Motivation:===
* Currently, deep neural networks only share some similarity to actual neurons: threshold behavior and hierarchical representations.
* However, in real neural networks, signals travel with finite speed and activations are integrated over time
* This ignored aspect could be one reason why real neuronal networks/brains are superior
* Further connecting the two fields of neuroscience and deep learning would be pretty cool
* We could use the "regular" neural network machinery to optimize weights etc for tasks like forecasting/image recognition and then see whether we find neural avalanches and chaotic behavior etc.

=== Details (first ideas):===
* In artificial neural networks, different neurons are connected by weights. To this, we add another connection between the neurons: the inter-neuron travel time.
* The inter-neuron travel time is computed by a RNN
* inference works by letting the network oscillate/ come to an equilibrium
* activation of neuron i at time t: a_i(t) = sum_over_connnected_neurons [f(a_j(t)) * delta(rnn(j->i)-t ) + exp(-lambda*t) f(a_i(t))], where delta is the Kronecker delta.
* I.e. the signal from connected neurons arrives at the time specified by the RNN and then slowly decays with exponent lambda
* if the RNN just gives t=1 for all travel times, this essentially reduces the normal deep neural net output.

== Evolution of social norms as a process within or between societies ==
=== General idea ===
Currently, there are two ideas floating around:
*How do social norms evolve *within* a society? Method-wise this is perhaps be related to the spread of ideas/information on a social network. The agents in this network are people. Potentially relevant models: Opinion formation, infectious (disease) spread and/or games on social networks.
*Think of a whole society (group/tribe/nation/etc) as an agent. A society may adopt or discard various social norms over time. If one of the chosen social norms (or a combination of the chosen combination of social norms) is woefully impractical it decreases the "fitness" of the whole society and it loses members/power/resources/territory to competing societies. Potentially relevant models: Models for evolutionary game theory.

The project can focus on (1), (2), or both.

====Branch: Agent Based Models and System Dynamics====

This branch seeks to use the 2 tools of ABMs and SD to further understand how social norms emerge through individual interaction from the bottom up(ABM) and how governing mechanisms then influence and shape those social norms from the top down (SD). Ideally this will even allow individual agents to select between emergent social norms and governing institutions which then further influences the feedbacks and system behavior.

The current challenge is finding a parsimonious construct and identify the key elements of this model to create the desired dynamics and analyze the subsequent behavior.

Interested in Branch : Tom, Thushara, Carlos Marino

===Recommended Papers===
For (1):
* Ostrom, Elinor. "Collective action and the evolution of social norms." Journal of economic perspectives 14.3 (2000): 137-158.
* Sethi, Rajiv, and Eswaran Somanathan. "The evolution of social norms in common property resource use." The American Economic Review (1996): 766-788.
* Centola, Damon, et al. "Experimental evidence for tipping points in social convention." Science 360.6393 (2018): 1116-1119.

For (2):
* Powers et al, "How institutions shaped the last major evolutionary transition to large-scale human societies" Phil. Trans. R. Soc. (2016)

For (branch):
* Centola, D., Becker, J., Brackbill, D., & Baronchelli, A. (2018). Experimental evidence for tipping points in social convention. Science, 360(6393), 1116-1119.
* Daniels, B. C., Krakauer, D. C., & Flack, J. C. (2017). Control of finite critical behaviour in a small-scale social system. Nature communications, 8, 14301. https://www.nature.com/articles/ncomms14301.pdf
* Lorini, G., & Marrosu, F. (2018). How individual habits fit/unfit social norms: from the historical perspective to a neurobiological repositioning of an unresolved problem. Frontiers in Sociology, 3, 14. https://www.frontiersin.org/articles/10.3389/fsoc.2018.00014/full
* Martin, R., & Sunley, P. (2006). Path dependence and regional economic evolution. Journal of economic geography, 6(4), 395-437.
* Cioffi-Revilla, C. (2005). A canonical theory of origins and development of social complexity. Journal of Mathematical Sociology, 29(2), 133-153. https://www.researchgate.net/publication/233820732_A_Canonical_Theory_of_Origins_and_Development_of_Social_Complexity

===Interested Participants===
Alice, Vandana, Alan, Xindi, Jenn, Matt, Sandra, Kevin, Alex, Cedric, Thushara, Subash, Josefine, Tom, Carlos

== Topological features of neutral networks in evolution ==
=== Introduction ===
In a genotype network, nodes are genotypes and a link from genotype A to genotype B indicates that they are separated by a single mutation. Each genotype has a phenotype associated with it. In a fixed environment, a phenotype is associated with a fixed fitness value. So for every node, one has:

GENOTYPE -> PHENOTYPE -> FITNESS VALUE

The fitness values form a "fitness landscape", in which one can embed the genotype network. The set of nodes in a genotype network that corresponds to the same fitness value are a *neutral network*. These networks have received little or no attention from network scientists. Let's change that!
=== General idea ===
Depending on the interest of participants, this project could focus on (1) data analysis or (2) network theory.

(1) Szendro et al. mention that empirical data for genotype networks and their neutral networks is available. This is a somewhat new development (<10years). One could scout for one or several available data sets and study the topology of the networks. For example,
* what are topological characteristics of genotype networks? Can these characteristics be explained by constraints of embedding on a curved manifold? (One could compare data to random graph models, e.g. Erdos-Renyi, small world, or geometric random graph models.)
*how are neutral networks for high or low fitness values different?
*one could also think of the genotype network as a multlayer network with a lot of layers ... and analyse its topology from a multilayer perspective.

(2) A neutral network is a "level-set network" in the genotype network. The genotype network is a network that is embedded in a curved manifold in a high-dimensional space. There is so much cool math/physics/topology that one could do with this!!
=== Recommended Papers===
*https://en.wikipedia.org/wiki/Neutral_network_(evolution)
*Szendro, Ivan G., et al. "Quantitative analyses of empirical fitness landscapes." Journal of Statistical Mechanics: Theory and Experiment 2013.01 (2013): P01005.
*De Visser, J. Arjan Gm, and Joachim Krug. "Empirical fitness landscapes and the predictability of evolution." Nature Reviews Genetics 15.7 (2014): 480.
*Kondrashov, Dmitry A., and Fyodor A. Kondrashov. "Topological features of rugged fitness landscapes in sequence space." Trends in Genetics 31.1 (2015): 24-33.
===Interested Participants===
Alice

Ricky

Carlos

Sarah B.

George

Luca

Kofi K. (background in cancer genomics, data mining, and bioinformatics tools)
kkhamitk@gmail.com

==Networks from thresholded normally distributed data==

===Observations:===
- real-world networks are often created by thresholding dyadic interaction; 
- lots of things are approximately normally distributed. 

===Idea:===
- Suppose for each pair of nodes, i and j, there is a normally distributed interaction: x_ij ~ Normal(0,1); 
- Then, we place edges between nodes i and j whenever x_ij>threshold; 
- Edge correlations could be controlled by a single parameter, i.e. Cov(x, y) = beta . 

===Conjecture:===
- The resulting degree distributions have two limiting forms, and are approximately Poisson or power law(ish) (+ exponential cut-off), with something intermediate inbetween (log normal?)

===Things to look at:===
- Can we solve for the degree distribution of this model? 
- Does this degree distribution look like real networks? Can we fit the model easily (e.g. maximum likelihood or method of moments) 
- What about the giant component phase transition? 
- Does clustering vanish in the limit of large network size? 

This would be a more mathematical/theoretical project, and less about real world data. 

===Interested participants:===
* George (background in physics and networks) 
* Alice 
* Yanchen 
* Guillaume 
* Jordan 
* Conor 

==The Evolution of Beliefs in Abrahamic Religions==
===General Idea===
One commonality across all Abrahamic faiths – Judaism, Christianity, Islam, and others – is its reliance on the written word to solidify and codify beliefs, even centuries after the text was documented. Because of this large time difference between when documents were written – Torah, New Testament, Qu’ran – and when these beliefs grow and evolve, decisions are often linked to other texts as justification for the decision. For instance, when Ecumenical Councils declare a new testament of faith, they often point to previous texts from church fathers for justification (or sometimes non-believers, like pre-Christian Greek philosophers). Similarly, when imams declare testaments of faith, these are often linked to the hadiths and sirahs as justification. Canon law and Islamic law is based on these two dynamics respectively. Religions often influence each other, both as attractors (Islam prompted Iconoclasm in Eastern Christianity) and repulsors (Early Christianity set itself in opposition to Judaic practices, despite being considered a Jewish sect). 
===Recommended Papers===
===Interested Participants===
1. Kevin 
2. Carlos Marino 
3. Pete K. 

==City as a Complex System: Clustering/Mobility Network Effects==

===Introduction===
Cities are complex systems within which many sub-systems develop, interact and evolve. The understanding of how different systems within a city interact and connect with each other can help inform better urban planning decisions, to support different communities and ecosystems.

Through this study, we aim to gain insights on human choices, development of ecosystems, and spatial distribution in a city. Data from Singapore is available as a case study.

===Research Questions===
Some possible research questions are listed below, but feel free to add on any ideas related to this topic and we can discuss how to go from there!

Possible research questions (open to more ideas!): 
a. Business Clustering & Flow of Capital (human and/or monetary) between Industries

Motivation: To better distribute jobs closer to homes, a polycentric structure can be developed to establish multiple employment nodes in different areas of a city. Understanding of how business ecosystems develop and factors to support successful business/industrial clusters can help inform the strategies to establish and facilitate the growth of polycentricity.

*Are there clustering effects for business/industries across different sectors and how can this be measured/analyzed 
*What are the implications of clustering on the performance of businesses and industries? 
*What are the drivers to facilitate a sustainable business ecosystem? 

b. Intra-city Public Transport (PT) Mobility Patterns

Motivation: The study of people’s PT mobility patterns within a city allows us to understand human movement, choice and interactions. Through understanding mobility patterns in relation to the built environment and demographic make-up of different areas, we can gain insights to the human-environment relationship. This facilitates the formulating of more informed policy decisions and urban planning strategies to cater to the needs of the society on a local and macro level.

