Santa Fe Institute Events Wiki - User contributions [en]

Complex Systems Summer School 2018-Tutorials

2018-07-10T21:21:27Z

CFinn: /* Resources */

{{Complex Systems Summer School 2018}}

Please use this space to organize any tutorial you would like to offer your peers. Thanks.

== Google Calendar ==

[https://calendar.google.com/calendar/embed?height=600&wkst=1&bgcolor=%23FFFFFF&src=5ukeqirhq8f847l3venbkecjb4%40group.calendar.google.com&color=%238D6F47&ctz=America%2FDenver Follow this link]

{{#iDisplay:https://calendar.google.com/calendar/embed?height=600&wkst=1&bgcolor=%23FFFFFF&src=5ukeqirhq8f847l3venbkecjb4%40group.calendar.google.com&color=%238D6F47&ctz=America%2FDenver}}

== Code Slam! ==
(organised by Alice)

Do you have an nice bit of code/ an app/ a jupyter noteboook that might be useful to other complex systems scientists? Pitch it in the code slam! Every volunteer gets five minutes to introduce their code or app to the group. We'll take a vote on the best presentation. The winner is going to get the invaluable CSSS Code Slam! trophy!!! ... which I will build from whatever materials that I find lying around at IAIA. :-)

===Suggested date and time===
Tuesday June 26, 8pm (Let me know if that time does not work for you!)

===Interested Participants===
(please indicate whether you would like to join as a presenter or audience)

Alice (presenter) 
Ben (presenter) 
Kofi (no code or app but let me know if you need help judging?) 
yuki 
Vandana 
Chathika (presenter) https://github.com/chathika/NL4Py https://pypi.org/project/NL4Py/#description

== Introduction to Machine Learning ==
===Suggested date and time===
Thu 21st 4:30-5:30 pm

=== Location ===
Lecture hall CLE

===Interested Participants===
(please indicate whether you would like to join as a presenter or audience)
* Yuki (presenter)
* Gianrocco (co-presenter)
* Kofi K. (audience)
* Alice (audience)
* Eleonora (audience)
* Louisa (audience)
* Ada (audience)
* Andrea (audience)
* Konstantinos (audience)
*Subash (audience)
* Xiaoyu (audience)
* Jared (audience)
* Ricky (audience)
*Vandana (audience)t
* Anastasiya (audience)
* Sanna (audience)
Simon (audience)

== Introduction to Deep Learning ==
===Suggested date and time===
Tue 21st 7-8pm

== Introduction to Recurrent Neural Networks ==
===Suggested date and time===
Thu 28th starting from like 5pm

===Interested Participants===
(please indicate whether you would like to join as a presenter or audience)
* Yuki (presenter)
* Kofi K (audience)
* Thushara (audience)
* Eleonora (audience)
* Ada (audience)
* Andrea (audience)
* Konstantinos (audience)
* Ariadna (audience)
* Xiaoyu (audience)
* Ricky (audience)
* Jared (audience)
* Anastasiya (audience)
* Sanna (audience)
* Xindi (audience)

==Physical theater==
Let's use our body and not just our brain! If you are interested in physical theater or simply just curious but with no previous experience, please sign up. I will go through some exercises involving body expression, movement and physical interactions.

https://en.wikipedia.org/wiki/Physical_theatre

=== Location ===
Dance studio - in the fitness center CONFIRMED!
=== date and time===
Tuesday 3rd July, 4:00 - 5:30PM
=== Interested participants===
*Niccolo (presenter)
*Yuki
* Gianrocco
* Jordan
* Jonas
* Eleonora
* Ada
* Evgenia
* Ariadna
* Subash
*Vandana
*maria
* Sanna

== Intro Improv Theatre ==
We will begin with some exercises to get out of our heads and to be aware of our fellow players. Then we build up towards the golden rule of improv (Yes, and ...) and finish with some short scenes! No prior experience needed!

https://en.wikipedia.org/wiki/Improvisational_theatre

I think 90min would be a good time.

=== Location ===
Dance studio - in the fitness center (to be confirmed)

=== Suggested date and time===
TBA

=== Interested participants===
*Jonas (presenter)
* R Maria
* Eleonora
* Ada
* Evgenia
* Ariadna
* Subash
*Vandana

== Introduction to ecological economics ==
The idea would be for me to give a 30 min overview more or less of the principles behind eco-eco: what it means, what makes it different from mainstream economics and environmental economics, how it is conceptualised and operationalised, etc. Then we can have a discussion/debate (can be more or less structured depending on how it goes, I'll prepare for something more structured if no natural debate arises) for 30 mins or however long people want.

=== Location ===
TBC

=== Suggested date and time===
Sometime in week 3

=== Interested participants===
1. Louisa (presenter) 
2. Shantal 
3. Jared 
4. Tom 
5. Cedric 
6. Kofi 
7. Cesar 

== Multi-scale integrated analysis of societal and ecosystem metabolism (MuSIASEM) ==
For those interested in an approach that describes the relations between funds and flows in societies and ecosystems (labour, water, land, energy, etc.) across different scales and hierarchical levels (https://en.wikipedia.org/wiki/MuSIASEM)

=== Location ===
TBC

=== Suggested date and time===
TBC

=== Interested participants===
* Louisa (presenter) 
* Ana 
* Thushara 
* Shantal 
* Tom 
* Alan 
* Xiaoyu 

== Simple VC overview ==

=== Location ===
Main Room of the Dorms

=== Suggested date and time===
9:00PM Wed, June 20th

=== Interested participants===

== Introduction to Statistical Physics of moving agents ==
I'll introduce stochastic differential equations which are a statistical physics tool that can be used to explore processes which we assume to be made up of some deterministic behaviour, and some stochastic (random) behaviour, which we call noise.
Noise can be interpreted in different ways depending on the application we wish to model (e.g. search strategies of foraging animals, stock market behaviour, gene regulatory networks).

This approach to stochastic processes will be different to the one presented by Srividya, although it can ultimately lead to the same 'distribution based' description. A nice thing about this approach is that it shows a strong link between the micro (fine scale) and macro (coarse scale) behaviour of systems.
I'll keep mathematics to a minimum and am happy to discuss finer details with anyone that cares.

=== Location ===
CLE auditorium

=== Suggested date and time===
Tuesday 26th 7pm?

=== Interested participants===
* Zohar (presenter) 
* Chathika (ignorant disciple) 
* Andrea 
* R Maria
* Ariadna
* Kofi 

==Managing Scientific Computing==

This tutorial will be split in two parts.

* Part 1) Managing large-scale computations using compute clusters and queueing systems (in particular SGE and PBS)
* Part 2) Plugging your custom C++-code to Python using pybind11 (and to Matlab using Matlab's C-wrapper)

github.com/benmaier/qsuite

https://github.com/pybind
=== Part 1 ===

Often times you find yourself writing some simulation code and end up with several free parameters. Now the problem is you wanna scan the parameter space and also sample a decent amount of times per parameter configuration. It is possible to scp your files to the server, write a job.sh script, submit that one, download the outputs, load the outputs and rewrap 'em to find the solutions you then want to analyze. Huge overhead.

I wrote a package/command-line tool (qsuite)[https://github.com/benmaier/qsuite] that deals with this typical problem.

=== Part 2 ===

What I've seen often is that people write custom C++-code only to compile it, feed data to it using the command line, then loading the output data to Python for analysis. This is an unneccesary overhead, as there exists a fairly simple framework to plug your C++-functions to a Python module that you can easily load and use in Python, called `pybind11`. I would give a short tutorial on how to use that one

Ben
(benjaminfrankmaier@gmail.com)

===Interested Participants:===
* Ben (presenter)
* Steph
* Alice
* Zohar
* Guillaume
* Kofi 
*
*

=== Suggested date and time===
Wednesday, 27th, 5pm

== COMPLETED EVENTS ==

== Python Agent Based Modelling- Mesa and Mesa Packages ==

Mesa is a relatively new ABM library based in the Python programming language. This tutorial will run through two quick ABMs using Mesa as well as a quick orientation of MESA API and GitHub page.

Mesa Packages is a new way to conceptualize ABM libraries with the idea of developing a repository of algorithms which can be integrated into ABMs. The goal is for the development of more complex models by having a rich repository of optimized algorithms which researchers can apply to different data or recombine in novel ways to explore a complex phenomenon. At the very least, it will hopefully make models easier to assemble and reduce the threshold so more people can build ABMs to support their research.

If you have any questions or thoughts please talk to me in person (Tom Pike) or send me an email tpike3@gmu.edu

Mesa API: https://mesa.readthedocs.io/en/master/overview.html

Mesa GitHub: https://github.com/projectmesa

Mesa Packages: https://github.com/projectmesa/mesa/wiki/Mesa-Packages

Mesa CSSS Tutorial: https://github.com/tpike3/CSSS_Mesa_tutorial

====Date, Time, Location (final)====
Date: Sunday June 17th

'''TIME CHANGE''' 
Time: 7:30
Location: 1st floor Conference Room (right across from cafeteria)

If you want to discuss some basic python, set up jupyter notebook etc I will be there by 6:30

''Based on a conversation it was requested to move the tutorial to Sunday evening, if you have issues please let me know
''

====Interested Participants====
1. Xiaoyu Wang 
2. Yuki 
3. Sanna 
4. R Maria 
5. Amy Schweikert 
6. Kevin Comer 
7. Thushara Gunda 
8. Kofi K 
9. Laura Mann 
10. Ariadna 
11. Eleonora 
12. Caroline Alves 
13. Jared Edgerton 
14. Allie 
15. 
== Agent-Based Model Analysis by Controlling NetLogo from Python with NL4Py ==

If you're interested in using Python libraries to analyze output from your NetLogo models you can do so with NL4Py.

You can get started with a simple: pip install nl4py

GitHub: https://github.com/chathika/NL4Py

Pypi: https://pypi.org/project/NL4Py/

Also, there are some Jupyter Notebook examples of using DEAP for calibration on multiple processors and SALib for sensitivity analysis via NL4Py here:
https://github.com/chathika/NL4Py/blob/master/examples/ParameterCalibrationWithDEAP.ipynb

https://github.com/chathika/NL4Py/blob/master/examples/SensitivityAnalysis.ipynb

It uses a different architecture from PyNetLogo, pushing the parallelization of headless workspaces to the JVM instead of leaving it to the Python application developer. NL4Py can help you to run thousands of NetLogo models under varying parameter configurations in parallel.

Please feel free to post any issues on the GitHub repository any arise!

Chathika
(chathika@knights.ucf.edu)

===Interested Participants:===
* JP (strictly novice Python skills but decent w/ NetLogo)
* Javier (strictly novice NetLogo skills but decent w/ Python)
* Xiaoyu Wang
* Ariadna (ok python/ no NL)
* Sanna
* Maria (ok python/ no NL)
* Amy (ok python, good with pandas library / little NL)
* Kevin (little python knowledge, decent knowledge with NetLogo)
-Kofi K. (limited Python experience, no NL)
* Eleonora (little Python, no NL)
* Ariadna (ok python/ no NL)

===Time===
By the poll results, 18th Monday 7:30 pm, works best.

== Structural robustness in networks ==
(organised by Alice)

The field of robustness and resilience in networks is wide and spans across many applications of complex systems: robustness in biological networks, cooperation and social networks, supply and trade networks, infrastructure, computing systems, etc. Speaking from experience, it is hard to navigate the literature on this topic and consolidate the many different jargons and the different notions of robustness and resilience. In this tutorial, I will
*share some ideas on how to navigate the field of network robustness and make sense of seemingly conflicting paradigms in the field,
*give an overview of some ideas on robustness in network theory
*have a discussion with you about the concepts of network robustness that exist in your field of study and how those may relate to notions of robustness in other fields.

For many questions concerning network robustness, there are no definitive answers. But I hope that this introduction can help you think critical about network robustness and navigate the litature quickly and with confidence.

===Suggested date and time===
Monday June 18, 7pm (Let me know if that time does not work for you!)

===Interested Participants===
* R Maria
* Luca
* Kofi K.
* Thushara
* Ariadna
* Sanna
* Cedric
* Jared Edgerton
* Andrea
* Eleonora
* Ada
* Evgenia
* Allie
*Subash

== Digital Trace Data (Web Scraping/API) ==
Practical demonstration on how to automatically scrape data from the web using several methods.

=== Suggested date and time ===
Tuesday 8PM

=== Participants ===

1. Jonas 
2. Andrea 
3. Louisa 
4. Inga 
5. Zohar 
6. Ana 
7. Thushara 
8. Evgenia 
9. Xiaoyu 
10. Alice 
11. Ariadna 
12. Subash 
13. Shantal 
14. Carlos 
15. Jared 
16. Chris 

== Introduction to Longitudinal Social Network Dynamics ==
(organised by Evgenia)

Are you curious about social network dynamics? This workshop gives an introduction to the statistical modelling of longitudinal social network data. We first explore why dynamic social network analysis is useful, and then look into a range of questions that could be answered within the model family of stochastic actor-based models (implemented in the RSiena package within R).

When this is useful:
- you want to focus on SOCIAL network dynamics
- the actors in the network have agency (they could decide over the course of action),
- there are multiple measurements (at least two) of the relationships within the same actor set.

Analytical possibilities are endless, e.g. you would like to explore
- how actor level characteristics affect evolution of relationships (creation and dissolution of new ties)
- how particular behaviour spreads in the network
- how what's going on within one type of network influences processes in another type of network (multiplex networks)
etc.

I will cover general considerations and point out useful resources.
In short: cool stuff.

===Suggested date and time===
UPDATE: The tutorial is shifted to 4 pm to avoid time conflict with experiment.
Wednesday June 27

MAIN LECTURE ROOM (where all of our classes take place)

===Interested Participants===

* Sanna
* Sandra
* Ariadna
* Thushara
===Further information===
Presentation posted on slack.

== Movement (Dance) Improvisation Tutorial/Workshop ==
(organized by Sarah H.) 

=== About the Tutorial ===
Thank you all for such a great improv dance tutorial yesterday! Had a wonderful time sharing the world of creative exploration and improvisation with you! 

This tutorial is an individual movement workshop that focuses on sensory (touch, sight, sound) exploration in the studio environment. No experience necessary! No contact with others… or if you are feeling ambitious - contact with others. More than likely, this will be an excellent opportunity to relax and "think" with your body… and let your mind rest. 

What does contact improvisational dance look like? Check out some of these youtube... 
Historical example of contact improvisation (we won't be doing this): https://www.youtube.com/watch?v=9FeSDsmIeHA 
And a more contemporary example: https://www.youtube.com/watch?v=nWfoGnT_etA
 

According to Wikipedia....: "Dance improvisation is the process of spontaneously creating movement. Development of improvised movement material is facilitated through a variety of creative explorations including body mapping through levels, shape and dynamics schema." https://en.wikipedia.org/wiki/Dance_improvisation 

Dance scholar Cynthia Novack suggests that movement improvisation informs us about the body's creative and physical response to social and political environments. With this in mind, I'll talk a little bit about the history of dance improvisation… and then will get you sensing and exploring through a series of movement "tasks" to deepen your sensory experience. 