*To study how PT mobility patterns within/across towns differ 
*Relationship between PT mobility and factors such as town demography profile, job/worker ratio, and land use mix 

===Resources===
Concept Plan: https://www.ura.gov.sg/Corporate/Planning/Concept-Plan/Past-Concept-Plans 
New employment districts: 
https://www.ura.gov.sg/Corporate/Planning/Growth-areas/Punggol-Digital-District 
https://www.jld.sg/ 
 
Industry Input-Output Raw Files: https://www.singstat.gov.sg/find-data/search-by-theme/economy/national-accounts/latest-data 
Industry Input-Output 2010 Summary: https://www.singstat.gov.sg/-/media/files/publications/economy/io_tables_2010_publication.pdf 
Industry Input-Output Explanation: https://www.singstat.gov.sg/-/media/files/publications/economy/ssnmar15-pg9-14.pdf 
OECD Input-Output (for reference): https://www.dartmouth.edu/~rstaiger/OECD%20Input-Output%20Database.pdf 

===Interested Participants===
Shantal, Alex, Jared, Sanna, Kevin, Chris, Sarah B.

==The Evolution of Water Narratives in US Newspapers==
===Motivation===
The complex interactions between physical and social factors in water management have led to the emergence of a new field in socio-hydrology. Various dynamics are studied by socio-hydrologists including the influence of economics, culture, and institutions on behaviors related to water. This study focuses on improving our understanding of the evolution of social narratives in the water domain. 

===Data===
We have access to 500K+ newspaper articles (across 37 publications over 15 years) from the LexisNexis database that touch on water to some degree shape or form.

===General Approach===
The data provides a lot of opportunities for play! Given the text nature of dataset, we will draw heavily from natural language processing techniques (http://mschoonvelde.com/assets/pdf/Syllabus_CEU.pdf). Currently, we are thinking of exploring a variety of natural language processing (e.g., word2vec and sentiment) and network evolution techniques to help us characterize and understand the evolution of narratives. We can try to understand the impact of the these social narratives such as:
* legal behavior (by looking at cases field through LexisNexis)
* water conservation policies (Gilligan, J. G., Wold, C. A., Worland, S. C., Nay, J. J., Hess, D. J., & Hornberger, G. M. (2018). Urban water conservation policies in the United States. Earth's Future. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2017EF000797)

There are of course other directions the project can evolve. If you are interested, please put your name down below and join us on slack (#waternewspapers) to be a part of the conversation!

===Recommended Papers===
* Marelli, B. (2008). Common Pool Resources: the Search for Rationality through Values. Empirical Evidence for the Theory of Collective Action in Northern Italy. https://dlc.dlib.indiana.edu/dlc/bitstream/handle/10535/1344/Marelli_119601.pdf?sequence=1 (Think about how the newspapers and their narratives are affecting the capacity for collective action around shared pool resources)
* Boumans, Jelle W., and Damian Trilling. "Taking stock of the toolkit: An overview of relevant automated content analysis approaches and techniques for digital journalism scholars." Digital Journalism 4.1 (2016): 8-23.
* Denny, M. J., & Spirling, A. (2018). Text preprocessing for unsupervised learning: why it matters, when it misleads, and what to do about it. Political Analysis, 26(2), 168-189. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2849145
* Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content analysis in an era of big data: A hybrid approach to computational and manual methods. Journal of Broadcasting & Electronic Media, 57(1), 34-52.
* Atteveldt, V. (2017). Text Analysis in R. Communication Methods and Measures, 11(4), 245-265. http://kenbenoit.net/pdfs/text_analysis_in_R.pdf
* Greene, Z., Ceron, A., Schumacher, G., & Fazekas, Z. (2016). The nuts and bolts of automated text analysis. Comparing different document pre-processing techniques in four countries. https://osf.io/ghxj8/
* Azarbonyad, H., Dehghani, M., Beelen, K., Arkut, A., Marx, M., & Kamps, J. (2017, November). Words are Malleable: Computing Semantic Shifts in Political and Media Discourse. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 1509-1518). ACM. https://arxiv.org/abs/1711.05603
* Zhang, P., & Moore, C. (2014). Scalable detection of statistically significant communities and hierarchies, using message passing for modularity. Proceedings of the National Academy of Sciences, 111(51), 18144-18149. http://www.pnas.org/content/pnas/111/51/18144.full.pdf

I wanted to throw this out as a possible method... https://www.erikgjesfjeld.net/evolution-of-diversity.html -- mapping concepts to keywords, looking for changing frequencies through time ... should be easy toi create a spatial component.

===Interested Participants===
Thushara, Saska, Jenn, Inga, Xindi, Yuki, Sandra, Eleonara, Javier, Kevin, Allie, Vandana

==Reproducibility and Underdeterminacy in Mathematical Modeling==
===Motivation===
The reproducibility crisis has shaken the scientific world as many findings have failed to replicate in new experiments and datasets. At the same time, the rise of highly accurate predictive machine learning methods challenges the notion that we need deep scientific understanding in order to make predictions about the world around us. Will developing scientific theory still be necessary, or practicably justifiable, if we can just get enough data?

===General Approach===
There are several dimensions of this tension that we could explore: 
- We think of physics as the area that achieves the highest degree of predictive accuracy among all sciences. Can deep learning predict physical scenes more accurately than physical models? If not, perhaps there is still hope for science. If so, perhaps we need to rethink either the practice of physics or the authority of prediction. 
- Data is often brought to bear when trying to provide evidence for a mechanistic model. In order to establish firm evidence, strong alternative hypotheses must be specified. Yet in many cases, alternative models are not even compared, or when alternative models are compared, those alternatives are weak strawmen. Can typical datasets actually uniqely identify mechanistic models among alternatives? One approach to answering this question is to generate data according to known model (e.g., from published papers), and see if an analyst who does not know the true model can infer it, or to see if multiple different models provide equally good accounts of the data. 
- If we want to hold out hope that our scientific models are useful for prediction in the face of machine learning, perhaps we can productively combined structured scientific models with less structured machine learning approach, e.g., by predicting model residuals. Do structured models actually help in such a joint model above and beyong machine learning alone? 
- Can collecting richer data, such as finer-grain neural data or interviews / ethnographies in social science, help resolve any indeterminacy we identify, or would having richer data simply make machine learning more effective as an alternative?

Some additional random thoughts (in defense of science) by Jonas: 
- a lot of the flexible (low inductive bias) function approximators need a lot of (labeled) data; is this realistic in all/many/some scientific disciplines? For instance, in psychology there are fundamental limits to how many subjective measurements one can take of an individual on a given day, both in frequency and number of variables p; in such situation adding more inductive bias (e.g. through understandable parametric models) is possibly a good idea 
- convenience samples vs. samples from a proper sampling scheme (probably not a big problem in classifying cat pictures, but maybe a bigger problem in more contextualized phenomena) 
- observational data vs. experimentation 
- predictive models (function approximation) vs. causal models (building a model of the world); Pearl argues for the latter and against the former in his new book (but didn't read it) 
- in many situations it is not so interesting to predict variables, but to be able to come up with useful interventions on them; this is difficult in a black box approximators in which parameters do not map to concepts we have about the real world 

===Relevant Papers===

Kleinberg et al. (2017) The Theory Is Predictive, but Is It Complete? An Application to Human Perception of Randomness 
Youyou (2015) Computer-based personality judgments are more accurate than those made by humans 
Pearl and Mackenzie (2018) The Book of Why 

===Slack Channel ===

[https://csss18.slack.com/messages/CB7UELMQV/ link Slack]

===Interested Participants===
Pete K.,

Alice

Jonas

==Classifying language by grammatical motifs==

===General Idea===
Every once in a while when we get people from different countries sitting around a table (at CSSS for example!) and then we come across words, idioms, or concepts that we can't accurately translate from one language to another. There a lots of words that exist only in one language but not in others. Consequently, there are lots of concepts that exist only in one language but not in others. In this project, let us explore the differences between language by "higher-order grammatical structure", not just single words.

We can take a sentence and think of its structure as a small network (also called ''motif'') of words. Nodes are subjects and objects that are linked via verbs of prepositions (see for example https://en.wikipedia.org/wiki/Object_(grammar) ). Taking a text and counting the reoccurence of sentence structures, we can get a ''distribution of motifs''. Let us explore if we can use this distribution of motifs to characterise different texts. Comparisons could be
*between texts in different languages
*between British and American English
*between texts for different purposes (fiction/novels, news, scientific writing, policy, etc.)

Given the diversity of the CSSS crowd, this would be a unique opportunity to work on the comparison of texts in different languages!

The main challenge would be to develop a text mining algorithm that can give us the motif distribution for a text (in a given language). This project could benefit from
*expertise in computational linguistics, text mining, and machine learning
*a diverse team of people who speak different languages.

===Recommended Papers===
???

===Interested participants===
Alice
Vandana

==Structures in Open Source Software Communities==

===General Idea===

A lot of open source software projects organize through mailing list. This mailing list interactions in combination with for example data from github could give some insight in how those groups organize.
Possible interesting questions could include:
*How does the project size influence the structure.
*What members collaborate more/less?
*Who collaborates on specific code pieces?
*How does communication behavior influence the position of contributers in the community? (sentiment analyses? )
*... your ideas ...

===Existing Work in this Field?===

===Useful Methods===

===Data Sets===
* linux kernel https://lkml.org/lkml/2016/

===Interested participants===
Maria W
Cedric P

==Measuring information distortion in networks (rumors/fake news)==

===General Idea===
Analyzing analytically, numerically and experimentally how information get distorted in networks when passed between people.
The network is layered (people in one layer pass the message to people in the next layer). In-degrees and out-degrees are fixed (1,2,3...)

Possible parameters: error rate, degree, length of chains, number of agents, speed of news propagation (internet vs newspapers etc.)

===Interested participants===
1. Javier 
2. Pete 
3. Zohar 
4. Cedric 
5. Guillaume 
6. Allie? 
7. Yuki 
8. Jonas 
9. Yanchen 
10. Jarno 
11. R Maria 
12. Josefine 
13. George 

===Data Sets===

* Safe Cast: Radiation and air quality data primarily for Fukushima and Tokyo https://blog.safecast.org/downloads/

==Measuring epigenetic effect of stress at a macro scale==

===General Idea===
Epigenetic processes describe environmental effects on genome expression/regulation which are transmitted to the next generations. In particular, recent research indicates that stress in human can have transgenerational effect. Can these epigenetic effects can be detected in data at a macro scale, for instance after a global stressful crisis (world war, etc..) ?

===Relevant papers===
1. Israel Rosenfield and Edward Ziff. "Epigenetics: The Evolution Revolution" The New York Review of Books (2018)

2. McGuiness et al. "Socio-economic status is associated with epigenetic differences in the pSoBid cohort" International Journal of Epidemiology (2012)

2. Uddin et al, "Epigenetic and immune function profiles associated with posttraumatic stress disorder". Proceedings of the National Academy of Sciences (2010)

3. Borders et al. "Chronic stress and low birth weight neonates in a low-income population of women." (2007)
DOI: https://doi.org/10.1097/01.AOG.0000250535.97920.b5

4. Miller GE, Chen E, Parker KJ. Psychological Stress in Childhood and Susceptibility to the Chronic Diseases of Aging: Moving Towards a Model of Behavioral and Biological Mechanisms. Psychological bulletin. (2011). doi:10.1037/a0024768.

5. Jack P. Shonkoff, Andrew S. Garner. "The Lifelong Effects of Early Childhood Adversity and Toxic Stress." Pediatrics. (2012), DOI: 10.1542/peds.2011-2663

===Interested participants===
1. Cedric P 
2. Sarah B. 
3. Chathika G. 
4. Simon J. 
5. Kofi K (background in bioinformatics, data-mining, behavioral psychology, microbiology)

===Data Sets===
1. ???

==Robustness of the presidential information cascade on Twitter==

===General Idea===
How does information dissemination change when Drumpf blocks other users on Twitter?

===Interested participants===
Alice

==Topology of natural conversations==

===General Idea===
Everyone who belongs to a Whatsapp political discussion group (or any other discussion group regarding a specific topic) knows that consensus is difficult to reach. People seem to go back and forth in their arguments trying to convince others of their own views. Looks like a dynamical system to me! I would like to use what we learned from Joshua's talk and what we will learn from Simon deDeo's lectures to represent each text sent as a point along a one dimensional opinion continuum. The state of the conversation can then be represented as a point moving along the state space composed of every person participanting in the conversation. Is there an attractor? is it a strange attractor? What is its topology? How does that topology look like when people are arguing versus when they are planning or simply chatting? Hit me up if you are interested!

===Interested participants===
1. Niccolo (proponent) 
2. Cedric 
3. Yuki
4. Simon 

==Scaling of information requirements in living things==

Information about the environment is a resource that organisms must take in and process to survive, just like energy/nutrients. Inspired by West's talk, I wonder how this requirement might scale as a function of mass. Bacteria sense chemical concentrations in their environments, while more advanced organisms process increasingly sophisticated kinds of information (visual, social, and so on). However, we can simply ask how many bits per unit time are required by various creatures. By analogy with the principles underlying metabolic scaling, I would guess that bigger organisms are able to do more with less because larger networks might allow for greater processing power. On another level, innovations in processing like the emergence of nerves and brains might change that picture.

The nice thing about this project is that I think it ought to be relatively easy; if we read enough existing papers I think we should be able to produce reasonable estimates of information requirements, and there will be a story behind the answer one way or another.

===Thoughts? Recommended Papers?===

===Interested Participants===
1. Elan (proponent) 
2. Yanchen 
3. Subash 
4. Kofi K (background in (bioinformatics, data-mining, microbiology & genomics) 
5. Conor (background in information theory)

==Game of Coins: Developing a Robustness Analysis Tool for Decentralized Cryptocurrency Networks==
===Game Theory and Decentralized Governance Models===

===Changing the Data Paradigm: New Models in Data Ownership===
===Information Asymmetry in Distributed Systems: A Common Currency===
===Summary===
Creating a tool that is based on a set metrics derived from available network data that would determine robustness and health of public decentralized cryptocurrency networks.

Since the inception of Bitcoin in 2009 there has been a huge rise in the development of decentralized networks (and centralized networks), with each Coin there is a network behind that Coin. However since some (not all) of these networks are p2p based there are user thresholds that make certain networks (Coins) viable and secure (51%, DoS, Sybil ect).

Bitcoin is described as the most robust and secure financial network amongst cryptocurrency networks however there are thousands of other networks competing for some sort of slice of the market.

Of these other networks battling each other, (Bitcoin is generally categorized as a payment network), there are many viable use cases for decentralized networks (Coins) beyond payment networks:
*Decentralized data market place
*Tokenized securities
*Governance models
*Stable digital currency
*Lending
*Distributed computing

===Potential Data===
*Example of a decentralized open source coin explorer: http://explorer.threeeyed.info/info
https://coinmetrics.io/data-downloads/ 
https://onchainfx.com/ 
https://bitinfocharts.com/ 
https://coin.dance/nodes 
https://dappradar.com/ 

===Suggested Literature===
*New P2P Paradigm: https://www.hindawi.com/journals/misy/2018/2159082/
*Metcalfe Law in regards to Network Value: http://novel.ict.ac.cn/zxu/JournalPDF/Zhang_JCST_2015.pdf
*Governance Model Overview: https://blockchainconsultants.io/blockchain-governance-models/
*Governance Article of just one blockchain (Decred): https://www.cryptocompare.com/coins/guides/a-look-at-decreds-governance-system/
*Article on Tokenized Securities: https://medium.com/@apompliano/the-official-guide-to-tokenized-securities-44e8342bb24f

===Thoughts? Questions?===
*Possibility of doing other projects related to cryptocurrency? Data is widely available for decentralized networks.
*Segmenting into difference governance models
*Energy Consumption and GPU sells metrics/modeling

===Interested Participants===
1. Jared Edgerton 
2. Laura Mann 
3. Alice Schwarze 
4. Chris Fussner 
5. Louisa Di Felice 

== Twitractors: What kind of non-linear dynamic attractrors exist across OSM discussions ==
Online social media discussions center around emotion-driven exchanges of information on current topics that participants often have considerable social and cognitive investment in. Typically, the participants on these discussions have both opposing and supporting views , leading to emergence of collective effects such as polarization or information cascades. The result is a "heartbeat" of emotion, signifying the global collective emotion among society regarding the topic under discussion.

In this project, we will explore this collective "heartbeat" over many topics on Twitter through non-linear time series analysis.

Join the discussion at #Twitractor on slack

=== Available Datasets ===
Twitter Firehose data with sentiment analysis.

=== Interested Participants ===
1. Chathika 
2. Laura 
3. Subash 
4. Evgenia 
5. Simon 

===Social Networks and International Relations===
===Summary===
This project draws from the logic of Paul Hooper's research on cooperation dynamics in communities and the fractal and scalar presentations. I think the interactions between countries follow similar social dynamics as families, hunter gatherer groups, organizations, and within countries. I would be interested in simulating conditions under which countries cooperate. I think there are clear analogs to periods of colonization, WWI, and WWII. Also, this approach would be novel to international relations research.

===Potential Data===
I thought this would be modeled with ABMs and referencing historical periods.

===Interested Participants===
1. Jared Edgerton 

==Fluctuations in correlated data, random variables or models==
When estimating observables (e.g. parameters) from datasets we need to quantify the error associated to our estimation in order to decide whether or not our estimation is statistically significant. In sets of correlated data, the correlations may produce fluctuations that affect the error of our estimators. In this project we are interested in studying how the fluctuations depend on the sample size in different sets of data, simulations or models that the participants bring.
In particular, when the fluctuations are anomalously suppressed, this phenomenon is known as ''hyperuniformity''. The fingerprint of these systems is the suppression of fluctuations on large scales, manifesting a regularity that is not apparent on short scales. It can be found in systems of any dimensions, examples are jammed packing systems, crystal-like materials and some biological tissues such as the chicken retina.

Some literature:
* hyperuniformity in buses: [https://www.quantamagazine.org/in-mysterious-pattern-math-and-nature-converge-20130205]
* foundations&examples: Torquato S. and Stillinger F. H., Phys. Rev. E, 68 (2003) 041113.
* hyperuniformity in jammed particle systems: L. Berthier, P. Chaudhuri, C. Coulais, O. Dauchot, and P. Sollich, Phys. Rev. Lett. 106, 120601 (2011).
* hyperuniformity in chicken retina: [https://www.quantamagazine.org/hyperuniformity-found-in-birds-math-and-physics-20160712/] and Jiao Y., Lau T., Hatzikirou H., Meyer-Hermann M., Corbo J. C. and Torquato S., Phys. Rev. E, 89 (2014) 022721.
* hyperuniformity in an avalanche model: Garcia-Millan, R., Pruessner, G., Pickering, L., & Christensen, K. (2017). ''Correlations and hyperuniformity in the avalanche size of the Oslo Model'', arXiv preprint arXiv:1710.00179.

==Understanding Cardiac Dynamics in Health and Disease (#cardio) ==

===Motivation===
Arrhythmias (abnormal electrical activity of the heart) are common cardiac diseases and are amongst the most common causes of impaired quality of life and death. I am particularly interested in two of the most complex cardiac arrythmias namely 1. atrial fibrillation (disorganized electrical activity in the upper chambers of the heart -i.e. atria- not lethal but very disabling) and 2. ventricular fibrillation (disorganized activity in the bottom part of the heart -i.e. ventricles- that is lethal). We have a minimal understanding of the mechanisms of these arrhythmias and our current therapeutic strategies (namely medications, implantable cardiac devices that can deliver electrical therapy and ablation procedures where we intentionally destroy heart tissue in specific areas of the heart) are relatively ineffective. The lack of effective treatments largely reflect the lack of our understanding of the fundamental mechanisms responsible for these arrhythmias.

===General Ideas===
*1. I have intracardiac recordings of patients that are in atrial fibrillation before and after a therapeutic procedure. These are spatiotemporal data of simultaneous recordings from 64 locations inside the heart. We could use these data to develop creative ways to either (a) understand the dynamics of the system and specifically phase transitions and changes in spatiotemporal structures (b) develop markers that predict the success of the procedure, (c ) identify locations inside the heart that would serve as "hot-spots" or would be critical for sustainment of the arrhythmia. 
*2. I have several toy models of cardiac arrhythmias. These models are simulations of reaction diffusion models (specific for cardiac dynamics) that give rise to solutions such as stable periodic activity, spiral waves, or wave breakdown with multiple daughter wavelets. These could be used for a more theoretical assessment of spatiotemporal phase transition. 
*3. Should any of the methods that we might come up ends up working, I plan to scale it up to large animal models and clinical (human) studies, in the near future and I would welcome your collaboration. 