Novack, C. (1988). Looking at Movement as Culture: Contact Improvisation to Disco. TDR/The Drama Review. Vol. 32. No. 4. Pp 102-119. https://ais.ku.edu.tr/course/19352/C.J.C.Bull%20-%20Contact%20Improvisation.pdf 

=== What to bring/What to Wear ===
Comfortable clothes good for movement. (Workout clothes). You will be laying on the floor… 

=== Suggested date and time ===
Monday, July 2nd, IAIA, Fitness Center 
Group Fitness/Dance Studio 
4:00-5:30pm 

=== Interested Participants? ===
1. Sarah H. (organizer)

== Dual tutorial on evolutionary dynamics /evolutionary computing==
(presented by Vandana, Chathika, Nam, Cedric)

Two tutorials: 
- evolutionary dynamics: model why and how things evolve (e.g. research of Paul Hooper) 
- evolutionary computing: model evolution to optimise things (e.g. research of Wendy Cho)

===Suggested date and time===
Monday 2th July, 6h45pm (Let me know if that time does not work for you!)

===Interested Participants===
(please indicate whether you would like to join as a presenter or audience)

 
 
 
 
Vandana 
Chathika (presenter) https://github.com/chathika/NL4Py https://pypi.org/project/NL4Py/#description

== Data Analysis Exercise with Ground Truth ==
(Pete K leads)

Either Tom Pike or I will be generating data for us from an unknown agent-based model. Our collective goal will be to analyze this data and discover as much as we can about this synthetic world in one warm Tuesday evening!

===Suggested date and time===
Tuesday 3rd July, 7pm

== Spatial ecology (Introduction to partial differential equations) ==
by Nikunj Goel

Earth is home to millions of organisms that occur in wide range of habitats ranging from humid tropics to icy poles. Understanding the drivers of distribution patterns of organisms is one of the fundamental goals of biogeography. Traditionally, biogeographers have sought to explain these patterns using niche models that relate the occurrence of an organism in a location to the prevailing climatic conditions (such as rainfall, temperature, PH, etc.). However, these models ignore the underlying spatial structure of the population and the interactions across various sub-populations, that might be equally important in predicting distribution patterns organisms.

In this tutorial, I aim to introduce a basic spatial model of population growth/spread using reaction-diffusion/partial-differential equations. We will derive Skellam's/Fisher's model of population spread from first principles and then analytically solve it. Next, I will present empirical examples from epidemiology, invasion ecology, and biome patterns [this is what I do!].

Due to time constraints, I won't be discussing the mathematics in too much detail. My goal is to make people aware of partial differential equations and get them started. I am happy to explain more OFFLINE!

===Interested Participants===
 

===Resources===
1. Partial Differential Equations in Ecology: Spatial Interactions and Population Dynamics (You should probably read this first. Its relatively simple paper) 
2. Biological Invasions: Theory and Practice (This is a nice and short book on invasion biology) 
3. Mathematical Biology, Part II: Spatial Models and Biomedical Applications (This is the definitive book of spatial biology; it's a bit heavy and jargony, though.) 

===Suggested date and time===
Tuesday 3rd July, 5 pm Main lecture hall

== Find your hook: how to make media and the public interested in your research ==
by Ranya Alkadamani (uploaded by Simon Jankowski)

Hey all,
Ranya my partner is here on Thursday, she’s a communications strategist (PR) working with scientific and tech organizations in Australia and has an awesome spiel on ‘finding your hook’ to attract interest in your work. She is going to run a tutorial with a short exercise to help us develop succinct messages about our individual research projects. It could be a good book-end for our project work or help define the focus of your larger research in the public sphere.

For anyone interested, it will run in the auditorium halfway through lunch. I believe that is 1.30 pm (TBD)

===Interested Participants===
1. Jonas 
2. Simon 
3. Javier 
4. Saska 
5. Conor 
6. ...

===Resources===
https://www.huffingtonpost.com/author/ranya-alkadamani 

===Suggested date and time===
Thursday 5th July, 1.30 pm Main lecture hall (TBD)

== Information Decompotion ==
by Conor Finn

I am going to hold a small tutorial/discussion on multivariate information decomposition before the barbecue. Anyone who is interested in quantifying how source variables provide unique, redundant and synergistic information about a target variable should come along.

===Resources===
1. The slides from the tutorial can be found here [http://tuvalu.santafe.edu/events/workshops/index.php/File:Conor_finn_ppid_csss18.pdf] 
2. The original paper on partial information decomposition is available from [https://arxiv.org/abs/1004.2515] 
3. My paper on this problem is available from [https://doi.org/10.3390/e20040297]

===Date and time===
Thursday 5th July, 7.00pm, Board Room 1

Complex Systems Summer School 2018-Tutorials

2018-07-10T21:19:49Z

CFinn:

{{Complex Systems Summer School 2018}}

Please use this space to organize any tutorial you would like to offer your peers. Thanks.

== Google Calendar ==

[https://calendar.google.com/calendar/embed?height=600&wkst=1&bgcolor=%23FFFFFF&src=5ukeqirhq8f847l3venbkecjb4%40group.calendar.google.com&color=%238D6F47&ctz=America%2FDenver Follow this link]

{{#iDisplay:https://calendar.google.com/calendar/embed?height=600&wkst=1&bgcolor=%23FFFFFF&src=5ukeqirhq8f847l3venbkecjb4%40group.calendar.google.com&color=%238D6F47&ctz=America%2FDenver}}

== Code Slam! ==
(organised by Alice)

Do you have an nice bit of code/ an app/ a jupyter noteboook that might be useful to other complex systems scientists? Pitch it in the code slam! Every volunteer gets five minutes to introduce their code or app to the group. We'll take a vote on the best presentation. The winner is going to get the invaluable CSSS Code Slam! trophy!!! ... which I will build from whatever materials that I find lying around at IAIA. :-)

===Suggested date and time===
Tuesday June 26, 8pm (Let me know if that time does not work for you!)

===Interested Participants===
(please indicate whether you would like to join as a presenter or audience)

Alice (presenter) 
Ben (presenter) 
Kofi (no code or app but let me know if you need help judging?) 
yuki 
Vandana 
Chathika (presenter) https://github.com/chathika/NL4Py https://pypi.org/project/NL4Py/#description

== Introduction to Machine Learning ==
===Suggested date and time===
Thu 21st 4:30-5:30 pm

=== Location ===
Lecture hall CLE

===Interested Participants===
(please indicate whether you would like to join as a presenter or audience)
* Yuki (presenter)
* Gianrocco (co-presenter)
* Kofi K. (audience)
* Alice (audience)
* Eleonora (audience)
* Louisa (audience)
* Ada (audience)
* Andrea (audience)
* Konstantinos (audience)
*Subash (audience)
* Xiaoyu (audience)
* Jared (audience)
* Ricky (audience)
*Vandana (audience)t
* Anastasiya (audience)
* Sanna (audience)
Simon (audience)

== Introduction to Deep Learning ==
===Suggested date and time===
Tue 21st 7-8pm

== Introduction to Recurrent Neural Networks ==
===Suggested date and time===
Thu 28th starting from like 5pm

===Interested Participants===
(please indicate whether you would like to join as a presenter or audience)
* Yuki (presenter)
* Kofi K (audience)
* Thushara (audience)
* Eleonora (audience)
* Ada (audience)
* Andrea (audience)
* Konstantinos (audience)
* Ariadna (audience)
* Xiaoyu (audience)
* Ricky (audience)
* Jared (audience)
* Anastasiya (audience)
* Sanna (audience)
* Xindi (audience)

==Physical theater==
Let's use our body and not just our brain! If you are interested in physical theater or simply just curious but with no previous experience, please sign up. I will go through some exercises involving body expression, movement and physical interactions.

https://en.wikipedia.org/wiki/Physical_theatre

=== Location ===
Dance studio - in the fitness center CONFIRMED!
=== date and time===
Tuesday 3rd July, 4:00 - 5:30PM
=== Interested participants===
*Niccolo (presenter)
*Yuki
* Gianrocco
* Jordan
* Jonas
* Eleonora
* Ada
* Evgenia
* Ariadna
* Subash
*Vandana
*maria
* Sanna

== Intro Improv Theatre ==
We will begin with some exercises to get out of our heads and to be aware of our fellow players. Then we build up towards the golden rule of improv (Yes, and ...) and finish with some short scenes! No prior experience needed!

https://en.wikipedia.org/wiki/Improvisational_theatre

I think 90min would be a good time.

=== Location ===
Dance studio - in the fitness center (to be confirmed)

=== Suggested date and time===
TBA

=== Interested participants===
*Jonas (presenter)
* R Maria
* Eleonora
* Ada
* Evgenia
* Ariadna
* Subash
*Vandana

== Introduction to ecological economics ==
The idea would be for me to give a 30 min overview more or less of the principles behind eco-eco: what it means, what makes it different from mainstream economics and environmental economics, how it is conceptualised and operationalised, etc. Then we can have a discussion/debate (can be more or less structured depending on how it goes, I'll prepare for something more structured if no natural debate arises) for 30 mins or however long people want.

=== Location ===
TBC

=== Suggested date and time===
Sometime in week 3

=== Interested participants===
1. Louisa (presenter) 
2. Shantal 
3. Jared 
4. Tom 
5. Cedric 
6. Kofi 
7. Cesar 

== Multi-scale integrated analysis of societal and ecosystem metabolism (MuSIASEM) ==
For those interested in an approach that describes the relations between funds and flows in societies and ecosystems (labour, water, land, energy, etc.) across different scales and hierarchical levels (https://en.wikipedia.org/wiki/MuSIASEM)

=== Location ===
TBC

=== Suggested date and time===
TBC

=== Interested participants===
* Louisa (presenter) 
* Ana 
* Thushara 
* Shantal 
* Tom 
* Alan 
* Xiaoyu 

== Simple VC overview ==

=== Location ===
Main Room of the Dorms

=== Suggested date and time===
9:00PM Wed, June 20th

=== Interested participants===

== Introduction to Statistical Physics of moving agents ==
I'll introduce stochastic differential equations which are a statistical physics tool that can be used to explore processes which we assume to be made up of some deterministic behaviour, and some stochastic (random) behaviour, which we call noise.
Noise can be interpreted in different ways depending on the application we wish to model (e.g. search strategies of foraging animals, stock market behaviour, gene regulatory networks).

This approach to stochastic processes will be different to the one presented by Srividya, although it can ultimately lead to the same 'distribution based' description. A nice thing about this approach is that it shows a strong link between the micro (fine scale) and macro (coarse scale) behaviour of systems.
I'll keep mathematics to a minimum and am happy to discuss finer details with anyone that cares.

=== Location ===
CLE auditorium

=== Suggested date and time===
Tuesday 26th 7pm?

=== Interested participants===
* Zohar (presenter) 
* Chathika (ignorant disciple) 
* Andrea 
* R Maria
* Ariadna
* Kofi 

==Managing Scientific Computing==

This tutorial will be split in two parts.

* Part 1) Managing large-scale computations using compute clusters and queueing systems (in particular SGE and PBS)
* Part 2) Plugging your custom C++-code to Python using pybind11 (and to Matlab using Matlab's C-wrapper)

github.com/benmaier/qsuite

https://github.com/pybind
=== Part 1 ===

Often times you find yourself writing some simulation code and end up with several free parameters. Now the problem is you wanna scan the parameter space and also sample a decent amount of times per parameter configuration. It is possible to scp your files to the server, write a job.sh script, submit that one, download the outputs, load the outputs and rewrap 'em to find the solutions you then want to analyze. Huge overhead.

I wrote a package/command-line tool (qsuite)[https://github.com/benmaier/qsuite] that deals with this typical problem.

=== Part 2 ===

What I've seen often is that people write custom C++-code only to compile it, feed data to it using the command line, then loading the output data to Python for analysis. This is an unneccesary overhead, as there exists a fairly simple framework to plug your C++-functions to a Python module that you can easily load and use in Python, called `pybind11`. I would give a short tutorial on how to use that one

Ben
(benjaminfrankmaier@gmail.com)

===Interested Participants:===
* Ben (presenter)
* Steph
* Alice
* Zohar
* Guillaume
* Kofi 
*
*

=== Suggested date and time===
Wednesday, 27th, 5pm

== COMPLETED EVENTS ==

== Python Agent Based Modelling- Mesa and Mesa Packages ==

Mesa is a relatively new ABM library based in the Python programming language. This tutorial will run through two quick ABMs using Mesa as well as a quick orientation of MESA API and GitHub page.

Mesa Packages is a new way to conceptualize ABM libraries with the idea of developing a repository of algorithms which can be integrated into ABMs. The goal is for the development of more complex models by having a rich repository of optimized algorithms which researchers can apply to different data or recombine in novel ways to explore a complex phenomenon. At the very least, it will hopefully make models easier to assemble and reduce the threshold so more people can build ABMs to support their research.

If you have any questions or thoughts please talk to me in person (Tom Pike) or send me an email tpike3@gmu.edu

Mesa API: https://mesa.readthedocs.io/en/master/overview.html

Mesa GitHub: https://github.com/projectmesa

Mesa Packages: https://github.com/projectmesa/mesa/wiki/Mesa-Packages

Mesa CSSS Tutorial: https://github.com/tpike3/CSSS_Mesa_tutorial

====Date, Time, Location (final)====
Date: Sunday June 17th

'''TIME CHANGE''' 
Time: 7:30
Location: 1st floor Conference Room (right across from cafeteria)

If you want to discuss some basic python, set up jupyter notebook etc I will be there by 6:30

''Based on a conversation it was requested to move the tutorial to Sunday evening, if you have issues please let me know
''

====Interested Participants====
1. Xiaoyu Wang 
2. Yuki 
3. Sanna 
4. R Maria 
5. Amy Schweikert 
6. Kevin Comer 
7. Thushara Gunda 
8. Kofi K 
9. Laura Mann 
10. Ariadna 
11. Eleonora 
12. Caroline Alves 
13. Jared Edgerton 
14. Allie 
15. 
== Agent-Based Model Analysis by Controlling NetLogo from Python with NL4Py ==

If you're interested in using Python libraries to analyze output from your NetLogo models you can do so with NL4Py.

You can get started with a simple: pip install nl4py

GitHub: https://github.com/chathika/NL4Py

Pypi: https://pypi.org/project/NL4Py/

Also, there are some Jupyter Notebook examples of using DEAP for calibration on multiple processors and SALib for sensitivity analysis via NL4Py here:
https://github.com/chathika/NL4Py/blob/master/examples/ParameterCalibrationWithDEAP.ipynb

https://github.com/chathika/NL4Py/blob/master/examples/SensitivityAnalysis.ipynb

It uses a different architecture from PyNetLogo, pushing the parallelization of headless workspaces to the JVM instead of leaving it to the Python application developer. NL4Py can help you to run thousands of NetLogo models under varying parameter configurations in parallel.

Please feel free to post any issues on the GitHub repository any arise!