===Specific Projects===
*1. Representation of intracardiac recordings as networks using horizontal visibility graphs: we plan to analyze both synthetic (simulation) data as well as real patient data. Our preliminary plan is to develop such networks and compare network characteristics between different states. 
*2. Use Koopman analysis to get an insight in the dominant spatiotemporal patterns that govern the dynamics of healthy and diseased heart rhythms. Similar to above we plan to analyze both synthetic (simulation) data as well as real patient data.

===Interested Participants===
*1. Konstantinos (Cardiology, Translational Research) 
*2. Antreas (Mathematics) 
*3. Anastasya (Physics) 

== Multi-scale Adaptive Systems ==

=== General idea ===

Many (all?) complex adaptive systems observed in nature seem to have a multi-level / hierarchical / multi-scale structure. Why is that? and what are the generic properties of that hierarchical/multi-scale structure?

'''Some Questions'''

* Q1. What is the nature of the relations between different scales of (complex) adaptive systems?
* Q2. What properties of these relations are essential to the dynamics of the system, both globally and at each scale/level?
* Q3. How do structural properties impact qualitative properties of the system, both globally and at each scale/level? (e.g. communication speed, robustness, ...)
* Q....

'''Some ideas:'''

* R1 & R2 (to Q1 & Q2): Some of the most interesting aspects seem to be:
** micro-macro abstraction (information loss) --> upward causation;
** macro-micro feedback and adaptation (macro control signals leading to micro adaptations) --> downward causation;
** different time scales between levels (e.g. faster adaptations at the mower levels than at the higher levels)
* R3 (to Q3à: study robustness, performance of coordination (e.g. speed of communication and/or of convergence)
* R.....

=== Contributions : 3 types ===

'''A. Reference to relevant work in different disciplines.'''

You worked with or know of a system that features some sort multi-scale feedback-driven structure

--> Please let me know about it and let us discuss, to identify the above properties instantiated in this particular system;

'''B. Domain-specific Application'''

Apply and explore the impacts of the above principles onto a system or application domain that you are working /interested in

--> Develop and play with an analytical model, or simulation, of an application-specific hierarchical feedback-driven system-of-systems.

Examples of possible application domains:
* Swarms of Swarms? "Controlled" swarms
* Hierarchical institutions, organisations, politics, rule/norm formation and evolution,...
* Micro-Macro economics, finance, behavioural economics, ...
* Networks of Networks (probably relevant to most/all of the above)

* Multi-level learning
* Multi-scale chemical reactions?
* Multi-scale biological systems
.....

'''C. General Theory'''

Extracting general principles, concepts, design patterns that apply across several system types.

Purpose: help understand, analyse and ** design ** complex adaptive systems with desirable properties (e.g. reaching local/global stakeholder goals; robustness; performance; security; reusability; flexibility/adaptability; etc)

Among other tools, we can use this '''simulator of a holonic cellular automata''' (HCA):
* videos of two configurations with different outcomes: [http://adadiaconescu.there-you-are.com/hca/hca-videos.html]
* project: [https://github.com/adadiaconescu/hca]
* details: see references (ALife 2018)

HCA simulation snapshot: 

[[File:HcaEx.png|300px]]
 

=== Interested? willing to share relevant existing work? or is your CSSS'18 project a possible application? ===

'''Participants:'''

Please add your name and the contribution(S) you're most interested in: A, B, C, ... all :)
or the link to the relevant work or project you'd like to share. Many thanks.

* Ada (A, B, C)
*
*
*
...

=== References ===

-- Herbert A Simon, "The Architecture of Complexity", in Proceedings of the American Philosophical Society, V. 106, No 6, December, 1962, pp.467-482
paper online: e.g., [http://www.cs.brandeis.edu/%7Ecs146a/handouts/papers/simon-complexity.pdf]

-- Jessica C. Flack, "Coarse-graining as a downward causation mechanism", Philosophical Transactions of the Royal Society, Volume 375, issue 2109, Nov 2017
paper online: [http://rsta.royalsocietypublishing.org/content/375/2109/20160338]

-- S. McGregor and C. Fernando, "Levels of description: A novel approach to dynamical hierarchies" ALife, 11(4), 2005
paper online: [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.101.5334&rep=rep1&type=pdf]

'''Some of my previous work:'''

-- Ada Diaconescu, Sven Tomforde and Christian Müller-Schloer, " Holonic Cellular Automata: Modelling Multi-level Self-organisation of Structure and Behaviour", ALife 2018, Tokyo, Japan
paper: [http://adadiaconescu.there-you-are.com/Papers/ALIFE2018/alife-cr_47diacones.pdf]

-- Ada Diaconescu, Sylvain Frey, Christian Müller-Schloer, Jeremy Pitt, Sven Tomforde, "Goal-oriented Holonics for Complex System (Self-)Integration: Concepts and Case Studies", SASO 2016, Augsburg, DE, pp 100-109
paper: [http://adadiaconescu.there-you-are.com/Papers/SASO2016/saso2016.pdf]

=List of all available datsets=
* Safe Cast: Radiation and air quality data primarily for Fukushima and Tokyo https://blog.safecast.org/downloads/
* Good Judgment Open scraped data: crowdsourced geopolitical forecasts from the Good Judgment Open website. Ask Niccolo
* Twitter Firehose data with sentiment analysis (Plutchik 's emotions and OCEAN personality traits) https://gitlab.com/caslab_ucf/Public/TwitterSenitmentLive.git . Ask Chathika for more info.

==Exploring Income Inequality From a Game Theoretic (or Other) Perspective:==

Many economic markets are fundamentally unfair and lead to high level of inequality. This has consequences for how people's opinions of fairness and trust develop and evolve. Data shows that an american citizen's likelihood of making their way from the bottom to the top is lower than that of citizens from other advanced countries. Data also shows that children born into "rich" families are more likely than not to remain rich. Literature also shows very strong demographic variations.

===Thoughts? Recommended Papers?===
Here is some relevant literature:
https://www.jstor.org/stable/pdf/3088921.pdf?refreqid=excelsior%3A1839833f8090beb4f9e3f37e55cbf6c0
http://web.mit.edu/14.193/www/WorldCongress-IEW-Version6Oct03.pdf
https://arxiv.org/pdf/1406.6620.pdf
http://cailinoconnor.com/wp-content/uploads/2015/03/CRKE-2.pdf

One idea is to consider a evolutionary game theoretic model that considers a stratified market (stratified into different income levels). Within each stratum, you could have various groups of agents corresponding to different demographics. The model could include some systemic barriers that may be unique to certain demographics. Agents could be self-interested, altruistic, spiteful, etc.

A non-game theoretic model could also work, so this is quite an open problem. If anybody else is interested in discussing this further, please contact Priya.

Another approach could be agent based modeling. 
Some literature: 
1. http://yildizoglu.fr/macroabm2/Submissions/15-Russo_et_al_Inequality_ABMacro.pdf 
2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4430112/ 

===Interested participants:===
- Priya 
- Carlos Marino 
- Cedric 

=Archived Projects ("Parking Lot")=
This section is for projects that we decide not to continue with. Maybe they're ideas that can be picked back up later (hence the "parking lot").

== Using Principles from Complex Systems in Thinking about AGI Development ==

AGI = Artificial General Intelligence, a catchphrase for "smarter-than-human" AI, a very misleading phrase which basically means algorithms which are generally capable of performing a wide range of tasks with high efficacy without being explicitly programmed to do each task.

For now, this is intentionally vague to keep open the various possibilities and gather together those who are interested. The project would move beyond current ML techniques, though, and either build on those techniques in significantly novel ways, propose new techniques, or consider from a theoretical standpoint how to design and train an agent (without specification of the implementation) which can perform a broad range of tasks "intelligently" and is aligned with human interests. An important focus is on ensuring alignment (doing what humans would want it to do), which is for various reasons quite hard to do both technically and philosophically.

There are two ways to use complex systems principles:
* In the design and training process of the algorithm
* In understanding how an algorithm will interact with the world around it

Specific project ideas:
* Building in an adaptive mechanism for an agent to adjust its input-output map as the dynamics of its environment change
* Using insights from various evolutionary processes to design a learning process that can produce an intelligent and aligned agent (either using existing AI techniques, or being implementation-agnostic and considering an arbitrary agent)

Feel free to add your name below, and any project ideas above! If we get a few interested people we can meet tonight or tomorrow.

===Interested Participants:===
* Luca Rade
* Nam Le

R1 & R2 (to Q1 & Q2): Most interesting aspects seem to be:
-- micro-macro abstraction (information loss) --> upward causation;
-- macro-micro feedback and adaptation (macro control signals leading to micro adaptations) --> downward causation;
-- different time scales between levels (e.g. faster adaptations at the mower levels than at the higher levels)
R3 (to Q3à: study robustness, performance of coordination (e.g. speed of communication and/or of convergence)
R.....

Complex Systems Summer School 2018-Projects & Working Groups

2018-06-14T19:59:08Z

Jenn: /* Recommended Papers */

{{Complex Systems Summer School 2018}}

=Projects=

== Using Principles from Complex Systems in Thinking about AGI Development ==

AGI = Artificial General Intelligence, a catchphrase for "smarter-than-human" AI, a very misleading phrase which basically means algorithms which are generally capable of performing a wide range of tasks with high efficacy without being explicitly programmed to do each task.

For now, this is intentionally vague to keep open the various possibilities and gather together those who are interested. The project would move beyond current ML techniques, though, and either build on those techniques in significantly novel ways, propose new techniques, or consider from a theoretical standpoint how to design and train an agent (without specification of the implementation) which can perform a broad range of tasks "intelligently" and is aligned with human interests. An important focus is on ensuring alignment (doing what humans would want it to do), which is for various reasons quite hard to do both technically and philosophically.

There are two ways to use complex systems principles:
* In the design and training process of the algorithm
* In understanding how an algorithm will interact with the world around it

Specific project ideas:
* Building in an adaptive mechanism for an agent to adjust its input-output map as the dynamics of its environment change
* Using insights from various evolutionary processes to design a learning process that can produce an intelligent and aligned agent (either using existing AI techniques, or being implementation-agnostic and considering an arbitrary agent)

Feel free to add your name below, and any project ideas above! If we get a few interested people we can meet tonight or tomorrow.