Chathika
(chathika@knights.ucf.edu)

===Interested Participants:===
* JP (strictly novice Python skills but decent w/ NetLogo)
* Javier (strictly novice NetLogo skills but decent w/ Python)
* Xiaoyu Wang
* Ariadna (ok python/ no NL)
* Sanna
* Maria (ok python/ no NL)
* Amy (ok python, good with pandas library / little NL)
* Kevin (little python knowledge, decent knowledge with NetLogo)
-Kofi K. (limited Python experience, no NL)
* Eleonora (little Python, no NL)
* Ariadna (ok python/ no NL)

===Time===
By the poll results, 18th Monday 7:30 pm, works best.

== Structural robustness in networks ==
(organised by Alice)

The field of robustness and resilience in networks is wide and spans across many applications of complex systems: robustness in biological networks, cooperation and social networks, supply and trade networks, infrastructure, computing systems, etc. Speaking from experience, it is hard to navigate the literature on this topic and consolidate the many different jargons and the different notions of robustness and resilience. In this tutorial, I will
*share some ideas on how to navigate the field of network robustness and make sense of seemingly conflicting paradigms in the field,
*give an overview of some ideas on robustness in network theory
*have a discussion with you about the concepts of network robustness that exist in your field of study and how those may relate to notions of robustness in other fields.

For many questions concerning network robustness, there are no definitive answers. But I hope that this introduction can help you think critical about network robustness and navigate the litature quickly and with confidence.

===Suggested date and time===
Monday June 18, 7pm (Let me know if that time does not work for you!)

===Interested Participants===
* R Maria
* Luca
* Kofi K.
* Thushara
* Ariadna
* Sanna
* Cedric
* Jared Edgerton
* Andrea
* Eleonora
* Ada
* Evgenia
* Allie
*Subash

== Digital Trace Data (Web Scraping/API) ==
Practical demonstration on how to automatically scrape data from the web using several methods.

=== Suggested date and time ===
Tuesday 8PM

=== Participants ===

1. Jonas 
2. Andrea 
3. Louisa 
4. Inga 
5. Zohar 
6. Ana 
7. Thushara 
8. Evgenia 
9. Xiaoyu 
10. Alice 
11. Ariadna 
12. Subash 
13. Shantal 
14. Carlos 
15. Jared 
16. Chris 

== Introduction to Longitudinal Social Network Dynamics ==
(organised by Evgenia)

Are you curious about social network dynamics? This workshop gives an introduction to the statistical modelling of longitudinal social network data. We first explore why dynamic social network analysis is useful, and then look into a range of questions that could be answered within the model family of stochastic actor-based models (implemented in the RSiena package within R).

When this is useful:
- you want to focus on SOCIAL network dynamics
- the actors in the network have agency (they could decide over the course of action),
- there are multiple measurements (at least two) of the relationships within the same actor set.

Analytical possibilities are endless, e.g. you would like to explore
- how actor level characteristics affect evolution of relationships (creation and dissolution of new ties)
- how particular behaviour spreads in the network
- how what's going on within one type of network influences processes in another type of network (multiplex networks)
etc.

I will cover general considerations and point out useful resources.
In short: cool stuff.

===Suggested date and time===
UPDATE: The tutorial is shifted to 4 pm to avoid time conflict with experiment.
Wednesday June 27

MAIN LECTURE ROOM (where all of our classes take place)

===Interested Participants===

* Sanna
* Sandra
* Ariadna
* Thushara
===Further information===
Presentation posted on slack.

== Movement (Dance) Improvisation Tutorial/Workshop ==
(organized by Sarah H.) 

=== About the Tutorial ===
Thank you all for such a great improv dance tutorial yesterday! Had a wonderful time sharing the world of creative exploration and improvisation with you! 

This tutorial is an individual movement workshop that focuses on sensory (touch, sight, sound) exploration in the studio environment. No experience necessary! No contact with others… or if you are feeling ambitious - contact with others. More than likely, this will be an excellent opportunity to relax and "think" with your body… and let your mind rest. 

What does contact improvisational dance look like? Check out some of these youtube... 
Historical example of contact improvisation (we won't be doing this): https://www.youtube.com/watch?v=9FeSDsmIeHA 
And a more contemporary example: https://www.youtube.com/watch?v=nWfoGnT_etA
 

According to Wikipedia....: "Dance improvisation is the process of spontaneously creating movement. Development of improvised movement material is facilitated through a variety of creative explorations including body mapping through levels, shape and dynamics schema." https://en.wikipedia.org/wiki/Dance_improvisation 

Dance scholar Cynthia Novack suggests that movement improvisation informs us about the body's creative and physical response to social and political environments. With this in mind, I'll talk a little bit about the history of dance improvisation… and then will get you sensing and exploring through a series of movement "tasks" to deepen your sensory experience. 

Novack, C. (1988). Looking at Movement as Culture: Contact Improvisation to Disco. TDR/The Drama Review. Vol. 32. No. 4. Pp 102-119. https://ais.ku.edu.tr/course/19352/C.J.C.Bull%20-%20Contact%20Improvisation.pdf 

=== What to bring/What to Wear ===
Comfortable clothes good for movement. (Workout clothes). You will be laying on the floor… 

=== Suggested date and time ===
Monday, July 2nd, IAIA, Fitness Center 
Group Fitness/Dance Studio 
4:00-5:30pm 

=== Interested Participants? ===
1. Sarah H. (organizer)

== Dual tutorial on evolutionary dynamics /evolutionary computing==
(presented by Vandana, Chathika, Nam, Cedric)

Two tutorials: 
- evolutionary dynamics: model why and how things evolve (e.g. research of Paul Hooper) 
- evolutionary computing: model evolution to optimise things (e.g. research of Wendy Cho)

===Suggested date and time===
Monday 2th July, 6h45pm (Let me know if that time does not work for you!)

===Interested Participants===
(please indicate whether you would like to join as a presenter or audience)

 
 
 
 
Vandana 
Chathika (presenter) https://github.com/chathika/NL4Py https://pypi.org/project/NL4Py/#description

== Data Analysis Exercise with Ground Truth ==
(Pete K leads)

Either Tom Pike or I will be generating data for us from an unknown agent-based model. Our collective goal will be to analyze this data and discover as much as we can about this synthetic world in one warm Tuesday evening!

===Suggested date and time===
Tuesday 3rd July, 7pm

== Spatial ecology (Introduction to partial differential equations) ==
by Nikunj Goel

Earth is home to millions of organisms that occur in wide range of habitats ranging from humid tropics to icy poles. Understanding the drivers of distribution patterns of organisms is one of the fundamental goals of biogeography. Traditionally, biogeographers have sought to explain these patterns using niche models that relate the occurrence of an organism in a location to the prevailing climatic conditions (such as rainfall, temperature, PH, etc.). However, these models ignore the underlying spatial structure of the population and the interactions across various sub-populations, that might be equally important in predicting distribution patterns organisms.

In this tutorial, I aim to introduce a basic spatial model of population growth/spread using reaction-diffusion/partial-differential equations. We will derive Skellam's/Fisher's model of population spread from first principles and then analytically solve it. Next, I will present empirical examples from epidemiology, invasion ecology, and biome patterns [this is what I do!].

Due to time constraints, I won't be discussing the mathematics in too much detail. My goal is to make people aware of partial differential equations and get them started. I am happy to explain more OFFLINE!

===Interested Participants===
 

===Resources===
1. Partial Differential Equations in Ecology: Spatial Interactions and Population Dynamics (You should probably read this first. Its relatively simple paper) 
2. Biological Invasions: Theory and Practice (This is a nice and short book on invasion biology) 
3. Mathematical Biology, Part II: Spatial Models and Biomedical Applications (This is the definitive book of spatial biology; it's a bit heavy and jargony, though.) 

===Suggested date and time===
Tuesday 3rd July, 5 pm Main lecture hall

== Find your hook: how to make media and the public interested in your research ==
by Ranya Alkadamani (uploaded by Simon Jankowski)

Hey all,
Ranya my partner is here on Thursday, she’s a communications strategist (PR) working with scientific and tech organizations in Australia and has an awesome spiel on ‘finding your hook’ to attract interest in your work. She is going to run a tutorial with a short exercise to help us develop succinct messages about our individual research projects. It could be a good book-end for our project work or help define the focus of your larger research in the public sphere.

For anyone interested, it will run in the auditorium halfway through lunch. I believe that is 1.30 pm (TBD)

===Interested Participants===
1. Jonas 
2. Simon 
3. Javier 
4. Saska 
5. Conor 
6. ...

===Resources===
https://www.huffingtonpost.com/author/ranya-alkadamani 

===Suggested date and time===
Thursday 5th July, 1.30 pm Main lecture hall (TBD)

== Information Decompotion ==
by Conor Finn

I am going to hold a small tutorial/discussion on multivariate information decomposition before the barbecue. Anyone who is interested in quantifying how source variables provide unique, redundant and synergistic information about a target variable should come along.

===Resources===
1. The slides from the tutorial can be found here [http://tuvalu.santafe.edu/events/workshops/index.php/File:Conor_finn_ppid_csss18.pdf]
2. The original paper on partial information decomposition is available from [https://arxiv.org/abs/1004.2515]
3. My paper on this problem is available from [https://doi.org/10.3390/e20040297]

===Date and time===
Thursday 5th July, 7.00pm, Board Room 1

File:Conor finn ppid csss18.pdf

2018-07-10T21:19:02Z

CFinn: The slides from my tutorial on multivariate information decomposition before the barbecue.

The slides from my tutorial on multivariate information decomposition before the barbecue.

Complex Systems Summer School 2018-After Hours

2018-07-04T18:28:24Z

CFinn: /* July 4th: Fancy Dinner */

{{Complex Systems Summer School 2018}}

Please use this space to plan social events.

==July 4th: Fancy Dinner==

JP wants to take people for a fancy dinner and then encounter the uniquely inefficient way American restaurants split a check.

7:30pm, Pink Adobe

JP's Car (5 seats) 
# JP
# Nam
# Anastasiya
#yuki
#simon

JP's Other Car (5 seats)
# Rosalba (I can drive)
# Ariadna
#Carol
# Carlos
# Maria

Still Needs A Ride
#
#
#
#
...

==July 4th: Independence Day Celebrations!==

Check out what's going on at Santa Fe Place mall as they ring in another birthday of the U.S.A. Live music, food trucks, and fireworks at 9:00pm!

Lorenzo's Shuttle (15pps)

1.sandra 
2.cedric 
3. Vandana 
4.Xindi 
5. Sarah 
6. Alex 
7. Shantal 
8.Conor 
9. Matt 
10. Patricia 
11. Yanchen 
12.Tom 
13.Rishi 
14.Javier 
15.Saska 

Still Needs A Ride
1. Ben 
2.Ricky
3.
4.
5.
...

== '''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. 

== '''Earthships''' in Taos ==

For those of us heading to the spiritual retreat in Taos, let's take a short trip to the Earthships (https://taos.org/what-to-do/landmark-sites/earthship-biotecture/). We're still working out whether or not we can get a guided tour, but we will at least do the self-guided 1.5 hour tour.

Jordan's car: 
1. Jordan 
2. Chris 
3. Allie 
4. Simon 
5. George 

== '''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 
4. Carlos 
5. Shantal 
6. Thushara (driver) 
7. Matt 
 

==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.Simon 

=== '''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 
...

=== '''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====
At least for Cars 1, 2, and 4: (unsure about others) 
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 
Early Morning Sunday - Travel back to IAIA 

====Cars and Passengers====
 
Car 1 (leaving Saturday at 5:00am) 
1. Kevin 
2. Shantal 
3. Alex 
4. Ariadna 
 
Car 2 (leaving Saturday at 5:00am) 
1. Subash 
2. Neil 
3. Magdalena 
4. Nikunj 
5. Vandana 
 
Car 3 (leaving Saturday at 7:00am, not going to Carlsbad) 
1. Chathika 
2. Anastasiya 
3. Maria 
4. Cesar 
 
Car 4. (Leaving Friday after SFI, indifferent re camping / AirBnB, organising camping equipment) 
1. Evgenia (any other drivers?) 
2. Josefine 
3. Louisa 
4. Elan 
 
Car 5. (Leaving Friday after SFI) 
1. Rishi 
2. Javier 
3. Saska 
4. Peter 
5. Rosalba 
6. Jonas 
 
Car 6. (Leaving Friday after SFI) 
1. Konstantinos 
2. Andrea 
3. Niccolo 
4. Nam 
5. Gianrocco 
6. Carlos 

====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. Laura (has a car) 
28. Kevin 
29. Luca 
30.Elan 
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! ]]

=== '''July 1: JP's FARM!!!''' ===

JP is throwing open the gate and having folks over for a Sunday afternoon hang-out. Float in the pond! Stare at cows! See weird bugs!

Visit [[Ranch Party | Ranch Party 2018]] page to sign-up!

=== '''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 
5. maria (?) 

=== ''' 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.

Slack: #spiritual_retreat

$50/person ($45 for airbnb, $5 for snacks/beers/wine)

1. Pete (p10) 
2. Saska (p2) 
3. Javier (p1) 
4. Catriona 
5. Rishi (p12) 
6. Jonas (p11) 
7. Allie (p9) 
8. Niccolo 
9. Jordan (p7) 
10. Duy (p6) 
11. Elan (p11) 
12 R Maria (p5) 
13. Zohar 
14. Guillaume 
15.yuki 
16.cedric 
17. Chathika 
18. Cesar 
19. Conor 
20. 
21. Andrea (p3) 
22. 
23. 
24. Louisa 
25. Eleonora 
26. Kevin (p8) 
27. Evgenia 
28. 
29. Luca 
30. 
31. Sanna 
32. George 
33. Amy 
34. Inga 
35. Chris 

Available cars (to see how many we need to rent) 
1. Duy 
2. Cat 
3. Jordan 
4. Amy 

Jordan's car (leaving Friday evening, returning Sunday in time for JP's ranch party): 
1. Jordan 
2. Allie 
3. Kevin 
4. 
5. 

Waiting list : 
1.

Complex Systems Summer School 2018-After Hours

2018-07-04T05:55:58Z

CFinn: /* July 4th: Independence Day Celebrations! */

{{Complex Systems Summer School 2018}}

Please use this space to plan social events.

==July 4th: Fancy Dinner==

JP wants to take people for a fancy dinner and then encounter the uniquely inefficient way American restaurants split a check.

7:30pm, Pink Adobe

JP's Car (5 seats) 
# JP
# Nam
# Anastasiya
#yuki
#simon

JP's Other Car (5 seats)
# Rosalba (I can drive)
# Ariadna
#Carol
# Carlos
# Conor

Still Needs A Ride
#
#
#
#
...

==July 4th: Independence Day Celebrations!==

Check out what's going on at Santa Fe Place mall as they ring in another birthday of the U.S.A. Live music, food trucks, and fireworks at 9:00pm!

Lorenzo's Shuttle (15pps)

1.sandra 
2.cedric 
3. Vandana 
4.Xindi 
5. Sarah 
6. Alex 
7. Shantal 
8.Conor 
9. 
10. 
11. 
12. 
13. 
14. 
15. 

Still Needs A Ride
#
#
#
#
#
...

== '''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. 

== '''Earthships''' in Taos ==

For those of us heading to the spiritual retreat in Taos, let's take a short trip to the Earthships (https://taos.org/what-to-do/landmark-sites/earthship-biotecture/). We're still working out whether or not we can get a guided tour, but we will at least do the self-guided 1.5 hour tour.

Jordan's car: 
1. Jordan 
2. Chris 
3. Allie 
4. Simon 
5. George 

== '''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 
4. Carlos 
5. Shantal 
6. Thushara (driver) 
7. Matt 
 

==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.Simon 

=== '''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 
...

=== '''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====
At least for Cars 1, 2, and 4: (unsure about others) 
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 
Early Morning Sunday - Travel back to IAIA 

====Cars and Passengers====
 
Car 1 (leaving Saturday at 5:00am) 
1. Kevin 
2. Shantal 
3. Alex 
4. Ariadna 
 
Car 2 (leaving Saturday at 5:00am) 
1. Subash 
2. Neil 
3. Magdalena 
4. Nikunj 
5. Vandana 
 
Car 3 (leaving Saturday at 7:00am, not going to Carlsbad) 
1. Chathika 
2. Anastasiya 
3. Maria 
4. Cesar 
 
Car 4. (Leaving Friday after SFI, indifferent re camping / AirBnB, organising camping equipment) 
1. Evgenia (any other drivers?) 
2. Josefine 
3. Louisa 
4. Elan 
 
Car 5. (Leaving Friday after SFI) 
1. Rishi 
2. Javier 
3. Saska 
4. Peter 
5. Rosalba 
6. Jonas 
 
Car 6. (Leaving Friday after SFI) 
1. Konstantinos 
2. Andrea 
3. Niccolo 
4. Nam 
5. Gianrocco 
6. Carlos 

====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. Laura (has a car) 
28. Kevin 
29. Luca 
30.Elan 
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! ]]

=== '''July 1: JP's FARM!!!''' ===

JP is throwing open the gate and having folks over for a Sunday afternoon hang-out. Float in the pond! Stare at cows! See weird bugs!

Visit [[Ranch Party | Ranch Party 2018]] page to sign-up!

=== '''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 
5. maria (?) 

=== ''' 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.

Slack: #spiritual_retreat

$50/person ($45 for airbnb, $5 for snacks/beers/wine)

1. Pete (p10) 
2. Saska (p2) 
3. Javier (p1) 
4. Catriona 
5. Rishi (p12) 
6. Jonas (p11) 
7. Allie (p9) 
8. Niccolo 
9. Jordan (p7) 
10. Duy (p6) 
11. Elan (p11) 
12 R Maria (p5) 
13. Zohar 
14. Guillaume 
15.yuki 
16.cedric 
17. Chathika 
18. Cesar 
19. Conor 
20. 
21. Andrea (p3) 
22. 
23. 
24. Louisa 
25. Eleonora 
26. Kevin (p8) 
27. Evgenia 
28. 
29. Luca 
30. 
31. Sanna 
32. George 
33. Amy 
34. Inga 
35. Chris 

Available cars (to see how many we need to rent) 
1. Duy 
2. Cat 
3. Jordan 
4. Amy 

Jordan's car (leaving Friday evening, returning Sunday in time for JP's ranch party): 
1. Jordan 
2. Allie 
3. Kevin 
4. 
5. 

Waiting list : 
1.

Complex Systems Summer School 2018-After Hours

2018-07-04T05:46:49Z

CFinn: /* July 4th: Fancy Dinner */

{{Complex Systems Summer School 2018}}

Please use this space to plan social events.

==July 4th: Fancy Dinner==

JP wants to take people for a fancy dinner and then encounter the uniquely inefficient way American restaurants split a check.

7:30pm, Pink Adobe

JP's Car (5 seats) 
# JP
# Nam
# Anastasiya
#yuki
#simon

JP's Other Car (5 seats)
# Rosalba (I can drive)
# Ariadna
#Carol
# Carlos
# Conor

Still Needs A Ride
#
#
#
#
...

==July 4th: Independence Day Celebrations!==

Check out what's going on at Santa Fe Place mall as they ring in another birthday of the U.S.A. Live music, food trucks, and fireworks at 9:00pm!

Lorenzo's Shuttle (15pps)

1.sandra 
2.cedric 
3. Vandana 
4.Xindi 
5. Sarah 
6. Alex 
7. Shantal 
8. 
9. 
10. 
11. 
12. 
13. 
14. 
15. 

Still Needs A Ride
#
#
#
#
#
...

== '''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. 

== '''Earthships''' in Taos ==

For those of us heading to the spiritual retreat in Taos, let's take a short trip to the Earthships (https://taos.org/what-to-do/landmark-sites/earthship-biotecture/). We're still working out whether or not we can get a guided tour, but we will at least do the self-guided 1.5 hour tour.

Jordan's car: 
1. Jordan 
2. Chris 
3. Allie 
4. Simon 
5. George 

== '''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 
4. Carlos 
5. Shantal 
6. Thushara (driver) 
7. Matt 
 

==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.Simon 

=== '''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 
...

=== '''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====
At least for Cars 1, 2, and 4: (unsure about others) 
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 
Early Morning Sunday - Travel back to IAIA 

====Cars and Passengers====
 
Car 1 (leaving Saturday at 5:00am) 
1. Kevin 
2. Shantal 
3. Alex 
4. Ariadna 
 
Car 2 (leaving Saturday at 5:00am) 
1. Subash 
2. Neil 
3. Magdalena 
4. Nikunj 
5. Vandana 
 
Car 3 (leaving Saturday at 7:00am, not going to Carlsbad) 
1. Chathika 
2. Anastasiya 
3. Maria 
4. Cesar 
 
Car 4. (Leaving Friday after SFI, indifferent re camping / AirBnB, organising camping equipment) 
1. Evgenia (any other drivers?) 
2. Josefine 
3. Louisa 
4. Elan 
 
Car 5. (Leaving Friday after SFI) 
1. Rishi 
2. Javier 
3. Saska 
4. Peter 
5. Rosalba 
6. Jonas 
 
Car 6. (Leaving Friday after SFI) 
1. Konstantinos 
2. Andrea 
3. Niccolo 
4. Nam 
5. Gianrocco 
6. Carlos 

====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. Laura (has a car) 
28. Kevin 
29. Luca 
30.Elan 
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! ]]

=== '''July 1: JP's FARM!!!''' ===

JP is throwing open the gate and having folks over for a Sunday afternoon hang-out. Float in the pond! Stare at cows! See weird bugs!

Visit [[Ranch Party | Ranch Party 2018]] page to sign-up!

=== '''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 
5. maria (?) 

=== ''' 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.

Slack: #spiritual_retreat

$50/person ($45 for airbnb, $5 for snacks/beers/wine)

1. Pete (p10) 
2. Saska (p2) 
3. Javier (p1) 
4. Catriona 
5. Rishi (p12) 
6. Jonas (p11) 
7. Allie (p9) 
8. Niccolo 
9. Jordan (p7) 
10. Duy (p6) 
11. Elan (p11) 
12 R Maria (p5) 
13. Zohar 
14. Guillaume 
15.yuki 
16.cedric 
17. Chathika 
18. Cesar 
19. Conor 
20. 
21. Andrea (p3) 
22. 
23. 
24. Louisa 
25. Eleonora 
26. Kevin (p8) 
27. Evgenia 
28. 
29. Luca 
30. 
31. Sanna 
32. George 
33. Amy 
34. Inga 
35. Chris 

Available cars (to see how many we need to rent) 
1. Duy 
2. Cat 
3. Jordan 
4. Amy 

Jordan's car (leaving Friday evening, returning Sunday in time for JP's ranch party): 
1. Jordan 
2. Allie 
3. Kevin 
4. 
5. 

Waiting list : 
1.

Complex Systems Summer School 2018-After Hours

2018-06-28T03:29:27Z

CFinn: /* '"Spiritual Retreat"' near Taos and Hot Springs */

{{Complex Systems Summer School 2018}}

Please use this space to plan social events.

==upcoming events==

=== '''July 1: JP's FARM!!!''' ===

JP is throwing open the gate and having folks over for a Sunday afternoon hang-out. Float in the pond! Stare at cows! See weird bugs!

Visit [[Ranch Party | Ranch Party 2018]] page to sign-up!

=== '''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.

Slack: #spiritual_retreat

$50/person ($45 for airbnb, $5 for snacks/beers/wine)

1. Pete (p10) 
2. Saska (p2) 
3. Javier (p1) 
4. Catriona 
5. Rishi (p12) 
6. Jonas (p11) 
7. Allie (p9) 
8. Niccolo 
9. Jordan (p7) 
10. Duy (p6) 
11. Elan (p11) 
12 R Maria (p5) 
13. Zohar 
14. Guillaume 
15.yuki 
16.cedric 
17. Chathika 
18. Cesar 
19. Conor 
20. 
21. Andrea (p3) 
22. Simon 
23. Maria 
24. Louisa 
25. Eleonora 
26. Kevin (p8) 
27. Evgenia 
28. 
29. Luca 
30. 
31. Sanna 
32. George 
33. Amy 
34. Inga 
35. Chris 

Available cars (to see how many we need to rent) 
1. Duy 
2. Cat 
3. Jordan 
4. Amy 

Waiting list : 
1.

=== '''Earthships''' in Taos ===

For those of us heading to the spiritual retreat in Taos, let's take a short trip to the Earthships (https://taos.org/what-to-do/landmark-sites/earthship-biotecture/). We're still working out whether or not we can get a guided tour, but we will at least do the self-guided 1.5 hour tour.

Jordan's car: 
1. Jordan 
2. Chris 
3. Allie 
4. Simon 
5. George 

=== '''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 
4. Carlos 
5. Shantal 
6. Thushara (driver) 
7. Matt 
 

=== '''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.Simon 

=== '''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 
...

=== '''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====
At least for Cars 1, 2, and 4: (unsure about others) 
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 
Early Morning Sunday - Travel back to IAIA 

====Cars and Passengers====
 
Car 1 (leaving Saturday at 5:00am) 
1. Kevin 
2. Shantal 
3. Alex 
4. Ariadna 
 
Car 2 (leaving Saturday at 5:00am) 
1. Subash 
2. Neil 
3. Magdalena 
4. Nikunj 
5. Vandana 
 
Car 3 (leaving Saturday at 7:00am, not going to Carlsbad) 
1. Chathika 
2. Anastasiya 
3. Maria 
4. Cesar 
 
Car 4. (Leaving Friday after SFI, indifferent re camping / AirBnB, organising camping equipment) 
1. Evgenia (any other drivers?) 
2. Josefine 
3. Louisa 
4. Elan 
 
Car 5. (Leaving Friday after SFI) 
1. Rishi 
2. Javier 
3. Saska 
4. Peter 
5. Rosalba 
6. Jonas 
 
Car 6. (Leaving Friday after SFI) 
1. Konstantinos 
2. Andrea 
3. Niccolo 
4. Nam 
5. Gianrocco 
6. Carlos 

====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. Laura (has a car) 
28. Kevin 
29. Luca 
30.Elan 
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! ]]

Ranch Party

2018-06-28T03:27:47Z

CFinn: /* MITRE car (it's pronounced "MY-ter") */

{{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!
# Alan
# Ada
# Andrea

===Sasha's car (5 seats)===
#Sasha (dd)
#Stephanie
# Neil
# Xindi
#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
#
#

==Riding with Lorenzo (multiple runs possible) ==
# Vandana 
# Maria 
# Jarno 
# Anastasiya 
# Subash 
# Chris 
# Tom 
# Gianrocco 
# Magdalena 
# Matt 
# Zohar 
# Rosalba 
# Caroline Alves 
# 
# 

===Still Needs A Ride===

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

=Supplies=

==Non-Vegetarian Things==

JP: Burgers, Brats, Chicken

==Vegetarian==

* TG: veggie platter
* Dave: veggie burgers

==Snacks==

JP: Tortillas, Chips

Sanna: free-from dessert

==Drinks==

==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)

2018-06-25T20:48:54Z

CFinn:

{{Complex Systems Summer School 2018}}

Dan Larremore Workshop 2

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

1. Carlos Marino 
2. 
3. Maria 
4. Simon 
5. Carlos Garcia 
6.Thushara 
7.Gianrocco 
8. jenn 
9.Josefine 
10. Subash 
11.Cedric 
12.Chathika 
13. Jacob 
14. Jarno 
15.Sage Crump 
16. Teianna (CM) 
17.Wesley Taylor 
18. Eleonora 
19. Talia 
20. Zohar 
21. Cat 
22. Vandana 
23.Magdalena 
24. Ben 
25.Yuki 
26. Conor 
27. 
28. 
29. 
30. 
31. 
32. 
33. 
34. 
35. 
36. 
37. 
38. 
39. 
40. 

Overflow
#
#
#
#
#

Complex Systems Summer School 2018-After Hours

2018-06-19T23:09:02Z

CFinn: /* "Spiritual retreat" near Taos and Hot Springs */

{{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 
32.Elan 
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 
8. Conor 

=== '''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

2018-06-19T23:06:01Z

CFinn:

{{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. Magdalena 
3.Conor 
4. 
5.

Complex Systems Summer School 2018-Projects & Working Groups

2018-06-19T04:21:08Z

CFinn: /* Understanding Cardiac Dynamics in Health and Disease (#cardio) */

{{Complex Systems Summer School 2018}}

=Projects=

== Estimating the true number of malaria cases in Venezuela ==
 In 2016, Venezuela experienced one of the worst economic collapses in Latin America. The effects of this collapse have resulted in unprecedented inflation and food insecurity. The economic collapse has also caused the subsequent collapse of the medical and public health infrastructure, resulting in a surge of malaria, a mosquito-disease that was previously eradicated from Venezuela in 1977. However, due to a lack of governmental transparency and under-reporting of malaria cases from the government, it is challenging to know the true magnitude of the malaria outbreak, and to understand where within Venezuela the center of the epidemic is occurring. It is important to understand the actual factors resulting in the increase and spread of malaria within Venezuela and the spillover of cases other countries resulting from out-migration of Venezuelans to know inform prevention and control measures for the outbreak.

Our project aims to use publicly available data sources, such as Pan American Health Organization malaria reports from Venezuela and bordering countries, migration flows from Venezuela into bordering and proximal countries around Venezuela, new reports and social media, and economic/medical indicators from previous years, such as the cost of antimalarials to reconstruct the time-series of the malaria outbreak, to quantify the true number of malaria cases occurring in Venezuela and to identify factors contributing to the outbreak. 