===Interested Participants:===
* Luca Rade
* Nam Le

== Neural style transfer in music styles via interacting agents==
'''General idea''': A) learn generative models of different music styles using neural networks. B) let these networks ('agents') interact and see what `fusion' music styles result.

'''Relevant papers''':
1) neural style transfer for images (make images look like Van Gough paintings etc.) : https://tinyurl.com/ybpq5agm

2) neural nets for music: https://tinyurl.com/yb2qdqbq and http://imanmalik.com/cs/2017/06/05/neural-style.html, https://magenta.tensorflow.org/performance-rnn

3) bunch of theories of how music styles are results of combination: https://tinyurl.com/y723ugyo

4) music recommendation using neural networks (from Spotify): http://benanne.github.io/2014/08/05/spotify-cnns.html#predicting, https://papers.nips.cc/paper/5004-deep-content-based-music-recommendation

'''Novelty''' lies in having the a) multiple agents learn multiple styles independently then letting them exchange information in a meaningful way (probably the trickiest bit) and b) letting these fusion music styles evolve in a network etc. and see what "world-music" results at the end for example.

'''Details''' will come...

=== Potential Data ===
*
===Thoughts?===
* we could also use text corpora instead? Shakespeare etc.
* ...

===Interested Participants:===
* Yuki
* Vandana
* Xindi
* R Maria
* Kevin
* Priya
* Ricky
* Chris

==Optimal representations of high dimensional data in deep learning and biological systems:==

What is the best way for a system to represent very high dimensional data? For example, how should the retina encode visual stimuli in neuron firing patterns? How does the immune system encode the space of antigens it might encounter? In each case, it would not be feasible (or efficient) to create a unique tag for each input. Rather, the systems in question must decide which features in the stimuli are most relevant, and trade off between specificity and generality.

Along these lines, there are two more specific questions to investigate:

-It has recently been conjectured that the success of deep learning networks is related to their optimization of a specific informational quantity in each layer https://arxiv.org/abs/1710.11324. Unfortunately this paper is not very clearly written, but basically the idea is that when binning inputs into representations, the distribution of bin sizes should be given by a specific power law, which optimizes the aforementioned information measure. Do biological systems employ the same strategy? With access to the right data, this idea should be straightforward to test. For example, if we have a list of antibodies together with the set of antigens they react to, we can compute this quantity and see whether the antigen "bins" are indeed distributed according to the predicted power law.

-A diverse collection of biological systems that are faced with this task seem to be well-modeled by maximum entropy distributions, with a constraint on pairwise correlations and parameters (i.e. lagrange multipliers) set near a critical point https://arxiv.org/pdf/1012.2242.pdf. This has been applied to the previously given examples of the retina and the immune system, as well as flocking in birds. As far as I know, it is not yet known with certainty whether this kind of encoding scheme is optimal in some sense (like in the previous bullet), or if it is an artifact of our own inference methods, but I think the answer is interesting either way. An immediate question is, if these maximum entropy models are a powerful tool for humans to model high dimensional systems, might biological systems also be producing their own maximum entropy models of environmental variables? That is, are maximum entropy models with constraints on pairwise correlations optimal in some information-theoretic sense, which can be made precise? For example, would this be a particularly useful way to model the distribution of natural images one might encounter? While less straightforward than the previous bullet, I think these are questions well-suited to the skills of the people here, and I think we could make significant progress!

If anyone has expertise to offer, your feedback/participation would be very much appreciated! In particular, I think this project would greatly benefit from those of you that have knowledge in machine learning and biology (my own area is physics and information theory). Feel free to email me at e.stopnitzky@gmail.com

===Thoughts?===
A '''genetic model''' with the resulting '''protein products''' could also be useful here (e.g. looking at expression levels and/or variants in a particular gene or set of genes as it pertains to the protein(s) coded by the aforementioned gene(s). In sum, can we find/demonstrate an algorithmic basis for gene expression and/or protein coding? - Kofi

===Interested participants:===
- Kofi Khamit-Kush (Background in Biology, specifically Cancer Genomics and data mining). kkhamitk@gmail.com 
-Jacob 
- Yuki 
- Alice 
- Sarah B. (experience with sequencing data/gene expression) 
- Subash 

==The Emergence and Evolution of Legal Systems as Pertaining to Water Distribution==
===General Idea===
There are numerous legal systems that have been identified, broadly categorized into large families – Common Law (Anglosphere and Commonwealth nations), Civil Law (Romance Language nations, Germany, China), Islamic law (most Muslim nations), Customary Law (India, sub-Saharan Africa). More importantly, most nations do not purely lie in one category, but tend to combine elements of multiple systems, either due to merging (i.e. German law combining Germanic tradition with Civil traditions), or through subsidiarity (i.e. Louisiana having Napoleonic law, despite being in a Common Law nation). We are interested in determining how these legal systems by nations and states emerged, influenced each other, and interact over national boundaries. 
This is an immense task, so to scope it, one idea has been to limit this project to laws pertaining to water distribution. This is of particular interest when looking at states of nations that have different legal systems, such as Louisiana in the U.S., Quebec in Canada, and Scotland in the U.K. For international interactions, sub-Saharan African nations might also be of value in assessing, as many nations border nations with different legal systems, and water is often a scarse resource in these areas. 
If anyone has interest in this topic, and/or expertise in either legal systems or water distribution, feel free to sign up or discuss. 
===Recommended Papers===
Energy and Efficiency in the Realignment of Common-Law Water Rights, Carol M. Rose, The Journal of Legal Studies 1990 19:2, 261-296 
Theories of Water Law, Samuel C. Wiel, Harvard Law Review, Vol. 27, No. 6 (Apr., 1914), pp. 530-544 

===Interested Participants===
1. Kevin Comer 
2. Cedric Perret
3. Chris Fussner
4. Jared Edgerton

== Academic hiring networks ==
=== General idea:===
I am thinking about doing something around academic hiring networks in different disciplines and to play around with idea of multilevel networks (e.g. look at the interplay between different institutional norms in various disciplines and hiring dynamics). Also, would be cool to have a look on interplay between publishing / hiring networks.
We could also explore other ideas related to the academia theme like exploring factors that excellence / equality tradeoffs, or factors that promote gender balance in science, etc. 

Who would be interested? 

I've created the channel #hiring_networks at slack. 

=== Literature:===
* A. Clauset, S. Arbesman and D.B. Larremore. 2015. Systematic inequality and hierarchy in faculty hiring networks. Science Advances 1(1), e1400005 (2015).

===Interested Participants===
1. Evgenia (Background in social network dynamics, psychology and organisation science) 
2. Ricky (Background in multilayer networks, network resilience, machine learning) 
3. Allie (Background in networks, science of science, gender) 
4. Carlos Marino.(Background in network optimization) 
5 . Andrea (Background in networks, multiplex and multilayer networks, information theory) 
6. Sanna (Background in networks and various social science disciplines) 
7. Conor (Background in information theory) 
8. Subash (Background in information theory - transfer entropy in specific, experimental design) 
9. Patricia (Background in modeling dynamical systems, agent-based modeling, experience working on academic search committees) 
10. Javier 
11. Saska 

== Make deep neural networks more biologically accurate by including inter-neural travel times==
=== General idea:===
Make deep neural networks more biologically accurate by including inter-neural travel times. Train with some normal task like digit-recognition.

=== Motivation:===
* Currently, deep neural networks only share some similarity to actual neurons: threshold behavior and hierarchical representations.
* However, in real neural networks, signals travel with finite speed and activations are integrated over time
* This ignored aspect could be one reason why real neuronal networks/brains are superior
* Further connecting the two fields of neuroscience and deep learning would be pretty cool
* We could use the "regular" neural network machinery to optimize weights etc for tasks like forecasting/image recognition and then see whether we find neural avalanches and chaotic behavior etc.

=== Details (first ideas):===
* In artificial neural networks, different neurons are connected by weights. To this, we add another connection between the neurons: the inter-neuron travel time.
* The inter-neuron travel time is computed by a RNN
* inference works by letting the network oscillate/ come to an equilibrium
* activation of neuron i at time t: a_i(t) = sum_over_connnected_neurons [f(a_j(t)) * delta(rnn(j->i)-t ) + exp(-lambda*t) f(a_i(t))], where delta is the Kronecker delta.
* I.e. the signal from connected neurons arrives at the time specified by the RNN and then slowly decays with exponent lambda
* if the RNN just gives t=1 for all travel times, this essentially reduces the normal deep neural net output.

== Evolution of social norms as a process within or between societies ==
=== General idea ===
Currently, there are two ideas floating around:
*How do social norms evolve *within* a society? Method-wise this is perhaps be related to the spread of ideas/information on a social network. The agents in this network are people. Potentially relevant models: Opinion formation, infectious (disease) spread and/or games on social networks.
*Think of a whole society (group/tribe/nation/etc) as an agent. A society may adopt or discard various social norms over time. If one of the chosen social norms (or a combination of the chosen combination of social norms) is woefully impractical it decreases the "fitness" of the whole society and it loses members/power/resources/territory to competing societies. Potentially relevant models: Models for evolutionary game theory.

The project can focus on (1), (2), or both.

====Branch: Agent Based Models and System Dynamics====

This branch seeks to use the 2 tools of ABMs and SD to further understand how social norms emerge through individual interaction from the bottom up(ABM) and how governing mechanisms then influence and shape those social norms from the top down (SD). Ideally this will even allow individual agents to select between emergent social norms and governing institutions which then further influences the feedbacks and system behavior.

The current challenge is finding a parsimonious construct and identify the key elements of this model to create the desired dynamics and analyze the subsequent behavior.

Interested in Branch : Tom

===Recommended Papers===
For (1):
* Ostrom, Elinor. "Collective action and the evolution of social norms." Journal of economic perspectives 14.3 (2000): 137-158.
* Sethi, Rajiv, and Eswaran Somanathan. "The evolution of social norms in common property resource use." The American Economic Review (1996): 766-788.
* Centola, Damon, et al. "Experimental evidence for tipping points in social convention." Science 360.6393 (2018): 1116-1119.

For (2):
* Powers et al, "How institutions shaped the last major evolutionary transition to large-scale human societies" Phil. Trans. R. Soc. (2016)

===Interested Participants===
Alice, Vandana, Alan, Xindi, Jenn, Matt, Sandra, Kevin, Alex, Cedric, Thushara, Subash, Josefine, Tom

== Topological features of neutral networks in evolution ==
=== Introduction ===
In a genotype network, nodes are genotypes and a link from genotype A to genotype B indicates that they are separated by a single mutation. Each genotype has a phenotype associated with it. In a fixed environment, a phenotype is associated with a fixed fitness value. So for every node, one has:

GENOTYPE -> PHENOTYPE -> FITNESS VALUE

The fitness values form a "fitness landscape", in which one can embed the genotype network. The set of nodes in a genotype network that corresponds to the same fitness value are a *neutral network*. These networks have received little or no attention from network scientists. Let's change that!
=== General idea ===
Depending on the interest of participants, this project could focus on (1) data analysis or (2) network theory.

(1) Szendro et al. mention that empirical data for genotype networks and their neutral networks is available. This is a somewhat new development (<10years). One could scout for one or several available data sets and study the topology of the networks. For example,
* what are topological characteristics of genotype networks? Can these characteristics be explained by constraints of embedding on a curved manifold? (One could compare data to random graph models, e.g. Erdos-Renyi, small world, or geometric random graph models.)
*how are neutral networks for high or low fitness values different?
*one could also think of the genotype network as a multlayer network with a lot of layers ... and analyse its topology from a multilayer perspective.

(2) A neutral network is a "level-set network" in the genotype network. The genotype network is a network that is embedded in a curved manifold in a high-dimensional space. There is so much cool math/physics/topology that one could do with this!!
=== Recommended Papers===
*https://en.wikipedia.org/wiki/Neutral_network_(evolution)
*Szendro, Ivan G., et al. "Quantitative analyses of empirical fitness landscapes." Journal of Statistical Mechanics: Theory and Experiment 2013.01 (2013): P01005.
*De Visser, J. Arjan Gm, and Joachim Krug. "Empirical fitness landscapes and the predictability of evolution." Nature Reviews Genetics 15.7 (2014): 480.
*Kondrashov, Dmitry A., and Fyodor A. Kondrashov. "Topological features of rugged fitness landscapes in sequence space." Trends in Genetics 31.1 (2015): 24-33.
===Interested Participants===
Alice

Ricky

Carlos

Sarah B.

George

Luca

Kofi K. (background in cancer genomics, data mining, and bioinformatics tools)
kkhamitk@gmail.com

==Networks from thresholded normally distributed data==

===Observations:===
- real-world networks are often created by thresholding dyadic interaction; 
- lots of things are approximately normally distributed. 

===Idea:===
- Suppose for each pair of nodes, i and j, there is a normally distributed interaction: x_ij ~ Normal(0,1); 
- Then, we place edges between nodes i and j whenever x_ij>threshold; 
- Edge correlations could be controlled by a single parameter, i.e. Cov(x, y) = beta . 

===Conjecture:===
- The resulting degree distributions have two limiting forms, and are approximately Poisson or power law(ish) (+ exponential cut-off), with something intermediate inbetween (log normal?)

===Things to look at:===
- Can we solve for the degree distribution of this model? 
- Does this degree distribution look like real networks? Can we fit the model easily (e.g. maximum likelihood or method of moments) 
- What about the giant component phase transition? 
- Does clustering vanish in the limit of large network size? 

This would be a more mathematical/theoretical project, and less about real world data. 

===Interested participants:===
- George (background in physics and networks) 
- Alice 
-Yanchen 
-Guillaume 
-Jordan 

==The Evolution of Beliefs in Abrahamic Religions==
===General Idea===
One commonality across all Abrahamic faiths – Judaism, Christianity, Islam, and others – is its reliance on the written word to solidify and codify beliefs, even centuries after the text was documented. Because of this large time difference between when documents were written – Torah, New Testament, Qu’ran – and when these beliefs grow and evolve, decisions are often linked to other texts as justification for the decision. For instance, when Ecumenical Councils declare a new testament of faith, they often point to previous texts from church fathers for justification (or sometimes non-believers, like pre-Christian Greek philosophers). Similarly, when imams declare testaments of faith, these are often linked to the hadiths and sirahs as justification. Canon law and Islamic law is based on these two dynamics respectively. Religions often influence each other, both as attractors (Islam prompted Iconoclasm in Eastern Christianity) and repulsors (Early Christianity set itself in opposition to Judaic practices, despite being considered a Jewish sect). 
===Recommended Papers===
===Interested Participants===
1. Kevin 
2. Carlos Marino 
3. Pete K. 

==City as a Complex System: Clustering/Mobility Network Effects==

===Introduction===
Cities are complex systems within which many sub-systems develop, interact and evolve. The understanding of how different systems within a city interact and connect with each other can help inform better urban planning decisions, to support different communities and ecosystems.

Through this study, we aim to gain insights on human choices, development of ecosystems, and spatial distribution in a city. Data from Singapore is available as a case study.

===Research Questions===
Some possible research questions are listed below, but feel free to add on any ideas related to this topic and we can discuss how to go from there!

Possible research questions (open to more ideas!): 
a. Business Clustering & Flow of Capital (human and/or monetary) between Industries

Motivation: To better distribute jobs closer to homes, a polycentric structure can be developed to establish multiple employment nodes in different areas of a city. Understanding of how business ecosystems develop and factors to support successful business/industrial clusters can help inform the strategies to establish and facilitate the growth of polycentricity.

*Are there clustering effects for business/industries across different sectors and how can this be measured/analyzed 
*What are the implications of clustering on the performance of businesses and industries? 
*What are the drivers to facilitate a sustainable business ecosystem? 

b. Intra-city Public Transport (PT) Mobility Patterns

Motivation: The study of people’s PT mobility patterns within a city allows us to understand human movement, choice and interactions. Through understanding mobility patterns in relation to the built environment and demographic make-up of different areas, we can gain insights to the human-environment relationship. This facilitates the formulating of more informed policy decisions and urban planning strategies to cater to the needs of the society on a local and macro level.

*To study how PT mobility patterns within/across towns differ 
*Relationship between PT mobility and factors such as town demography profile, job/worker ratio, and land use mix 

===Interested Participants===
Shantal, Alex, Jared, Sanna, Kevin, Chris, Sarah B.

==The Evolution of Water Narratives in US Newspapers==
===Motivation===
The complex interactions between physical and social factors in water management have led to the emergence of a new field in socio-hydrology. Various dynamics are studied by socio-hydrologists including the influence of economics, culture, and institutions on behaviors related to water. This study focuses on improving our understanding of the evolution of social narratives in the water domain. 

===Data===
We have access to 500K+ newspaper articles (across 37 publications over 15 years) from the LexisNexis database that touch on water to some degree shape or form.

===General Approach===
The data provides a lot of opportunities for play! Currently, we are thinking of exploring a variety of natural language processing (e.g., word2vec and sentiment) and network evolution techniques to help us characterize and understand the evolution of narratives. There are of course other directions the project can evolve. If you are interested, please put your name down below and join us on slack (#waternewspapers) to be a part of the conversation!

===Recommended Papers===
I think it would be useful to think about how the newspapers and their narratives are affecting the capacity for collective action around shared pool resources... this might be useful towards that: https://dlc.dlib.indiana.edu/dlc/bitstream/handle/10535/1344/Marelli_119601.pdf?sequence=1

===Interested Participants===
Thushara, Saska, Jenn, Inga, Xindi, Yuki, Sandra, Eleonara, Javier, Kevin, Allie, Vandana

==Reproducibility and Underdeterminacy in Mathematical Modeling==
===Motivation===
The reproducibility crisis has shaken the scientific world as many findings have failed to replicate in new experiments and datasets. At the same time, the rise of highly accurate predictive machine learning methods challenges the notion that we need deep scientific understanding in order to make predictions about the world around us. Will developing scientific theory still be necessary, or practicably justifiable, if we can just get enough data?

===General Approach===
There are several dimensions of this tension that we could explore: 
- We think of physics as the area that achieves the highest degree of predictive accuracy among all sciences. Can deep learning predict physical scenes more accurately than physical models? If not, perhaps there is still hope for science. If so, perhaps we need to rethink either the practice of physics or the authority of prediction. 
- Data is often brought to bear when trying to provide evidence for a mechanistic model. In order to establish firm evidence, strong alternative hypotheses must be specified. Yet in many cases, alternative models are not even compared, or when alternative models are compared, those alternatives are weak strawmen. Can typical datasets actually uniqely identify mechanistic models among alternatives? One approach to answering this question is to generate data according to known model (e.g., from published papers), and see if an analyst who does not know the true model can infer it, or to see if multiple different models provide equally good accounts of the data. 
- If we want to hold out hope that our scientific models are useful for prediction in the face of machine learning, perhaps we can productively combined structured scientific models with less structured machine learning approach, e.g., by predicting model residuals. Do structured models actually help in such a joint model above and beyong machine learning alone? 
- Can collecting richer data, such as finer-grain neural data or interviews / ethnographies in social science, help resolve any indeterminacy we identify, or would having richer data simply make machine learning more effective as an alternative?

Some additional random thoughts (in defense of science) by Jonas: 
- a lot of the flexible (low inductive bias) function approximators need a lot of (labeled) data; is this realistic in all/many/some scientific disciplines? For instance, in psychology there are fundamental limits to how many subjective measurements one can take of an individual on a given day, both in frequency and number of variables p; in such situation adding more inductive bias (e.g. through understandable parametric models) is possibly a good idea 
- convenience samples vs. samples from a proper sampling scheme (probably not a big problem in classifying cat pictures, but maybe a bigger problem in more contextualized phenomena) 
- observational data vs. experimentation 
- predictive models (function approximation) vs. causal models (building a model of the world); Pearl argues for the latter and against the former in his new book (but didn't read it) 
- in many situations it is not so interesting to predict variables, but to be able to come up with useful interventions on them; this is difficult in a black box approximators in which parameters do not map to concepts we have about the real world 

===Relevant Papers===

Kleinberg et al. (2017) The Theory Is Predictive, but Is It Complete? An Application to Human Perception of Randomness 
Youyou (2015) Computer-based personality judgments are more accurate than those made by humans 
Pearl and Mackenzie (2018) The Book of Why 

===Slack Channel ===

[https://csss18.slack.com/messages/CB7UELMQV/ link Slack]

===Interested Participants===
Pete K.,

Alice

Jonas

==Classifying language by grammatical motifs==

===General Idea===
Every once in a while when we get people from different countries sitting around a table (at CSSS for example!) and then we come across words, idioms, or concepts that we can't accurately translate from one language to another. There a lots of words that exist only in one language but not in others. Consequently, there are lots of concepts that exist only in one language but not in others. In this project, let us explore the differences between language by "higher-order grammatical structure", not just single words.

We can take a sentence and think of its structure as a small network (also called ''motif'') of words. Nodes are subjects and objects that are linked via verbs of prepositions (see for example https://en.wikipedia.org/wiki/Object_(grammar) ). Taking a text and counting the reoccurence of sentence structures, we can get a ''distribution of motifs''. Let us explore if we can use this distribution of motifs to characterise different texts. Comparisons could be
*between texts in different languages
*between British and American English
*between texts for different purposes (fiction/novels, news, scientific writing, policy, etc.)

Given the diversity of the CSSS crowd, this would be a unique opportunity to work on the comparison of texts in different languages!

The main challenge would be to develop a text mining algorithm that can give us the motif distribution for a text (in a given language). This project could benefit from
*expertise in computational linguistics, text mining, and machine learning
*a diverse team of people who speak different languages.

===Recommended Papers===
???

===Interested participants===
Alice
Vandana

==Structures in Open Source Software Communities==

===General Idea===

A lot of open source software projects organize through mailing list. This mailing list interactions in combination with for example data from github could give some insight in how those groups organize.
Possible interesting questions could include:
*How does the project size influence the structure.
*What members collaborate more/less?
*Who collaborates on specific code pieces?
*How does communication behavior influence the position of contributers in the community? (sentiment analyses? )
*... your ideas ...

===Existing Work in this Field?===

===Useful Methods===

===Data Sets===
* linux kernel https://lkml.org/lkml/2016/

===Interested participants===
Maria W
Cedric P

==Measuring information distortion in networks (rumors/fake news)==

===General Idea===
Analyzing analytically, numerically and experimentally how information get distorted in networks when passed between people.
The network is layered (people in one layer pass the message to people in the next layer). In-degrees and out-degrees are fixed (1,2,3...)

Possible parameters: error rate, degree, length of chains, number of agents, speed of news propagation (internet vs newspapers etc.)

===Interested participants===
1. Javier 
2. Pete 
3. Zohar 
4. Cedric 
5. Guillaume 
6. Allie? 
7. Yuki 
8. Jonas 
9. Yanchen 
10. Jarno 
11. R Maria 
12. Josefine 

===Data Sets===

* Safe Cast: Radiation and air quality data primarily for Fukushima and Tokyo https://blog.safecast.org/downloads/

==Measuring epigenetic effect of stress at a macro scale==

===General Idea===
Epigenetic processes describe environmental effects on genome expression/regulation which are transmitted to the next generations. In particular, recent research indicates that stress in human can have transgenerational effect. Can these epigenetic effects can be detected in data at a macro scale, for instance after a global stressful crisis (world war, etc..) ?

===Relevant papers===
1. Israel Rosenfield and Edward Ziff. "Epigenetics: The Evolution Revolution" The New York Review of Books (2018)
2. McGuiness et al. "Socio-economic status is associated with epigenetic differences in the pSoBid cohort" International Journal of Epidemiology (2012)
2. Uddin et al, "Epigenetic and immune function profiles associated with posttraumatic stress disorder". Proceedings of the National Academy of Sciences (2010)

===Interested participants===
1. Cedric P 
2. Sarah B. 
3.
4.
5.

===Data Sets===
1. ???

==Robustness of the presidential information cascade on Twitter==

===General Idea===
How does information dissemination change when Trump blocks other users on Twitter?

===Interested participants===
Alice

==Topology of natural conversations==

===General Idea===
Everyone who belongs to a Whatsapp political discussion group (or any other discussion group regarding a specific topic) knows that consensus is difficult to reach. People seem to go back and forth in their arguments trying to convince others of their own views. Looks like a dynamical system to me! I would like to use what we learned from Joshua's talk and what we will learn from Simon deDeo's lectures to represent each text sent as a point along a one dimensional opinion continuum. The state of the conversation can then be represented as a point moving along the state space composed of every person participanting in the conversation. Is there an attractor? is it a strange attractor? What is its topology? How does that topology look like when people are arguing versus when they are planning or simply chatting? Hit me up if you are interested!

===Interested participants===
1. Niccolo (proponent)

==Scaling of information requirements in living things==

Information about the environment is a resource that organisms must take in and process to survive, just like energy/nutrients. Inspired by West's talk, I wonder how this requirement might scale as a function of mass. Bacteria sense chemical concentrations in their environments, while more advanced organisms process increasingly sophisticated kinds of information (visual, social, and so on). However, we can simply ask how many bits per unit time are required by various creatures. By analogy with the principles underlying metabolic scaling, I would guess that bigger organisms are able to do more with less because larger networks might allow for greater processing power. On another level, innovations in processing like the emergence of nerves and brains might change that picture.

The nice thing about this project is that I think it ought to be relatively easy; if we read enough existing papers I think we should be able to produce reasonable estimates of information requirements, and there will be a story behind the answer one way or another.

===Thoughts?===

===Interested Participants===
1. Elan (proponent) 
2. Yanchen 
3. Subash ,br.

==Game of Coins: Developing a Robustness Analysis Tool for Decentralized Cryptocurrency Networks==
===Summary===
Creating a tool that is based on a set metrics derived from available network data that would determine robustness and health of public decentralized cryptocurrency networks.

Since the inception of Bitcoin in 2009 there has been a huge rise in the development of decentralized networks (and centralized networks), with each Coin there is a network behind that Coin. However since some (not all) of these networks are p2p based there are user thresholds that make certain networks (Coins) viable and secure (51%, DoS, Sybil ect).

Bitcoin is described as the most robust and secure financial network amongst cryptocurrency networks however there are thousands of other networks competing for some sort of slice of the market.

Of these other networks battling each other, (Bitcoin is generally categorized as a payment network), there are many viable use cases for decentralized networks (Coins) beyond payment networks:
*Decentralized data market place
*Tokenized securities
*Governance models
*Stable digital currency
*Lending
*Distributed computing

===Potential Data===
*Example of a decentralized open source coin explorer: http://explorer.threeeyed.info/info
https://coinmetrics.io/data-downloads/
https://onchainfx.com/
https://bitinfocharts.com/
https://coin.dance/nodes
https://dappradar.com/

===Suggested Literature===
https://www.hindawi.com/journals/misy/2018/2159082/
http://novel.ict.ac.cn/zxu/JournalPDF/Zhang_JCST_2015.pdf
*Governance Model Overview: https://blockchainconsultants.io/blockchain-governance-models/
*Governance Article of just one blockchain (Decred): https://www.cryptocompare.com/coins/guides/a-look-at-decreds-governance-system/
*Article on Tokenized Securities: https://medium.com/@apompliano/the-official-guide-to-tokenized-securities-44e8342bb24f

===Thoughts? Questions?===
*Possibility of doing other projects related to cryptocurrency? Data is widely available for decentralized networks.
*Segmenting into difference governance models
*Energy Consumption and GPU sells metrics/modeling

===Interested Participants===
1. Jared Edgerton 
2. Laura Mann 
3. Alice Schwarze

=List of all available datsets=
* Safe Cast: Radiation and air quality data primarily for Fukushima and Tokyo https://blog.safecast.org/downloads/
* Good Judgment Open scraped data: crowdsourced geopolitical forecasts from the Good Judgment Open website. Ask Niccolo
* Twitter Firehose data with sentiment analysis (Plutchik 's emotions and OCEAN personality traits)

=Archived Projects ("Parking Lot")=
This section is for projects that we decide not to continue with. Maybe they're ideas that can be picked back up later (hence the "parking lot").

Complex Systems Summer School 2018-Speed Dating ++

2018-06-14T14:41:42Z

Jenn:

{{Complex Systems Summer School 2018}}

--> Using Multilayered Methods to Analyze Genes putative to triple-negative breast cancer sub-types

--> Niccolo & Kofi: FoldIt to assess Novel Mortalin variants coded by HSPA9 Pertaining to Triple Neg Breast Cancer sub-types

--> Using Social learning Tools based on evolutionary patterns to predict tumor cell learning based on cancer treatment type

--> Breast cancer incidence rates in women stratified by lifestyle and behavioral factors

--> Is there a Genetic basis for mental disorders? Can we use bioinformatics tools to find patterns of incidence?

--> George & Kofi: Using exact Bayesian algorithms to assess tumor cell networks by breast cancer sub-type

--> Pete & Kofi: Using Data-mining and bioinformatics tools to assess healthy learning environments for students at Ivy League Universities in the Massachusetts area

--> Quantifying the randomness of epidemic patterns

--> Quantifying proximal networks for mosquito-borne diseases

--> talia and cesar: The spread of diseases from migration due to political unrest in Venezuela: the Maduro effect

--> talia and jarno: Predicting ADHD using maternal psychological risk factors

--> Kevin & Ada: Multi-level Analysis of Sub-State Actors in International Politics

--> Evgenia & Ada: Co-evolution of Internal and External Ties within Organisations

--> Chris & Ada: Multi-scale Feedback Loops in Micro-Finance

--> Allie & Ada: Hierarchical Science of Science

--> Yanchen & Ada: Self-similarity for Network Aggregation

--> Gianrocco & Allie: Food for Research

--> Alex & Allie: The Effects of Urban Environments on Scientific Collaboration

--> Conor & Allie: Quantifying Multivariate Effects in Diversity

--> Talia & Allie: Characterizing the Heritability of Gender Norms across Academic Families

---> Maria & Jacob: Evolution of cooperative resource redistribution in microbial systems

---> Saska & Jacob: Plant microbial interactions reconsidered: All pay auctions force plants hands

---> Goerge & Jacob: The structural determination of consensus and diversity n GRN; How plants choose how to grow.

---> Ana & Jacob: Increasing the nutritional value of crops through redirection of resources towards fibrous tissues

---> Alice & Jacob: Analysis of protein-protein interaction networks at critical junctions for root zonation.

---> Sarah & Kevin: Changes in behavior of bacteria in response to different virus attack patterns

---> Matt & Kevin: Collective action problems with limited resources in congressional budgeting

---> Vandana & Kevin: Changes in agent behavior in iterated games with different rules for each period

---> Chris & Kevin: Sentiment analysis based on reactions to the stock market from text mining Twitter data (i.e. hedonometer research)

---> Patricia & Kevin: Simulation of flash crashes to test different threshold levels for control systems of automated stock trading programs

---> Anastasiya & Kevin: Identification of attractors in UN Voting Data from 1948 to the present

---> Tom & Kevin: Defining deviations of city size from Zipf's Law as a parameter of centralization (or decentralization) in a nation-state

Evolution of the evolution of evolution.

A framework for understanding anxiety in social situations.

Impact of fats moderated by scalar participation on generalised anxiety disorder

Eating chocolate cake reverses weight loss in major depression.

No connection between MRNA transcription levels and compulsive hand-washing

Repeated antisocial behavior modulates immune responses in face of radiation exposure

Evaluating effects of drought on savanna trees with respect to their phylogenetic distance

Sasha & Evgenia: Variation in synapse number in development -> effect on social network formation

Jordan & Sasha: Impact of brain inflammation on synapse stability as an adaptive network

Sasha & Nam: Supervised & Unsupervised learning in neural networks dynamics

Sasha & Konstantinos: Using Dynamic Mode Decomposition to evaluate patterns of activity of spontaneous neural waves in visual cortex before and after eye opening

Jordan & Xiaoyu: New mechanisms for synchronization in power grids with high renewable penetration

Jordan & Andrea: Quantifying perturbation spreading in multilayer systems using information theory

Ben & Jordan: Early warning signs for saddle-node bifurcations on temporal human contact networks

Alan & Jordan: Insights on multilayer networks from microbial metabolic networks

Optimization of industrial development paths to meet climate policy goals

Software development of a communication network for a swarm of robots

George and Luca: A generative model of mutualistic networks

Marina and Luca: Is a network always a network? A comparison of graph theoretic properties of social and biological networks under stress

Xindi and Luca: Modeling the publishing ecosystem using Lotka-Volterra system of equations

The heterogeneous effect of evolution of the spread disease on high and low income groups

Simon and Cesar: Comparing methods for water supply infrastructure for a case study in Ecuador and its economic and political outcomes

Vandana and Cesar: Fitness effects of high and low income groups under an epidemic

Yanchen and Cesar: How income levels affect network structure after an epidemic outbreak?
 
Matt & Ricky: k-Core Groups is Political Discussion Networks

Ariadna & Ricky: Thermodynamics of Multilayer Networks - Phase Transition as a Function of Network Connectivity

Conor & Ricky: Evaluating the Importance of Synergistic Interactions in Multilayer Networks

Magdalena & Ricky: Multilayer Innovation Networks

Anastasia & Ricky: Interplay between Topology and Nonlinear Dynamics in Oscillator Networks

Jarno & Ricky: Psychological Profile an a Factor in Multilayer Terrorist Networks
 
Alan & Evgenia: "Social dynamics of heterogeneous microbial populations"

Evgenia & Maria del Rio Chanona: "The evolution of multilayer network of labour unions and industry organisations"

Evgenia & Tom: "Altering networks to increase cooperation"

Ben & Evgenia: "How individuals novelty seeking affects contact switching in swinger network dynamics"

Evgenia and Peter: "Propagation of the negative and positive attitudes towards prescribing controlled substances in clinicians' social networks"

Vandana and Anastasiya: "Studying phase transitions in lattices using game theory in structured populations"

Jared and Marina: "Friend or Foe? Effects of political crises on friendship networks"

Maria R. and Marina: "Surviving the Big Crash: Impact of financial crisis on structure and evolution of social networks"

Ricky & Matt: k-Core Groups in Political Discussion Networks.

Kevin & Matt: Collective Action Problems with Limited Resources in Congressional Budgeting.

Anastasiya & Matt: Perturbation Spreading and Social Influence of Public Opinion in Political Networks.

Javier & Matt: Twitterbots as Opinion Leaders in the Spread Fake News.

Patricia & Matt: Using Facial Recognition to Track Networked Interaction Among Caucus Attendees.

Carol & Matt: Using Brain Signals to Predict Sociability in Discussion Networks.
 
Pride or punishment: When a new company moves to town

Investment policy impact on inequality

Emergence of social networks: comparing slack and self-reported contact data

Twitter bots as opinion leaders in the spread of fake news

Spread of gentrification in cities using multilayer network models

Taxation as a function of disorder in corporate structures

Measuring self-organization/productivity applying hidden Markov models to interevent communication data

Population dynamics and proximity to water

Inga & Gianrocco: 'Phones for Malaria'

Ariadna & Gianrocco: 'Modeling socio-temporal patterns from wireless wearable device'

Magdalena & Gianrocco: 'Sustainable innovation pathways for nutritive artificial meat production'

Jarno & Gianrocco: 'Autistic gut: food intolerance effects on impaired emotional intelligence'

How technology affected the neolithic society of south east asia

Network early warning signs of global financial crisis

An ABM of conflict due to robots stealing jobs. Jared & Maria

The innovation network of microbial communities. Luca & Maria

The evolution of the multilayer network of labor union and industry organization. Evgenia & Maria

Surviving the big crash -- the impact of financial crisis on the structure and evolution of social networks. Marina & Maria

Modeling influenza epidemic patterns with HMMs. Talia & Ariadna

Thermodynamics of a multilayer network. Ricky & Ariadna

Synergistic error correction in stochastic processes. Conor & Ariadna

Crowd Archeology and Ideas of Cultural Identity in Southeast Asia. Jenn & Chris

Urban Centers as Cultural Centers in Past, Present and Future. Jenn & Chris

Developing Micro Consensus Models on the Blockchain. Nicollo & Chris

Designing Governance for Large Groups. Nicollo & Chris

Defining Network Robustness Parameters in Decentralized Cryptocurrency Networks. Andrea & Chris

Bayesian Optimal Information Integration of wind measurements in wind farms: Niccolo & Xiaoyu Wang

Water system recover after exogenous event using networks and newspaper data: Thushara & Xindi

Spatial book sales pattern analysis: Konstantinos & Xindi

Replicator dynamics for interactions between multiple genomic loci to understand interactions on a genomic scale: Vandana & Sarah

Decomposing performance improvements in cells into biological innovation mechanisms: Magdalena & Sarah

The Effects of Urban Environments on Scientific Collaboration - Alex & Allie

Investigations into the Urban Collective Conscience - Alex & Juarno

NLP for ethnography of communication networks_Sandra&Yuki

Networks of communication in biological systems_Alan&Sandra

Financial connections proxied by information sharing_Eleonora&Sandra

How to play well with others: communication systems and human cooperation_Catriona&Sandra

Similarity/Differences in Public Transport Network Structure in Singapore and Belgrade

Adaptive Network in Transport Systems

Optimizing strategies for public transport systems in cities

Commonly considered factors sufficient to give rise to clustering in business networks

Mobility and access: Spatial dynamics of healthcare provision for senior population - Shantal & Peter

Reconstructing social network structure of past societies using Bayesian influence

Critical comparison between evolution of mutualism and evolution of cooperation

First order phase transition in human social organisation

How social hierarchy impacts inter-organisational collaborations

How did the emergence of financial system reinforce hierarchical organisation

Comparison of patterns of neuronal avalanches in social and non-social species

Coupling and decompling of biological oscillators - Subash and Kostantinos

Environemntal influences on endogenous dynamics - Subash and Thushara

Comparing information transfer in living systems; a comparative study between between slime molds and human population - Subash and Peter

Slime mold inspired search algorithms in swarm-robotics - Subash and Zohar

Dynamic mode decomposition of slime mold oscillators - Subash & ..

Using slime mold as a model organism to solve political conflicts - Subash and Jared

Egocentric networks and adverse opioid related events - Jared & Peter

Got Pills? Leveraging social networks for prescription medicines in disruption - Marina & Peter

Spatial Distribution of syringe supply and syringe sharing: A simulation study Peter & Saska

Huff & Kogan -- where am I going to go when the volcano blows: networks and survival after natural disasters

Huff & Perret -- climate change effects on evolution of tool use and form

Fussner & Huff -- public archaeology and ideas of cultural identity in island southeast asia

Fussner & Huff -- urban centers as sites of cultural exchange past, present, and future in ISEA

Edgerton & Huff -- evolution of networked social identity and conflict in eastern indonesia past and present

Huff & Stopnitzky -- practice and theory of navigation by stable ocean wave patterns

Huff & Schweikert -- local rural indonesian perspectives on reliable low-carbon energy access

Cantwell & Huff -- inferring trade network structure from isotopic analysis of ceramics and pig bones: with application to the iSEA Neolithic

June 14 Dome Showing

2018-06-14T14:39:30Z

Jenn:

{{Complex Systems Summer School 2018}}

==Thursday, June 14 Dome Showing==

===7:00pm===
1. Marina Kogan 
2. Sanna Ojanperä 
3. Saska Aloric 
4. Chathika Gunaratnec 
5. Ana Contreras Navarro 
6. Javier Garcia 
7. Pete Krafft 
8. Tom PIke 
9. Sasha Mikhailova 
10. Kevin Comer 
11. Thushara Gunda 
12. Yuki 
13. Xindi Wang 
14. Patricia Mellodge 
15. Alex Shannon 
16. Shantal Cheong 
17. Ada Diaconescu 
18. Vandana RV 
19. Rosalba 
20.Cesar 
21.Subash Ray 
22.Niccolo Pescetelli 
23. simon Jankowski 
24. Zohar 
25.Sandra 
26. Alice 
29. Gianrocco 
28. Evgenia 
29. George 
30.Xiaoyu Wang 
31. Konstantinos Aronis 

===8:00pm===

1.Priya 
2. Peter Geissert 
3. Guillaume St-Onge 
4. Cedric Perret 
5. Maria Waldl 
6. Luca Rade 
7. Yanchen Liu 
8. Sarah Berkemer 
9. Ricky 
10. Carlos Marcelo 
11. Eleonora Mavroeidi 
12. Alan Pacheco 
13. Catriona 
14. Kofi Khamit-Kush 
15. Nikunj Goel 
16. Louisa Di Felice 
17. Chris Fussner 
18. Inga Holmdahl 
19. Nam Le 
20. Jonas 
21.Maria 
22. jenn 
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24. 
25. 
26. 
29. 
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30. 

===9:00pm===

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