=== Available data ===
-TBD

=== Interested participants ===

* talia
* gianrocco
* chris
* inga
* peter

=== meeting time ===

1:15 monday June 18

== 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
*gianrocco
* inga
* goel
* peter

=== Meeting time ===
friday @ SFI after 1st lecture (10:00 am)
monday June 18, 6:45-7:30, location: 2nd fl residence hall

== 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
* You guys might be interested in checking the MusicMap project. Link: https://musicmap.info

===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
* Nam Le (Neural Networks, ML, Music lover. ranking: 1A, 2B)
* Xiaoyu (Background in control theory, electrical system, ranking: 1A 2B)
* Ana (music lover, good at synthesizing research)
* Josefine (Networks, ABMs, plays music and knows some music theory. Ranking: 1A 2B)

==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) 
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. Subash (Background in information theory - transfer entropy in specific, experimental design) 
8. Patricia (Background in modeling dynamical systems, agent-based modeling, experience working on academic search committees) 
9. Javier 
10. Saska 
11. Xiaoyu (Background in control theory, electrical system, and wind energy) 

== Peer-review process ==
=== General Idea: ===
Investigate the peer review process from the perspectives of gender, institutional prestige, and nationality(?). Also, let's talk bigger picture about how we model and incentivize successful peer review.

Research Questions:
* How does institution and gender impact affect time between submission and acceptance?
* How does the relationship between the gender and institution of the author and the editor impact submission and acceptance decisions?
* How does single/double blind review affect female author acceptance rate? Is single or double blind faster?
* What is the rate of co-authorship between men/men, women/women, men/women?
* Does the H-index of the last author/first author predictive of time from submission to acceptance (publication?)?

=== Theme 2: Other Idea by Neil ===
Rethinking science as an Institution
* Study of Incentives in science: What’s role of incentives in the peer review process?
* Experiments: How do we incentivize/re-engineer peer review process? Can we model the different peer review traditions (single blind, double blind, etc.)?
* Design/Engineering interventions

=== Literature: ===
* https://publons.com/blog/pressforprogress-in-peer-review/
* http://www.pnas.org/content/pnas/114/48/12708.full.pdf (double blind vs single blind)
* https://elifesciences.org/articles/21718 gender bias in peer review (it has anonymised network with about 40k authors https://elifesciences.org/articles/21718/figures#SD3-data)
* https://link.springer.com/article/10.1007/s11192-015-1800-6 calibrated ABM on peer review
* https://www.nature.com/articles/nature12786 subjectivity/objectivity and hearding in peer review

=== Data: ===
* https://www.bmj.com/us/research/research
* https://f1000research.com/browse (also data on rejected papers)
* PLOS One Data

=== Interested Participants ===
1. Allie 
2. Andrea 
3. Evgenia 
4. Javier 
5. Neil 
5. Saska 
6. Sasha 
7. Sanna 

== 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, Duy Huynh

====Branch: Emergence of institutions on trade networks====

Medieval age sees the emergence of institutions which affect or control the long exchange trading. In short, these institutions can provide informations on potential trade partners in exchange of resources. Could we explain which mechanism have led to their emergence ? To do that, we could use model with game theory (simulate the trading), evolution, network and theory and economics models. Of course, we can explore different institutions or trading networks. 

Something related: Avner Greif. "Reputation and Coalitions in Medieval Trade: Evidence on the Maghribi Traders". The journal of Economic History. (1989) 
To model institutions in a game theory form: Leonid Hurwicz, "Institutions as families of game forms", (1996) The Japanese Economic Review 
Interested in Branch:

===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.
*Reza Rezazadegan, Chris, Barretta, Christian Reidys. "Multiplicity of phenotypes and RNA evolution". Journal of Theoretical Biology(2018) Paper on percolation of neutral space in 100 base pair long RNA's given energetic minimum folding.

===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. 
4. Xiaoyu (Background in control theory, Interested in Chinese Taoism) 

==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===
Mousavi, Hamid, et al. "Mining semantic structures from syntactic structures in free text documents." Semantic Computing (ICSC), 2014 IEEE International Conference on. IEEE, 2014.

===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 
14. Nam 
15. Andrea 

===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)
6. Nam Le 
7. Jarno 

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

==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 
5. Nam 

==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. Louisa (background in societal metabolism & sustainability) 
6. Xiaoyu Wang (background in control 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. Andrea (Mathematics) 
*3. Anastasya (Physics) 
*4. Conor (Physics / Information Theory)

== 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)
* Louisa (B, C)
* Jordan (A)
* Patricia (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 works on synchronization in modular networks. What is not so present here (at least not explicitly) is an analysis in terms of the feedback from macro to micro; although this is implicit in the character of phase coupling (i.e. the force on a phase is given by its difference from the average phase of its neighbors)
-- Garlaschelli, D., Hollander, F. den, Meylahn, J., & Zeegers, B. (2017). Synchronization of phase oscillators on the hierarchical lattice, 1–33. [http://arxiv.org/abs/1703.02535]

-- Kogan, O., Rogers, J. L., Cross, M. C., & Refael, G. (2009). Renormalization group approach to oscillator synchronization. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 80(3), 1–12. [https://doi.org/10.1103/PhysRevE.80.036206]

-- Arenas, A., Díaz-Guilera, A., & Pérez-Vicente, C. J. (2006). Synchronization reveals topological scales in complex networks. Physical Review Letters, 96(11), 1–4. [https://doi.org/10.1103/PhysRevLett.96.114102]

'''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]

== Evolution of trade networks ==
=== General Idea ===
Global economic integration has been a powerful driver of increased efficiency and improved living standards around the world, but has also raised concerns about the costs it has imposed on vulnerable groups and its potential impact on inequality. This project seek to analyse the evolution of trade networks and examine to what extend increased interconnectedness makes domestic economies more or less resilient to global trade shocks. 
Use a multi-layer network of trading partnerships to capture the different levels of integration in global value chains and examine the evolution of the network dynamics in the presence of an exogenous shock (eg increase in import tariffs). 

=== Potential data ===
World Input Output data 
http://www.wiod.org/home 
TiVA 
https://stats.oecd.org/index.aspx?queryid=75537 
Firm Level data 

=== Interested Participants ===
* Eleonora 
*
*

=== Meeting time ===
friday @ SFI at 3:30pm

==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 

==Understanding/Optimizing the features of social network structure to reach a quick but fair consensus ==

 Consensus seeking process is crucial for groups to make coordinated actions, vote for their institutions and react to dynamics environment. Research have shown that hierarchy can make the group reach a faster consensus but also lead to unfair decision. Could we keep the benefit of hierarchy without its cost ? To answer this question, we will use different method to analyse and optimise the impact of different features of a social network structure on the time to reach consensus and the fairness of the final decision.

So far, people have proposed to explore: 
- different distribution of degree and degree correlation 
- other mesoscale features of the network (hierarchy, communities, clique, clustering) 
- explore different voter model. For instance, individual with highly different opinion slowly influence each other (then homophily help reaching a faster consensus ?) 
- multiple speaker/ listeners

=== Suggested papers ===
Gavrilets et al. "Convergence to consensus in heterogeneous groups and the emergence of informal leadership". Nature scientific reports. (2016)
Lu et al, "Consensus over directed static networks with arbitrary finite communication delays" Physical review E (2009 )
=== Methods ===
Network analysis 
Multi-objective evolutionary computing (genetic algorithms, etc...) 
Non-linear dynamic analysis 

=== Interested Participants ===
1. Cedric 
2. Guillaume 
3. Zohar ? 
4. Javier? 

==Searching for patterns and narratives in the SFI Complex Systems Summer Schools==

===Data===
Hey guys, this is another project idea:

We can work with data of previous SFI Complex Systems Summer School generations available in the wiki. These include institution, country, working groups, project topics, project outcomes, (maybe not in the wiki but easy to find in google scholar) resulting collaborations post-CSSS, etc., etc.

In the wiki, there is information since 2006.

===Possible analysis===
Some of you have shown interest in this project and have thought of great and interesting ways of searching for the narratives hidden in this social experiment.

Matthew, from Ohio State University, mentioned we could look for network flows. Since many of the participants are directly advised by people from their institutions to apply to the CSSS, we could see which institutions remain predominant throughout the years.

Yuki seeded the idea of analyzing career paths of the participants of the CSSS. Are they still on academia? Did they end up working in the industry? How many of these people became entrepreneurs? (Is good to know our statistical possibilities guys).

Guillaume and Amy said diversity in teams has been studied as a measure of success? So we could also play with this idea.

Someone also said we could analyze changes or trends in topics of projects throughout the years? Has interest on understanding online social networks increased throughout the years in the CSSS participants?

===Interested participants===

Ana 
Talia 

Anyways, we can meet soon to talk about this. Please feel free to reach out on slack or directly. I would love to know what you guys think.

== Emergence of sustainable development contradictions ==

=== Introduction ===
Achieving the United Nation's 17 sustainable development goals (SDGs) requires progress along multiple dimensions of human development, and many improvements can be tackled using new or improved technology. However, some technological interventions can lead to contradictory changes in macro-level indicators. As a simple example, building a new factory may increase employment and thereby reduce hunger, but might simultaneously increase greenhouse gas emissions from manufacturing. But how do these and more complex contradictions emerge at the micro-level? Are there combinations of technologies that make them less likely, and if so, why? Does the sequence (of how technologies are introduced) make a difference? 

This project takes a technology-focused view at these questions and investigates the effects of introducing a new or improved technology portfolio into an existing network of resources, technologies, and industries. Since technologies require a similar set of resources/industries regardless of where they are being manufactured, we'll likely start by building a location-independent network and studying network changes as new technologies are added. Depending on people's interest and time constraints we can then pick one or multiple locations and incorporate data on resource availability, the rate of resource use (and temporal changes therein), existing industrial capabilities etc.

Additional ideas more than welcome!!! Feel free to indicate your interest here, on Slack, or reach out directly (mklemun@mit.edu)

=== Data and industry classification systems ===
North American Industry Classification System 
Sustainable Industry Classification System 
World Development Indicators (World Bank) 
Eurostat's classification server 
=== Interested Participants ===
* Magdalena Klemun
* Neil Gaikwad
* Chathika Gunaratne
* Amy Schweikert

=== Meeting time ===
TBD

== Metabolic rates and the collapse/transformation/adaptation of societies==

=== Introduction ===
Similar to how organisms have a metabolic rate which is linked to their lifespan, societies can be described by exosomatic metabolic rates (quantified, for example, in MJ/h where the hours are calculated as the total population size times 8760 – hours in a year).

The main idea behind this project would be to explore the relation between societies’ exosomatic metabolic rates, and their lifespan/sustainability. Looking at organisms, the higher the metabolic rate the shorter the lifespan – considering societies obviously adds many layers of complexity, but it is a relation which may be interesting to discuss and explore (even if to falsify it and build a critique of applications of biological concepts to social science).

=== Ideas/questions ===

The project is still very much in an open/exploratory phase (and will hopefully remain open and exploratory throughout its evolution). Some possible questions which we could discuss and focus on include, for example:

- Is it possible to define a taxonomy of societies, based on metabolic characteristics (e.g. exosomatic metabolic rates, human activity patterns, level of openness and trade, dependence on non-renewable resources, etc.) from which we can infer something about the society’s sustainability (and therefore its lifetime?) Since societies are open systems this would also mean looking at different relations across different types of societies (e.g. resource-rich societies exporting primary sources to resource-poor and capital-rich societies, which then transform them into secondary, lucrative products and re-export them)

- How do we define and conceptualize collapse? Wha does it mean for a society to transform, collapse or adapt? Could possibly explore and conceptualize different types of transformations and define relations between societies, ecosystems and transformations – this could also build on literature that views social systems as autopoietic self-organizing structures (Maturana, Luhmann..)

- Focusing on an individual society e.g. the US and seeing what history tells us about the direction in which it is going (is it nearing some form of collapse or radical transformation?)

=== Literature ===
- Multi-scale integrated assessment of societal metabolism: introducing the approach (Giampietro and Mayumi, 2000) 
- Sustainability of complex societies (Tainter, 1995) 
- Allometry of human fertility and energy use (Moses and Brown, 2003) 

Related fields (but anyone from any field is more than welcome to join! The more diverse the better): history, philosophy, ecological economics, theoretical ecology, anthropology, societal metabolism, energetics, hierarchy theory

=== Interested Participants ===
* Louisa
* Inga
* Amy

=== Meeting time ===
TBD

=='''Mean First Saturation Time (Random walks on networks)'''==
===='''General idea'''====
Random walks on networks have been broadly studied. An interesting measurement is the mean first passage time between two nodes (i,j) which is the expected time a random walker starting from i will take to reach j for the first time. A generalization of the mean first passage time would be the mean first saturation time which is the expected time at which S (or more) of N random walkers departing from node i arrive at node j.

The idea is to explore this measurement for different networks and for different distributions of N and S

'''Novelty''' Several studies have computed both numerically and analytically properties of random walk on networks. However, to the best of my knowledge, the mean first saturation time has not been studied.

===='''Real world applications'''====
European countries have a limit to the number of refugees they can take. By using a network of migration flows, we might be able to understand the susceptibility of each country and optimize the flow of migrants.

===='''Relevant papers'''====
* Suleimenova, D., Bell, D., & Groen, D. (2017). A generalized simulation development approach for predicting refugee destinations. Scientific reports, 7(1), 13377.
* Maier, B. F., & Brockmann, D. (2017). Cover time for random walks on arbitrary complex networks. Physical Review E, 96(4), 042307.
* Schaub, M. T., Lehmann, J., Yaliraki, S. N., & Barahona, M. (2014). Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution. Network Science, 2(1), 66-89.
* Asllani, M., Carletti, T., Di Patti, F., Fanelli, D., & Piazza, F. (2018). Hopping in the crowd to unveil network topology. Physical review letters, 120(15), 158301.

===Thoughts?===

===Interested Participants=== 
* R Maria
* Ben
* Guillaume

== The effects of changing relative timescales on complex systems ==

Most complex systems have multiple processes operating at different speeds. In general, the ratios between these processes can change - whether through evolution, the decisions of individual agents, new technologies, or external factors. In a simple linear system changing the relative timescales would not qualitatively change the dynamics, but in complex systems it often does. Our goal is to analyze several models of complex systems across different domains and using different methodologies to 1) understand how changing the relative timescales in each of these systems changes the dynamics and 2) determine if anything can be said more generally about the effects of changing the relative timescales in (a subset of) complex systems. We are looking both at "vertical" relative timescales, between for example a fast and a slow dynamics, and "horizontal" relative timescales, for example between the growth rates and the death rates in an ecosystem.

=== Systems being analyzed ===

(to be fleshed out)

1. Lotka Volterra ecosystem model.
* multiplying death rates by a constant
* slowly changing the parameters over time

2. Institutional change, David Krakauer's model.
* changing the espilon value that governs the separation of the fast and slow dynamics.

3. Spatial models with diffusion.
* Changing the diffusion rate
* Making diffusion instantaneous, removing space as a factor

4. Mutation/adaptation.
* changing the rates of mutation (either in genetic or in idealized adaptive models)

5. Cooperative networks.
* removing timescale separation
* changing speed of processes on the network

=== Participants ===
* Luca
* Carlos Marcelo
* Anastasiya
* Josefine
* Rishi
* Rosalba
* Sarah
* Ada

==Dance Improvisation and Complex Systems==

===Introduction===
According to Wikipedia: "Dance improvisation is the process of spontaneously creating movement. Development of improvised movement material is facilitated through a variety of creative explorations including body mapping through levels, shape and dynamics schema."
https://en.wikipedia.org/wiki/Dance_improvisation 

Many (not all) choreographers will use "dance improvisation" to generate/invent "new" movements, as a part of their art-making process. 

Thoughts on the central question we could consider: Is improvisational dance really improvisational dance? Theorization in Critical Dance Studies exists in this "between-ness" - the interstitial space between bodies - which can be at the membrane level - or encompass the space between bodies across a room. This space can be consumed by movement transmission, cultural transmission, thought transmission, visual transmission - all which have their own sets of cultural constraints.

===NEXT MEETING===
Lobby (Lecture room building), 7:00 pm - 8:00pm, Monday, June 18, 2018 -- Feel free to stop by!

===Research Questions===
Possible research questions…but open to more!: 
1. Questions we'd like to explore. 
a. Can we quantify dance improvisation? 
i. An emergent property. A task between two people. Interaction between two or more people that requires knowing and predicting your partner. So that you're not literally crashing into each other. 
ii. Sharing a common goal -- because that's the common goal of the group. 
iii. Ability to create new moves that lie outside the starting alphabet. 
iv. Defining dynamics between two people that you wouldn't have with anyone else. 
b. Can we define improvisation? Looking to other fields to help us define this term. 
c. Simply put, is movement always already spontaneous? Is improvisation truly improvised? 
d. How then, is dance improvisation differ from other fields? (Theater, music, conversation, movement improv, etc.) 
e. Are your movement choices more informed by past movement choices? 
f. i.e. How predictive are your movements? 
g. Is improvisation complex or chaotic? 
h. Can we embody something that is random? 
i. How do we measure "improvisationality"? Degrees of randomness?! 
j. Are we just repeating something that has already been done in the past? 

===Resources===
TBA

===Interested Participants===
Please feel free to join: #improv-dance 
1.Sarah H. 
2. Sasha 
3. Ana 
4. Gianrocco 
5. Patricia 
6. Arianda 
7. Vandana 
8. Yuki 

===Fun YouTube Videos===
Some interesting YouTube videos, either improvisational jams, or choreography inspired by improvisation... 
https://www.youtube.com/watch?v=fPHDb6ylhVY 
https://www.youtube.com/watch?v=xAYrEv4yp_Q 
https://www.youtube.com/watch?v=0wQG9BTW5AE 

=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.....

==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

Complex Systems Summer School 2018-Projects & Working Groups

2018-06-19T04:17:49Z

CFinn: /* Scaling of information requirements in living things */

{{Complex Systems Summer School 2018}}

=Projects=

== Estimating the true number of malaria cases in Venezuela ==
 In 2016, Venezuela experienced one of the worst economic collapses in Latin America. The effects of this collapse have resulted in unprecedented inflation and food insecurity. The economic collapse has also caused the subsequent collapse of the medical and public health infrastructure, resulting in a surge of malaria, a mosquito-disease that was previously eradicated from Venezuela in 1977. However, due to a lack of governmental transparency and under-reporting of malaria cases from the government, it is challenging to know the true magnitude of the malaria outbreak, and to understand where within Venezuela the center of the epidemic is occurring. It is important to understand the actual factors resulting in the increase and spread of malaria within Venezuela and the spillover of cases other countries resulting from out-migration of Venezuelans to know inform prevention and control measures for the outbreak.

Our project aims to use publicly available data sources, such as Pan American Health Organization malaria reports from Venezuela and bordering countries, migration flows from Venezuela into bordering and proximal countries around Venezuela, new reports and social media, and economic/medical indicators from previous years, such as the cost of antimalarials to reconstruct the time-series of the malaria outbreak, to quantify the true number of malaria cases occurring in Venezuela and to identify factors contributing to the outbreak. 

=== Available data ===
-TBD

=== Interested participants ===

* talia
* gianrocco
* chris
* inga
* peter

=== meeting time ===

1:15 monday June 18

== 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
*gianrocco
* inga
* goel
* peter

=== Meeting time ===
friday @ SFI after 1st lecture (10:00 am)
monday June 18, 6:45-7:30, location: 2nd fl residence hall

== 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
* You guys might be interested in checking the MusicMap project. Link: https://musicmap.info

===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
* Nam Le (Neural Networks, ML, Music lover. ranking: 1A, 2B)
* Xiaoyu (Background in control theory, electrical system, ranking: 1A 2B)
* Ana (music lover, good at synthesizing research)
* Josefine (Networks, ABMs, plays music and knows some music theory. Ranking: 1A 2B)

==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) 
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. Subash (Background in information theory - transfer entropy in specific, experimental design) 
8. Patricia (Background in modeling dynamical systems, agent-based modeling, experience working on academic search committees) 
9. Javier 
10. Saska 
11. Xiaoyu (Background in control theory, electrical system, and wind energy) 

== Peer-review process ==
=== General Idea: ===
Investigate the peer review process from the perspectives of gender, institutional prestige, and nationality(?). Also, let's talk bigger picture about how we model and incentivize successful peer review.

Research Questions:
* How does institution and gender impact affect time between submission and acceptance?
* How does the relationship between the gender and institution of the author and the editor impact submission and acceptance decisions?
* How does single/double blind review affect female author acceptance rate? Is single or double blind faster?
* What is the rate of co-authorship between men/men, women/women, men/women?
* Does the H-index of the last author/first author predictive of time from submission to acceptance (publication?)?

=== Theme 2: Other Idea by Neil ===
Rethinking science as an Institution
* Study of Incentives in science: What’s role of incentives in the peer review process?
* Experiments: How do we incentivize/re-engineer peer review process? Can we model the different peer review traditions (single blind, double blind, etc.)?
* Design/Engineering interventions

=== Literature: ===
* https://publons.com/blog/pressforprogress-in-peer-review/
* http://www.pnas.org/content/pnas/114/48/12708.full.pdf (double blind vs single blind)
* https://elifesciences.org/articles/21718 gender bias in peer review (it has anonymised network with about 40k authors https://elifesciences.org/articles/21718/figures#SD3-data)
* https://link.springer.com/article/10.1007/s11192-015-1800-6 calibrated ABM on peer review
* https://www.nature.com/articles/nature12786 subjectivity/objectivity and hearding in peer review

=== Data: ===
* https://www.bmj.com/us/research/research
* https://f1000research.com/browse (also data on rejected papers)
* PLOS One Data

=== Interested Participants ===
1. Allie 
2. Andrea 
3. Evgenia 
4. Javier 
5. Neil 
5. Saska 
6. Sasha 
7. Sanna 

== 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, Duy Huynh

====Branch: Emergence of institutions on trade networks====

Medieval age sees the emergence of institutions which affect or control the long exchange trading. In short, these institutions can provide informations on potential trade partners in exchange of resources. Could we explain which mechanism have led to their emergence ? To do that, we could use model with game theory (simulate the trading), evolution, network and theory and economics models. Of course, we can explore different institutions or trading networks. 

Something related: Avner Greif. "Reputation and Coalitions in Medieval Trade: Evidence on the Maghribi Traders". The journal of Economic History. (1989) 
To model institutions in a game theory form: Leonid Hurwicz, "Institutions as families of game forms", (1996) The Japanese Economic Review 
Interested in Branch:

===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.
*Reza Rezazadegan, Chris, Barretta, Christian Reidys. "Multiplicity of phenotypes and RNA evolution". Journal of Theoretical Biology(2018) Paper on percolation of neutral space in 100 base pair long RNA's given energetic minimum folding.

===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. 
4. Xiaoyu (Background in control theory, Interested in Chinese Taoism) 

==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===
Mousavi, Hamid, et al. "Mining semantic structures from syntactic structures in free text documents." Semantic Computing (ICSC), 2014 IEEE International Conference on. IEEE, 2014.

===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 
14. Nam 
15. Andrea 

===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)
6. Nam Le 
7. Jarno 

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

==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 
5. Nam 

==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. Louisa (background in societal metabolism & sustainability) 
6. Xiaoyu Wang (background in control 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. Andrea (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)
* Louisa (B, C)
* Jordan (A)
* Patricia (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 works on synchronization in modular networks. What is not so present here (at least not explicitly) is an analysis in terms of the feedback from macro to micro; although this is implicit in the character of phase coupling (i.e. the force on a phase is given by its difference from the average phase of its neighbors)
-- Garlaschelli, D., Hollander, F. den, Meylahn, J., & Zeegers, B. (2017). Synchronization of phase oscillators on the hierarchical lattice, 1–33. [http://arxiv.org/abs/1703.02535]

-- Kogan, O., Rogers, J. L., Cross, M. C., & Refael, G. (2009). Renormalization group approach to oscillator synchronization. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 80(3), 1–12. [https://doi.org/10.1103/PhysRevE.80.036206]

-- Arenas, A., Díaz-Guilera, A., & Pérez-Vicente, C. J. (2006). Synchronization reveals topological scales in complex networks. Physical Review Letters, 96(11), 1–4. [https://doi.org/10.1103/PhysRevLett.96.114102]

'''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]

== Evolution of trade networks ==
=== General Idea ===
Global economic integration has been a powerful driver of increased efficiency and improved living standards around the world, but has also raised concerns about the costs it has imposed on vulnerable groups and its potential impact on inequality. This project seek to analyse the evolution of trade networks and examine to what extend increased interconnectedness makes domestic economies more or less resilient to global trade shocks. 
Use a multi-layer network of trading partnerships to capture the different levels of integration in global value chains and examine the evolution of the network dynamics in the presence of an exogenous shock (eg increase in import tariffs). 

=== Potential data ===
World Input Output data 
http://www.wiod.org/home 
TiVA 
https://stats.oecd.org/index.aspx?queryid=75537 
Firm Level data 

=== Interested Participants ===
* Eleonora 
*
*

=== Meeting time ===
friday @ SFI at 3:30pm

==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 

==Understanding/Optimizing the features of social network structure to reach a quick but fair consensus ==

 Consensus seeking process is crucial for groups to make coordinated actions, vote for their institutions and react to dynamics environment. Research have shown that hierarchy can make the group reach a faster consensus but also lead to unfair decision. Could we keep the benefit of hierarchy without its cost ? To answer this question, we will use different method to analyse and optimise the impact of different features of a social network structure on the time to reach consensus and the fairness of the final decision.

So far, people have proposed to explore: 
- different distribution of degree and degree correlation 
- other mesoscale features of the network (hierarchy, communities, clique, clustering) 
- explore different voter model. For instance, individual with highly different opinion slowly influence each other (then homophily help reaching a faster consensus ?) 
- multiple speaker/ listeners

=== Suggested papers ===
Gavrilets et al. "Convergence to consensus in heterogeneous groups and the emergence of informal leadership". Nature scientific reports. (2016)
Lu et al, "Consensus over directed static networks with arbitrary finite communication delays" Physical review E (2009 )
=== Methods ===
Network analysis 
Multi-objective evolutionary computing (genetic algorithms, etc...) 
Non-linear dynamic analysis 

=== Interested Participants ===
1. Cedric 
2. Guillaume 
3. Zohar ? 
4. Javier? 

==Searching for patterns and narratives in the SFI Complex Systems Summer Schools==

===Data===
Hey guys, this is another project idea:

We can work with data of previous SFI Complex Systems Summer School generations available in the wiki. These include institution, country, working groups, project topics, project outcomes, (maybe not in the wiki but easy to find in google scholar) resulting collaborations post-CSSS, etc., etc.

In the wiki, there is information since 2006.

===Possible analysis===
Some of you have shown interest in this project and have thought of great and interesting ways of searching for the narratives hidden in this social experiment.

Matthew, from Ohio State University, mentioned we could look for network flows. Since many of the participants are directly advised by people from their institutions to apply to the CSSS, we could see which institutions remain predominant throughout the years.

Yuki seeded the idea of analyzing career paths of the participants of the CSSS. Are they still on academia? Did they end up working in the industry? How many of these people became entrepreneurs? (Is good to know our statistical possibilities guys).

Guillaume and Amy said diversity in teams has been studied as a measure of success? So we could also play with this idea.

Someone also said we could analyze changes or trends in topics of projects throughout the years? Has interest on understanding online social networks increased throughout the years in the CSSS participants?

===Interested participants===

Ana 
Talia 

Anyways, we can meet soon to talk about this. Please feel free to reach out on slack or directly. I would love to know what you guys think.

== Emergence of sustainable development contradictions ==

=== Introduction ===
Achieving the United Nation's 17 sustainable development goals (SDGs) requires progress along multiple dimensions of human development, and many improvements can be tackled using new or improved technology. However, some technological interventions can lead to contradictory changes in macro-level indicators. As a simple example, building a new factory may increase employment and thereby reduce hunger, but might simultaneously increase greenhouse gas emissions from manufacturing. But how do these and more complex contradictions emerge at the micro-level? Are there combinations of technologies that make them less likely, and if so, why? Does the sequence (of how technologies are introduced) make a difference? 

This project takes a technology-focused view at these questions and investigates the effects of introducing a new or improved technology portfolio into an existing network of resources, technologies, and industries. Since technologies require a similar set of resources/industries regardless of where they are being manufactured, we'll likely start by building a location-independent network and studying network changes as new technologies are added. Depending on people's interest and time constraints we can then pick one or multiple locations and incorporate data on resource availability, the rate of resource use (and temporal changes therein), existing industrial capabilities etc.

Additional ideas more than welcome!!! Feel free to indicate your interest here, on Slack, or reach out directly (mklemun@mit.edu)

=== Data and industry classification systems ===
North American Industry Classification System 
Sustainable Industry Classification System 
World Development Indicators (World Bank) 
Eurostat's classification server 
=== Interested Participants ===
* Magdalena Klemun
* Neil Gaikwad
* Chathika Gunaratne
* Amy Schweikert

=== Meeting time ===
TBD

== Metabolic rates and the collapse/transformation/adaptation of societies==

=== Introduction ===
Similar to how organisms have a metabolic rate which is linked to their lifespan, societies can be described by exosomatic metabolic rates (quantified, for example, in MJ/h where the hours are calculated as the total population size times 8760 – hours in a year).

The main idea behind this project would be to explore the relation between societies’ exosomatic metabolic rates, and their lifespan/sustainability. Looking at organisms, the higher the metabolic rate the shorter the lifespan – considering societies obviously adds many layers of complexity, but it is a relation which may be interesting to discuss and explore (even if to falsify it and build a critique of applications of biological concepts to social science).

=== Ideas/questions ===

The project is still very much in an open/exploratory phase (and will hopefully remain open and exploratory throughout its evolution). Some possible questions which we could discuss and focus on include, for example:

- Is it possible to define a taxonomy of societies, based on metabolic characteristics (e.g. exosomatic metabolic rates, human activity patterns, level of openness and trade, dependence on non-renewable resources, etc.) from which we can infer something about the society’s sustainability (and therefore its lifetime?) Since societies are open systems this would also mean looking at different relations across different types of societies (e.g. resource-rich societies exporting primary sources to resource-poor and capital-rich societies, which then transform them into secondary, lucrative products and re-export them)

- How do we define and conceptualize collapse? Wha does it mean for a society to transform, collapse or adapt? Could possibly explore and conceptualize different types of transformations and define relations between societies, ecosystems and transformations – this could also build on literature that views social systems as autopoietic self-organizing structures (Maturana, Luhmann..)

- Focusing on an individual society e.g. the US and seeing what history tells us about the direction in which it is going (is it nearing some form of collapse or radical transformation?)

=== Literature ===
- Multi-scale integrated assessment of societal metabolism: introducing the approach (Giampietro and Mayumi, 2000) 
- Sustainability of complex societies (Tainter, 1995) 
- Allometry of human fertility and energy use (Moses and Brown, 2003) 

Related fields (but anyone from any field is more than welcome to join! The more diverse the better): history, philosophy, ecological economics, theoretical ecology, anthropology, societal metabolism, energetics, hierarchy theory

=== Interested Participants ===
* Louisa
* Inga
* Amy

=== Meeting time ===
TBD

=='''Mean First Saturation Time (Random walks on networks)'''==
===='''General idea'''====
Random walks on networks have been broadly studied. An interesting measurement is the mean first passage time between two nodes (i,j) which is the expected time a random walker starting from i will take to reach j for the first time. A generalization of the mean first passage time would be the mean first saturation time which is the expected time at which S (or more) of N random walkers departing from node i arrive at node j.

The idea is to explore this measurement for different networks and for different distributions of N and S

'''Novelty''' Several studies have computed both numerically and analytically properties of random walk on networks. However, to the best of my knowledge, the mean first saturation time has not been studied.

===='''Real world applications'''====
European countries have a limit to the number of refugees they can take. By using a network of migration flows, we might be able to understand the susceptibility of each country and optimize the flow of migrants.

===='''Relevant papers'''====
* Suleimenova, D., Bell, D., & Groen, D. (2017). A generalized simulation development approach for predicting refugee destinations. Scientific reports, 7(1), 13377.
* Maier, B. F., & Brockmann, D. (2017). Cover time for random walks on arbitrary complex networks. Physical Review E, 96(4), 042307.
* Schaub, M. T., Lehmann, J., Yaliraki, S. N., & Barahona, M. (2014). Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution. Network Science, 2(1), 66-89.
* Asllani, M., Carletti, T., Di Patti, F., Fanelli, D., & Piazza, F. (2018). Hopping in the crowd to unveil network topology. Physical review letters, 120(15), 158301.

===Thoughts?===

===Interested Participants=== 
* R Maria
* Ben
* Guillaume

== The effects of changing relative timescales on complex systems ==

Most complex systems have multiple processes operating at different speeds. In general, the ratios between these processes can change - whether through evolution, the decisions of individual agents, new technologies, or external factors. In a simple linear system changing the relative timescales would not qualitatively change the dynamics, but in complex systems it often does. Our goal is to analyze several models of complex systems across different domains and using different methodologies to 1) understand how changing the relative timescales in each of these systems changes the dynamics and 2) determine if anything can be said more generally about the effects of changing the relative timescales in (a subset of) complex systems. We are looking both at "vertical" relative timescales, between for example a fast and a slow dynamics, and "horizontal" relative timescales, for example between the growth rates and the death rates in an ecosystem.

=== Systems being analyzed ===

(to be fleshed out)

1. Lotka Volterra ecosystem model.
* multiplying death rates by a constant
* slowly changing the parameters over time

2. Institutional change, David Krakauer's model.
* changing the espilon value that governs the separation of the fast and slow dynamics.

3. Spatial models with diffusion.
* Changing the diffusion rate
* Making diffusion instantaneous, removing space as a factor

4. Mutation/adaptation.
* changing the rates of mutation (either in genetic or in idealized adaptive models)

5. Cooperative networks.
* removing timescale separation
* changing speed of processes on the network

=== Participants ===
* Luca
* Carlos Marcelo
* Anastasiya
* Josefine
* Rishi
* Rosalba
* Sarah
* Ada

==Dance Improvisation and Complex Systems==

===Introduction===
According to Wikipedia: "Dance improvisation is the process of spontaneously creating movement. Development of improvised movement material is facilitated through a variety of creative explorations including body mapping through levels, shape and dynamics schema."
https://en.wikipedia.org/wiki/Dance_improvisation 

Many (not all) choreographers will use "dance improvisation" to generate/invent "new" movements, as a part of their art-making process. 

Thoughts on the central question we could consider: Is improvisational dance really improvisational dance? Theorization in Critical Dance Studies exists in this "between-ness" - the interstitial space between bodies - which can be at the membrane level - or encompass the space between bodies across a room. This space can be consumed by movement transmission, cultural transmission, thought transmission, visual transmission - all which have their own sets of cultural constraints.

===NEXT MEETING===
Lobby (Lecture room building), 7:00 pm - 8:00pm, Monday, June 18, 2018 -- Feel free to stop by!

===Research Questions===
Possible research questions…but open to more!: 
1. Questions we'd like to explore. 
a. Can we quantify dance improvisation? 
i. An emergent property. A task between two people. Interaction between two or more people that requires knowing and predicting your partner. So that you're not literally crashing into each other. 
ii. Sharing a common goal -- because that's the common goal of the group. 
iii. Ability to create new moves that lie outside the starting alphabet. 
iv. Defining dynamics between two people that you wouldn't have with anyone else. 
b. Can we define improvisation? Looking to other fields to help us define this term. 
c. Simply put, is movement always already spontaneous? Is improvisation truly improvised? 
d. How then, is dance improvisation differ from other fields? (Theater, music, conversation, movement improv, etc.) 
e. Are your movement choices more informed by past movement choices? 
f. i.e. How predictive are your movements? 
g. Is improvisation complex or chaotic? 
h. Can we embody something that is random? 
i. How do we measure "improvisationality"? Degrees of randomness?! 
j. Are we just repeating something that has already been done in the past? 

===Resources===
TBA

===Interested Participants===
Please feel free to join: #improv-dance 
1.Sarah H. 
2. Sasha 
3. Ana 
4. Gianrocco 
5. Patricia 
6. Arianda 
7. Vandana 
8. Yuki 

===Fun YouTube Videos===
Some interesting YouTube videos, either improvisational jams, or choreography inspired by improvisation... 
https://www.youtube.com/watch?v=fPHDb6ylhVY 
https://www.youtube.com/watch?v=xAYrEv4yp_Q 
https://www.youtube.com/watch?v=0wQG9BTW5AE 

=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.....

==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

Complex Systems Summer School 2018-Projects & Working Groups

2018-06-19T04:16:28Z

CFinn: /* Interested Participants */

{{Complex Systems Summer School 2018}}

=Projects=

== Estimating the true number of malaria cases in Venezuela ==
 In 2016, Venezuela experienced one of the worst economic collapses in Latin America. The effects of this collapse have resulted in unprecedented inflation and food insecurity. The economic collapse has also caused the subsequent collapse of the medical and public health infrastructure, resulting in a surge of malaria, a mosquito-disease that was previously eradicated from Venezuela in 1977. However, due to a lack of governmental transparency and under-reporting of malaria cases from the government, it is challenging to know the true magnitude of the malaria outbreak, and to understand where within Venezuela the center of the epidemic is occurring. It is important to understand the actual factors resulting in the increase and spread of malaria within Venezuela and the spillover of cases other countries resulting from out-migration of Venezuelans to know inform prevention and control measures for the outbreak.

Our project aims to use publicly available data sources, such as Pan American Health Organization malaria reports from Venezuela and bordering countries, migration flows from Venezuela into bordering and proximal countries around Venezuela, new reports and social media, and economic/medical indicators from previous years, such as the cost of antimalarials to reconstruct the time-series of the malaria outbreak, to quantify the true number of malaria cases occurring in Venezuela and to identify factors contributing to the outbreak. 

=== Available data ===
-TBD

=== Interested participants ===

* talia
* gianrocco
* chris
* inga
* peter

=== meeting time ===

1:15 monday June 18

== 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
*gianrocco
* inga
* goel
* peter

=== Meeting time ===
friday @ SFI after 1st lecture (10:00 am)
monday June 18, 6:45-7:30, location: 2nd fl residence hall

== 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
* You guys might be interested in checking the MusicMap project. Link: https://musicmap.info

===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
* Nam Le (Neural Networks, ML, Music lover. ranking: 1A, 2B)
* Xiaoyu (Background in control theory, electrical system, ranking: 1A 2B)
* Ana (music lover, good at synthesizing research)
* Josefine (Networks, ABMs, plays music and knows some music theory. Ranking: 1A 2B)

==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) 
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. Subash (Background in information theory - transfer entropy in specific, experimental design) 
8. Patricia (Background in modeling dynamical systems, agent-based modeling, experience working on academic search committees) 
9. Javier 
10. Saska 
11. Xiaoyu (Background in control theory, electrical system, and wind energy) 

== Peer-review process ==
=== General Idea: ===
Investigate the peer review process from the perspectives of gender, institutional prestige, and nationality(?). Also, let's talk bigger picture about how we model and incentivize successful peer review.

Research Questions:
* How does institution and gender impact affect time between submission and acceptance?
* How does the relationship between the gender and institution of the author and the editor impact submission and acceptance decisions?
* How does single/double blind review affect female author acceptance rate? Is single or double blind faster?
* What is the rate of co-authorship between men/men, women/women, men/women?
* Does the H-index of the last author/first author predictive of time from submission to acceptance (publication?)?

=== Theme 2: Other Idea by Neil ===
Rethinking science as an Institution
* Study of Incentives in science: What’s role of incentives in the peer review process?
* Experiments: How do we incentivize/re-engineer peer review process? Can we model the different peer review traditions (single blind, double blind, etc.)?
* Design/Engineering interventions

=== Literature: ===
* https://publons.com/blog/pressforprogress-in-peer-review/
* http://www.pnas.org/content/pnas/114/48/12708.full.pdf (double blind vs single blind)
* https://elifesciences.org/articles/21718 gender bias in peer review (it has anonymised network with about 40k authors https://elifesciences.org/articles/21718/figures#SD3-data)
* https://link.springer.com/article/10.1007/s11192-015-1800-6 calibrated ABM on peer review
* https://www.nature.com/articles/nature12786 subjectivity/objectivity and hearding in peer review

=== Data: ===
* https://www.bmj.com/us/research/research
* https://f1000research.com/browse (also data on rejected papers)
* PLOS One Data

=== Interested Participants ===
1. Allie 
2. Andrea 
3. Evgenia 
4. Javier 
5. Neil 
5. Saska 
6. Sasha 
7. Sanna 

== 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, Duy Huynh

====Branch: Emergence of institutions on trade networks====

Medieval age sees the emergence of institutions which affect or control the long exchange trading. In short, these institutions can provide informations on potential trade partners in exchange of resources. Could we explain which mechanism have led to their emergence ? To do that, we could use model with game theory (simulate the trading), evolution, network and theory and economics models. Of course, we can explore different institutions or trading networks. 

Something related: Avner Greif. "Reputation and Coalitions in Medieval Trade: Evidence on the Maghribi Traders". The journal of Economic History. (1989) 
To model institutions in a game theory form: Leonid Hurwicz, "Institutions as families of game forms", (1996) The Japanese Economic Review 
Interested in Branch:

===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.
*Reza Rezazadegan, Chris, Barretta, Christian Reidys. "Multiplicity of phenotypes and RNA evolution". Journal of Theoretical Biology(2018) Paper on percolation of neutral space in 100 base pair long RNA's given energetic minimum folding.

===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. 
4. Xiaoyu (Background in control theory, Interested in Chinese Taoism) 

==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===
Mousavi, Hamid, et al. "Mining semantic structures from syntactic structures in free text documents." Semantic Computing (ICSC), 2014 IEEE International Conference on. IEEE, 2014.

===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 
14. Nam 
15. Andrea 

===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)
6. Nam Le 
7. Jarno 

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

==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 
5. Nam 

==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) 
6. Louisa (background in societal metabolism & sustainability) 
7. Xiaoyu Wang (background in control 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. Andrea (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)
* Louisa (B, C)
* Jordan (A)
* Patricia (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 works on synchronization in modular networks. What is not so present here (at least not explicitly) is an analysis in terms of the feedback from macro to micro; although this is implicit in the character of phase coupling (i.e. the force on a phase is given by its difference from the average phase of its neighbors)
-- Garlaschelli, D., Hollander, F. den, Meylahn, J., & Zeegers, B. (2017). Synchronization of phase oscillators on the hierarchical lattice, 1–33. [http://arxiv.org/abs/1703.02535]

-- Kogan, O., Rogers, J. L., Cross, M. C., & Refael, G. (2009). Renormalization group approach to oscillator synchronization. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 80(3), 1–12. [https://doi.org/10.1103/PhysRevE.80.036206]

-- Arenas, A., Díaz-Guilera, A., & Pérez-Vicente, C. J. (2006). Synchronization reveals topological scales in complex networks. Physical Review Letters, 96(11), 1–4. [https://doi.org/10.1103/PhysRevLett.96.114102]

'''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]

== Evolution of trade networks ==
=== General Idea ===
Global economic integration has been a powerful driver of increased efficiency and improved living standards around the world, but has also raised concerns about the costs it has imposed on vulnerable groups and its potential impact on inequality. This project seek to analyse the evolution of trade networks and examine to what extend increased interconnectedness makes domestic economies more or less resilient to global trade shocks. 
Use a multi-layer network of trading partnerships to capture the different levels of integration in global value chains and examine the evolution of the network dynamics in the presence of an exogenous shock (eg increase in import tariffs). 

=== Potential data ===
World Input Output data 
http://www.wiod.org/home 
TiVA 
https://stats.oecd.org/index.aspx?queryid=75537 
Firm Level data 

=== Interested Participants ===
* Eleonora 
*
*

=== Meeting time ===
friday @ SFI at 3:30pm

==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 

==Understanding/Optimizing the features of social network structure to reach a quick but fair consensus ==

 Consensus seeking process is crucial for groups to make coordinated actions, vote for their institutions and react to dynamics environment. Research have shown that hierarchy can make the group reach a faster consensus but also lead to unfair decision. Could we keep the benefit of hierarchy without its cost ? To answer this question, we will use different method to analyse and optimise the impact of different features of a social network structure on the time to reach consensus and the fairness of the final decision.

So far, people have proposed to explore: 
- different distribution of degree and degree correlation 
- other mesoscale features of the network (hierarchy, communities, clique, clustering) 
- explore different voter model. For instance, individual with highly different opinion slowly influence each other (then homophily help reaching a faster consensus ?) 
- multiple speaker/ listeners

=== Suggested papers ===
Gavrilets et al. "Convergence to consensus in heterogeneous groups and the emergence of informal leadership". Nature scientific reports. (2016)
Lu et al, "Consensus over directed static networks with arbitrary finite communication delays" Physical review E (2009 )
=== Methods ===
Network analysis 
Multi-objective evolutionary computing (genetic algorithms, etc...) 
Non-linear dynamic analysis 

=== Interested Participants ===
1. Cedric 
2. Guillaume 
3. Zohar ? 
4. Javier? 

==Searching for patterns and narratives in the SFI Complex Systems Summer Schools==

===Data===
Hey guys, this is another project idea:

We can work with data of previous SFI Complex Systems Summer School generations available in the wiki. These include institution, country, working groups, project topics, project outcomes, (maybe not in the wiki but easy to find in google scholar) resulting collaborations post-CSSS, etc., etc.

In the wiki, there is information since 2006.

===Possible analysis===
Some of you have shown interest in this project and have thought of great and interesting ways of searching for the narratives hidden in this social experiment.

Matthew, from Ohio State University, mentioned we could look for network flows. Since many of the participants are directly advised by people from their institutions to apply to the CSSS, we could see which institutions remain predominant throughout the years.

Yuki seeded the idea of analyzing career paths of the participants of the CSSS. Are they still on academia? Did they end up working in the industry? How many of these people became entrepreneurs? (Is good to know our statistical possibilities guys).

Guillaume and Amy said diversity in teams has been studied as a measure of success? So we could also play with this idea.

Someone also said we could analyze changes or trends in topics of projects throughout the years? Has interest on understanding online social networks increased throughout the years in the CSSS participants?

===Interested participants===

Ana 
Talia 

Anyways, we can meet soon to talk about this. Please feel free to reach out on slack or directly. I would love to know what you guys think.

== Emergence of sustainable development contradictions ==

=== Introduction ===
Achieving the United Nation's 17 sustainable development goals (SDGs) requires progress along multiple dimensions of human development, and many improvements can be tackled using new or improved technology. However, some technological interventions can lead to contradictory changes in macro-level indicators. As a simple example, building a new factory may increase employment and thereby reduce hunger, but might simultaneously increase greenhouse gas emissions from manufacturing. But how do these and more complex contradictions emerge at the micro-level? Are there combinations of technologies that make them less likely, and if so, why? Does the sequence (of how technologies are introduced) make a difference? 

This project takes a technology-focused view at these questions and investigates the effects of introducing a new or improved technology portfolio into an existing network of resources, technologies, and industries. Since technologies require a similar set of resources/industries regardless of where they are being manufactured, we'll likely start by building a location-independent network and studying network changes as new technologies are added. Depending on people's interest and time constraints we can then pick one or multiple locations and incorporate data on resource availability, the rate of resource use (and temporal changes therein), existing industrial capabilities etc.

Additional ideas more than welcome!!! Feel free to indicate your interest here, on Slack, or reach out directly (mklemun@mit.edu)

=== Data and industry classification systems ===
North American Industry Classification System 
Sustainable Industry Classification System 
World Development Indicators (World Bank) 
Eurostat's classification server 
=== Interested Participants ===
* Magdalena Klemun
* Neil Gaikwad
* Chathika Gunaratne
* Amy Schweikert

=== Meeting time ===
TBD

== Metabolic rates and the collapse/transformation/adaptation of societies==

=== Introduction ===
Similar to how organisms have a metabolic rate which is linked to their lifespan, societies can be described by exosomatic metabolic rates (quantified, for example, in MJ/h where the hours are calculated as the total population size times 8760 – hours in a year).

The main idea behind this project would be to explore the relation between societies’ exosomatic metabolic rates, and their lifespan/sustainability. Looking at organisms, the higher the metabolic rate the shorter the lifespan – considering societies obviously adds many layers of complexity, but it is a relation which may be interesting to discuss and explore (even if to falsify it and build a critique of applications of biological concepts to social science).

=== Ideas/questions ===

The project is still very much in an open/exploratory phase (and will hopefully remain open and exploratory throughout its evolution). Some possible questions which we could discuss and focus on include, for example:

- Is it possible to define a taxonomy of societies, based on metabolic characteristics (e.g. exosomatic metabolic rates, human activity patterns, level of openness and trade, dependence on non-renewable resources, etc.) from which we can infer something about the society’s sustainability (and therefore its lifetime?) Since societies are open systems this would also mean looking at different relations across different types of societies (e.g. resource-rich societies exporting primary sources to resource-poor and capital-rich societies, which then transform them into secondary, lucrative products and re-export them)

- How do we define and conceptualize collapse? Wha does it mean for a society to transform, collapse or adapt? Could possibly explore and conceptualize different types of transformations and define relations between societies, ecosystems and transformations – this could also build on literature that views social systems as autopoietic self-organizing structures (Maturana, Luhmann..)

- Focusing on an individual society e.g. the US and seeing what history tells us about the direction in which it is going (is it nearing some form of collapse or radical transformation?)

=== Literature ===
- Multi-scale integrated assessment of societal metabolism: introducing the approach (Giampietro and Mayumi, 2000) 
- Sustainability of complex societies (Tainter, 1995) 
- Allometry of human fertility and energy use (Moses and Brown, 2003) 

Related fields (but anyone from any field is more than welcome to join! The more diverse the better): history, philosophy, ecological economics, theoretical ecology, anthropology, societal metabolism, energetics, hierarchy theory

=== Interested Participants ===
* Louisa
* Inga
* Amy

=== Meeting time ===
TBD

=='''Mean First Saturation Time (Random walks on networks)'''==
===='''General idea'''====
Random walks on networks have been broadly studied. An interesting measurement is the mean first passage time between two nodes (i,j) which is the expected time a random walker starting from i will take to reach j for the first time. A generalization of the mean first passage time would be the mean first saturation time which is the expected time at which S (or more) of N random walkers departing from node i arrive at node j.

The idea is to explore this measurement for different networks and for different distributions of N and S

'''Novelty''' Several studies have computed both numerically and analytically properties of random walk on networks. However, to the best of my knowledge, the mean first saturation time has not been studied.

===='''Real world applications'''====
European countries have a limit to the number of refugees they can take. By using a network of migration flows, we might be able to understand the susceptibility of each country and optimize the flow of migrants.

===='''Relevant papers'''====
* Suleimenova, D., Bell, D., & Groen, D. (2017). A generalized simulation development approach for predicting refugee destinations. Scientific reports, 7(1), 13377.
* Maier, B. F., & Brockmann, D. (2017). Cover time for random walks on arbitrary complex networks. Physical Review E, 96(4), 042307.
* Schaub, M. T., Lehmann, J., Yaliraki, S. N., & Barahona, M. (2014). Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution. Network Science, 2(1), 66-89.
* Asllani, M., Carletti, T., Di Patti, F., Fanelli, D., & Piazza, F. (2018). Hopping in the crowd to unveil network topology. Physical review letters, 120(15), 158301.

===Thoughts?===

===Interested Participants=== 
* R Maria
* Ben
* Guillaume

== The effects of changing relative timescales on complex systems ==

Most complex systems have multiple processes operating at different speeds. In general, the ratios between these processes can change - whether through evolution, the decisions of individual agents, new technologies, or external factors. In a simple linear system changing the relative timescales would not qualitatively change the dynamics, but in complex systems it often does. Our goal is to analyze several models of complex systems across different domains and using different methodologies to 1) understand how changing the relative timescales in each of these systems changes the dynamics and 2) determine if anything can be said more generally about the effects of changing the relative timescales in (a subset of) complex systems. We are looking both at "vertical" relative timescales, between for example a fast and a slow dynamics, and "horizontal" relative timescales, for example between the growth rates and the death rates in an ecosystem.

=== Systems being analyzed ===

(to be fleshed out)

1. Lotka Volterra ecosystem model.
* multiplying death rates by a constant
* slowly changing the parameters over time

2. Institutional change, David Krakauer's model.
* changing the espilon value that governs the separation of the fast and slow dynamics.

3. Spatial models with diffusion.
* Changing the diffusion rate
* Making diffusion instantaneous, removing space as a factor

4. Mutation/adaptation.
* changing the rates of mutation (either in genetic or in idealized adaptive models)

5. Cooperative networks.
* removing timescale separation
* changing speed of processes on the network

=== Participants ===
* Luca
* Carlos Marcelo
* Anastasiya
* Josefine
* Rishi
* Rosalba
* Sarah
* Ada

==Dance Improvisation and Complex Systems==

===Introduction===
According to Wikipedia: "Dance improvisation is the process of spontaneously creating movement. Development of improvised movement material is facilitated through a variety of creative explorations including body mapping through levels, shape and dynamics schema."
https://en.wikipedia.org/wiki/Dance_improvisation 

Many (not all) choreographers will use "dance improvisation" to generate/invent "new" movements, as a part of their art-making process. 

Thoughts on the central question we could consider: Is improvisational dance really improvisational dance? Theorization in Critical Dance Studies exists in this "between-ness" - the interstitial space between bodies - which can be at the membrane level - or encompass the space between bodies across a room. This space can be consumed by movement transmission, cultural transmission, thought transmission, visual transmission - all which have their own sets of cultural constraints.

===NEXT MEETING===
Lobby (Lecture room building), 7:00 pm - 8:00pm, Monday, June 18, 2018 -- Feel free to stop by!

===Research Questions===
Possible research questions…but open to more!: 
1. Questions we'd like to explore. 
a. Can we quantify dance improvisation? 
i. An emergent property. A task between two people. Interaction between two or more people that requires knowing and predicting your partner. So that you're not literally crashing into each other. 
ii. Sharing a common goal -- because that's the common goal of the group. 
iii. Ability to create new moves that lie outside the starting alphabet. 
iv. Defining dynamics between two people that you wouldn't have with anyone else. 
b. Can we define improvisation? Looking to other fields to help us define this term. 
c. Simply put, is movement always already spontaneous? Is improvisation truly improvised? 
d. How then, is dance improvisation differ from other fields? (Theater, music, conversation, movement improv, etc.) 
e. Are your movement choices more informed by past movement choices? 
f. i.e. How predictive are your movements? 
g. Is improvisation complex or chaotic? 
h. Can we embody something that is random? 
i. How do we measure "improvisationality"? Degrees of randomness?! 
j. Are we just repeating something that has already been done in the past? 

===Resources===
TBA

===Interested Participants===
Please feel free to join: #improv-dance 
1.Sarah H. 
2. Sasha 
3. Ana 
4. Gianrocco 
5. Patricia 
6. Arianda 
7. Vandana 
8. Yuki 

===Fun YouTube Videos===
Some interesting YouTube videos, either improvisational jams, or choreography inspired by improvisation... 
https://www.youtube.com/watch?v=fPHDb6ylhVY 
https://www.youtube.com/watch?v=xAYrEv4yp_Q 
https://www.youtube.com/watch?v=0wQG9BTW5AE 

=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.....

==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

DeDeo Workshop 1

2018-06-16T05:21:55Z

CFinn:

{{Complex Systems Summer School 2018}}

Tuesday, June 19th Sign-Up for Simon DeDeo's Workshop.

1. Marina Kogan 
2. Subash Ray 
3. Jonas 
4.Xindi 
5. Ricky 
6. Sarah B. 
7. Evgenia 
8.Shantal 
9.Conor Finn 
10. 
11. 
12. 
13. 
14. 
15. 
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June 14 Dome Showing

2018-06-15T01:03:00Z

CFinn: /* 9:00pm */

{{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. 
8. Tom PIke 
9. Sasha Mikhailova 
10. Kevin Comer 
11. Thushara Gunda 
12. 
13. Xindi Wang 
14. Patricia Mellodge 
15. Alex Shannon 
16. Shantal Cheong 
17. Konstantinos Aronis 
18. Vandana RV 
19. Rosalba 
20.Cesar 
21.Subash Ray 
22.Niccolo Pescetelli 
23. simon Jankowski 
24. Xiaoyu Wang 
25.Sandra 
26. Alice 
29. Gianrocco 
28. Evgenia 
29. George 
30. Andres Ortiz 

===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 
23. Ben 
24. Talia 
25. Rishi 
26.JP 
27. Jared Edgerton 
28. Zohar 
29.Ada 
30.Magdalena 

===9:00pm===

1.Caroline Alves 
2.Josefine 
3.Allie 
4. Andrea 
5. Anastasiya 
6.Ariadna 
7.Jacob 
8. Pete K. 
9.Elan 
10.Conor 
11. 
12. 
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Complex Systems Summer School 2018-Projects & Working Groups

2018-06-14T23:04:57Z

CFinn: /* Scaling of information requirements in living things */

{{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.

'''We'll meet on Friday at 1pm.'''

'''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? 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 
-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 
* 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 

===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 
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

==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

== 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 

===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 

=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.

=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-Projects & Working Groups

2018-06-14T22:50:04Z

CFinn: /* Interested participants: */

{{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.

'''We'll meet on Friday at 1pm.'''

'''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? 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 
-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 
* 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 

===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 
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 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? Recommended Papers?===

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

==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

== 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.

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

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

=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-Panel Questions

2018-06-14T20:04:14Z

CFinn:

{{Complex Systems Summer School 2018}}

* '''Publishing in Complex Systems''': there seems to be a gulf between the high impact interdisciplinary journals like PNAS, PRX, Nature Comms, etc. and the lower impact complex systems specific journals like Complexity or Complex Systems. This gap is dominated by domain specific journals, and as a result I have found it challenging to publish good, but perhaps not profound, interdisciplinary research. (The only exceptions I know of are perhaps Nature Scientific Reports and Royal Society Interface.) Does the panel have any advice for navigating these challenges?

Complex Systems Summer School 2018-Panel Questions

2018-06-14T19:30:22Z

CFinn:

{{Complex Systems Summer School 2018}}

* '''Publishing in Complex Systems''': their seems to be a gulf between the high impact interdisciplinary journals like PNAS, PRX, Nature Comms, etc. and the lower impact complex systems specific journals like Complexity or Complex Systems. This gap is dominated by domain specific journals, and as a result I have found it challenging to publish good, but perhaps not profound, interdisciplinary research. (The only exceptions I know of are perhaps Nature Scientific Reports and Royal Society Interface.) Does the panel have any advice for navigating these challenges?

Complex Systems Summer School 2018-Projects & Working Groups

2018-06-13T19:44:59Z

CFinn:

{{Complex Systems Summer School 2018}}

== 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). kkhamitk@gmail.com 
- George 
-Jacob 
- Yuki 
- Alice 

==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

== 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) 

== 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.

===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):
* ???

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

== 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

==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 
- Allie 

==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. Inter-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

==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===

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

==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 

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

Alice

Jonas

==Classifying language by grammatic 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 language
*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

Complex Systems Summer School 2018-Projects & Working Groups

2018-06-12T18:48:55Z

CFinn:

Complex Systems Summer School 2018-After Hours

2018-06-12T04:10:52Z

CFinn:

{{Complex Systems Summer School 2018}}

Please use this space to plan social 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. 

== '''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/

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

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

https://golondrinas.org/festivals/santa-fe-wine-festival/

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

On Saturdays at 12pm — who’s in?

Complex Systems Summer School 2018-After Hours

2018-06-12T04:01:09Z

CFinn:

Complex Systems Summer School 2018-After Hours

2018-06-12T03:51:53Z

CFinn:

{{Complex Systems Summer School 2018}}

Please use this space to plan social 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. 

'''Rodeo!''' 

Apparently there is rodeo in Santa Fe from June 20-23! We should go!

http://rodeodesantafe.org/

'''Santa Fe Wine Festival''' 

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

https://golondrinas.org/festivals/santa-fe-wine-festival/

Complex Systems Summer School 2018-After Hours

2018-06-12T03:45:31Z

CFinn: