Santa Fe Institute Events Wiki - User contributions [en]

Complex Systems Summer School 2019-Project Presentations

2019-06-29T17:42:45Z

ChrQua:

{{Complex Systems Summer School 2019}}

'''TUESDAY'''

Morning

*9.30: Resilience in Conway's Game of Life (Alex, Arta, Elissa, Luther, Kazuya, Patrick, Wenqian)
*9.40: Production Webs in Minecraft (Chris Q, Erwin, Kate, Bakus, Patrick)
*9.50: Modelling Housing Demand (John Shuler, Ian)
*10.00: Scrutinizing Early Warning Signals of Depression (Fabian, Toni, Andrea, Arta)
*10.10: Analyzing Collaboration throughout CSSS History (Jackie, Kyle, Dakota, Fabian, Emily)
*10.20: Intersections of CSS and CBR (Robert Winnie Travis dee ian)
*10.30: Complex Systems Summer School Social Survey
*10.40: Weighted Expectations (Mikaela, Elissa, Arta, Paula, Ahyan)
*10.50: Explaining mass extinction driven dwarfing (lilliput effect) with metabolic scaling theory (Anshuman, Jordi, Yuka, Jack)

*11.20: Multi-scale Inequality & Cities (Bhartendu, Alec, Ahyan, Chris Q, Daniel)
*11.30: Modelling the spatial diffusion of human language (Henri, Harun, Kenzie, Pablo Flores, Ritu)
*11.40: Self-Organizing City (Luther, German, Ludwig, Kazuya, Bhartendu)
*11.50: Too Much Information and Segregation (Wenqian, Pablo, Jordi, Brennan, Chris Q)
*12.00: Topological diversity in networks (Keith, Toni, Travis, Xin, Yuka)
*12.10: The individual lives of microbial cells: evolution of phenotypic diversity in a bacterial population (aka The Pheno-Evo Research Group) (Jessica L, Adam, Ritu, Pam, Daniel, Kirtus)
*12.20:

'''WEDNESDAY'''

Morning

*9.30: Modeling and predicting food insecurity using a resilience lens (Erwin, Andrew, Alexander, Pam, Dan, Fabian)
*9.40: Complex Movements and the City of Detroit (Jackie, Elissa, Travis and Ernest)
*9.50: Dynamics of Political Ideas on Social Networks (David, Jackie, Ludvig, Ernest, Ritu, Kyle)
*10.00: Resilence and presilience in protein network structure (April, Brennan, Keith, Ludvig, Laura, Mackenzie, Doug R, Anshuman)
*10.10: Is entropy sexy? (Kenzie, Henri, Ritu, Pablo)
*10.20: Evaluating Two Mechanisms for the Evolution of Social Complexity (Alex, Dries, Marjorie, Ignacio, Kazuya)
*10.30:cultural fractal
*10.40: Network Control with Graph Signal Processing (Alec, Billy, Brennan, Harun)
*10.50: Game Warping (Shruti, Aabir, Mikaela)

*11.20: Science Policy and Communication (John M, Chris B-J, Dakota Murray, Mackenzie Johnson, Kyle, Ritu, Andrew G-B)
*11.30: Lingua Technia (Dakota, John M, Chris B-J, Jeongki, Ignatio, Pablo F, Doug)
*11.40: Taming the Complex via Concept Mapping (Pam Dee Wenqian)
*11.50: Multi-dimensional money (Shruti, Ernest, Pavel)
*12.00: Artificial fossilization of animal interaction networks (Jack, Kate, Andrew, Anshuman, Dries, Emily)
*12.10: Toward an effective control of malaria in Ghana (Koissi, Jeongki, Anshuman, Bhartendu)
*12.20:

Afternoon

*14.00: Computational Synesthesia (Doug, Bhargav, Aabir, Mark, Ruggerio, Ethan)
*14.10:
*14.20:
*14.30: Scaling of water resources in US cities (Catherine, Jessica Brumley, Gen, Ian)
*14.40: Concave Utility as Efficient Encoding (Mikaela, Paula, Elissa)
*14.50:
*15.00:
*15.10:
*15.20: Paradigmatic Relations (Yuka, Mark)

*15.50: Perception of Aesthetic Information (Ethan, Mikaela, Mark)
*16.00:
*16.10:
*16.20:
*16.30:
*16.40:
*16.50:
*17.00:
*17.10:
*17.20:

Complex Systems Summer School 2019-Project Presentations

2019-06-29T17:40:12Z

ChrQua:

{{Complex Systems Summer School 2019}}

'''TUESDAY'''

Morning

*9.30: Resilience in Conway's Game of Life (Alex, Arta, Elissa, Luther, Kazuya, Patrick, Wenqian)
*9.40: Production Webs in Minecraft (Chris, Erwin, Kate, Bakus, Patrick)
*9.50: Modelling Housing Demand (John Shuler, Ian)
*10.00: Scrutinizing Early Warning Signals of Depression (Fabian, Toni, Andrea, Arta)
*10.10: Analyzing Collaboration throughout CSSS History (Jackie, Kyle, Dakota, Fabian, Emily)
*10.20: Intersections of CSS and CBR (Robert Winnie Travis dee ian)
*10.30: Complex Systems Summer School Social Survey
*10.40: Weighted Expectations (Mikaela, Elissa, Arta, Paula, Ahyan)
*10.50: Explaining mass extinction driven dwarfing (lilliput effect) with metabolic scaling theory (Anshuman, Jordi, Yuka, Jack)

*11.20: Multi-scale Inequality & Cities (Bhartendu, Alec, Ahyan, Chris Q, Daniel)
*11.30: Modelling the spatial diffusion of human language (Henri, Harun, Kenzie, Pablo Flores, Ritu)
*11.40: Self-Organizing City (Luther, German, Ludwig,Kazuya, Bhartendu)
*11.50: Too Much Information and Segregation (Wenqian, Pablo, Jordi, Brennan, Chris)
*12.00: Topological diversity in networks (Keith, Toni, Travis, Xin, Yuka)
*12.10: The individual lives of microbial cells: evolution of phenotypic diversity in a bacterial population (aka The Pheno-Evo Research Group) (Jessica L, Adam, Ritu, Pam, Daniel, Kirtus)
*12.20:

'''WEDNESDAY'''

Morning

*9.30: Modeling and predicting food insecurity using a resilience lens (Erwin, Andrew, Alexander, Pam, Dan, Fabian)
*9.40: Complex Movements and the City of Detroit (Jackie, Elissa, Travis and Ernest)
*9.50: Dynamics of Political Ideas on Social Networks (David, Jackie, Ludvig, Ernest, Ritu, Kyle)
*10.00: Resilence and presilience in protein network structure (April, Brennan, Keith, Ludvig, Laura, Mackenzie, Doug R, Anshuman)
*10.10: Is entropy sexy? (Kenzie, Henri, Ritu, Pablo)
*10.20: Evaluating Two Mechanisms for the Evolution of Social Complexity (Alex, Dries, Marjorie, Ignacio, Kazuya)
*10.30:cultural fractal
*10.40: Network Control with Graph Signal Processing (Alec, Billy, Brennan, Harun)
*10.50: Game Warping (Shruti, Aabir, Mikaela)

*11.20: Science Policy and Communication (John M, Chris B-J, Dakota Murray, Mackenzie Johnson, Kyle, Ritu, Andrew G-B)
*11.30: Lingua Technia (Dakota, John M, Chris B-J, Jeongki, Ignatio, Pablo F, Doug)
*11.40: Taming the Complex via Concept Mapping (Pam Dee Wenqian)
*11.50: Multi-dimensional money (Shruti, Ernest, Pavel)
*12.00: Artificial fossilization of animal interaction networks (Jack, Kate, Andrew, Anshuman, Dries, Emily)
*12.10: Toward an effective control of malaria in Ghana (Koissi, Jeongki, Anshuman, Bhartendu)
*12.20:

Afternoon

*14.00: Computational Synesthesia (Doug, Bhargav, Aabir, Mark, Ruggerio, Ethan)
*14.10:
*14.20:
*14.30: Scaling of water resources in US cities (Catherine, Jessica Brumley, Gen, Ian)
*14.40: Concave Utility as Efficient Encoding (Mikaela, Paula, Elissa)
*14.50:
*15.00:
*15.10:
*15.20: Paradigmatic Relations (Yuka, Mark)

*15.50: Perception of Aesthetic Information (Ethan, Mikaela, Mark)
*16.00:
*16.10:
*16.20:
*16.30:
*16.40:
*16.50:
*17.00:
*17.10:
*17.20:

Complex Systems Summer School 2019-Project Presentations

2019-06-29T01:56:44Z

ChrQua:

{{Complex Systems Summer School 2019}}

'''TUESDAY'''

Morning

*9.30: Resilience in Conway's Game of Life (Alex, Arta, Elissa, Luther, Kazuya, Patrick, Wenqian)
*9.40: Production Webs in Minecraft (Chris, Erwin, Kate, Bakus, Patrick)
*9.50: Modelling Housing Demand (John Shuler, Ian)
*10.00: Scrutinizing Early Warning Signals of Depression (Fabian, Toni, Andrea, Arta)
*10.10: Analyzing Collaboration throughout CSSS History (Jackie, Kyle, Dakota, Fabian, Emily)
*10.20: Intersections of CSS and CBR (Robert Winnie Travis dee ian)
*10.30: Complex Systems Summer School Social Survey
*10.40: Weighted Expectations (Mikaela, Elissa, Arta, Paula, Ahyan)
*10.50: Explaining mass extinction driven dwarfing (lilliput effect) with metabolic scaling theory (Anshuman, Jordi, Yuka, Jack)

*11.20: Scrutinizing early warning signals of depression (Andrea, Tony, Arta, Fabian)
*11.30: Modelling the spatial diffusion of human language (Henri, Harun, Kenzie, Pablo Flores, Ritu)
*11.40: Self-Organizing City (Luther, German, Ludwig,Kazuya, Bhartendu)
*11.50: Too Much Information and Segregation (Wenqian, Pablo, Jordi, Brennan, Chris)
*12.00:
*12.10:
*12.20:

'''WEDNESDAY'''

Morning

*9.30: Modeling and predicting food insecurity using a resilience lens (Erwin, Andrew, Alexander, Pam, Dan, Fabian)
*9.40: Complex Movements and the City of Detroit (Jackie, Elissa, Travis and Ernest)
*9.50: Dynamics of Political Ideas on Social Networks (David, Jackie, Ludvig, Ernest, Robert, Ritu, Kyle)
*10.00: Resilence and presilience in protein network structure (April, Brennan, Keith, Ludvig, Laura, Mackenzie, Doug R, Anshuman)
*10.10: Is entropy sexy? (Kenzie, Henri, Ritu, Pablo)
*10.20:Evaluating Two Mechanisms for the Evolution of Social Complexity
*10.30:cultural fractal
*10.40: Network Control with Graph Signal Processing (Alec, Billy, Brennan, Harun)
*10.50:

*11.20: Science Policy and Communication (John M, Chris B-J, Dakota Murray, Mackenzie Johnson, Kyle, Ritu, Andrew G-B)
*11.30: Lingua Technia (Dakota, John M, Chris B-J, Jeongki, Ignatio, Pablo F, Doug)
*11.40:
*11.50:
*12.00: Artificial fossilization of animal interaction networks (Jack, Kate, Andrew, Anshuman, Dries, Emily)
*12.10:
*12.20:

Afternoon

*14.00: Computational Synesthesia (Doug, Bhargav, Aabir, Mark, Ruggerio, Ethan)
*14.10:
*14.20:
*14.30: Scaling of water resources in US cities (Catherine, Jessica Brumley, Gen, Ian)
*14.40: Concave Utility as Efficient Encoding (Mikaela, Paula, Elissa)
*14.50:
*15.00:
*15.10:
*15.20: Paradigmatic Relations (Yuka, Mark)

*15.50: Perception of Aesthetic Information (Ethan, Mikaela, Mark)
*16.00:
*16.10:
*16.20:
*16.30:
*16.40:
*16.50:
*17.00:
*17.10:
*17.20:

Complex Systems Summer School 2019-Social Shuttle Times

2019-06-26T00:26:08Z

ChrQua:

{{Complex Systems Summer School 2019}}

==Social Shuttle Times with Lorenzo==

A shuttle will be available to get you to and from downtown Santa Fe on Friday evening and to other venues during these last two weeks

Shuttle schedule:

'''SATURDAY, JUNE 22'''

'''TO DOWNTOWN SANTA FE DURING THE DAY:''' 9:00 am - 2:30 pm

Pick Up Rotation: IAIA on the hour 9, 10, 11, 12, 1, 2

Water & Sandoval Downtown Stop: on the half hour 9:30, 10:30, 11:30, 12:30, 1:30 with last pick up 2:30.

'''TO DOWNTOWN SANTA FE DURING AT NIGHT:''' 10:00 pm - 12:30 am

Pick Up Rotation: IAIA on the hour 10, 11, 12

Water & Sandoval Downtown Stop: on the half hour 10:30, 11:30, with last pick up 12:30

Please be prompt to pickup locations, as the shuttle will need to keep a tight schedule in order to stay on time. We also want to be respectful of Lorenzo's time, especially with the late-night pickups.

In the event a shuttle is overloaded, a to-and-from trip ("orbit") should be approximately 45 minutes.

'''Reminder''' Uber and Lyft are also available and efficient ways of getting around.

'''TUESDAY, JUNE 25'''

'''TO ZAFARANO SHOPPING AREA (TARGET and MORE):''' 7:00 pm - 9:00 pm

Shuttle sign-up:
7:00 pm
* Jackie
* Emily
* Elissa
* Pablo
* Mackenzie
* Dee
* Wenqian
* Andrew
* Pam
* Anshuman
* Chris Q

Complex Systems Summer School 2019-Tutorials

2019-06-25T15:01:18Z

ChrQua: /* Interested Participants */

{{Complex Systems Summer School 2019}}

Please use this space to organize any tutorial you would like to offer your peers. It is useful to keep these in chronological order of occurrence (or at least proposed times) and include the time in the title, so that people can see what fits in their schedule at a glance by looking at the table of contents.

= Upcoming Tutorials =
==Introduction to Agent-Based Modelling with NetLogo - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/25)==
We will create an agent-based model in NetLogo together from scratch, and then test it to understand the emergent behavior (and possible interventions). I will go over some NetLogo design philosophy basics, the user interface, the world view, coding, data in- and output including GIS, and verification. We will also use BehaviorSpace (structured simulation experiments) to do some simple sensitivity analysis.

Required software will be NetLogo (v6.0 or higher, you probably have v6.04 or 6.1 if you recently installed) and some data analysis platform that you are comfortable with and can handle CSVs (Excel, R, Python, etc).

I would also be glad to consult on any projects involving ABM/systems simulation.

=== Suggested Date and Time ===
Tuesday 25JUN, 7:00 PM, lecture hall.

=== Interested Participants ===
Sign up below in the bulleted list below if you are interested. If you have experience with ABM and would like to share your expertise, please feel free to join. You can add more slots as needed:
* Patrick Steinmann (presenter)
* Dries
* Luther

If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].

==Introduction to Bayesian hypothesis testing and modeling - Fabian Dablander (Sunday, 6/30, 7:15PM - 9:15PM)==

Over the last thirty years, Bayesian inference has revolutionized statistics --- a discipline that is fraught with controversies, filled with individuals who hold strong opinions, and marred by a poor public image. In this tutorial, I (a) give a brief historical overview of statistics as a discipline; (b) provide a hands-on introduction to Bayesian hypothesis testing which provides a viable alternative to classical hypothesis testing; and (c) discuss good Bayesian modeling practices in the context of more complicated statistical models that go beyond simple hypotheses tests; this includes prior specification, model selection, and model checking.

=== Prior to the tutorial ===
The tutorial will have hands-on exercises, so please bring a Laptop (and possibly pen and paper). For (b), we will use JASP (https://jasp-stats.org/) which is a user-friendly, open-source alternative to SPSS that focuses on Bayesian hypothesis testing. For (c), we will use the R package *brms* which interfaces with Stan (https://mc-stan.org/). If you want to follow the hands-on exercises, please install these software ahead of time.

This tutorial assumes no background knowledge of Bayesian statistics. If you want to prepare a little bit, I recommend you check out the following two blog posts:
* https://fdabl.github.io/r/Regularization.html (relevant to part (b) of the tutorial)
* https://fdabl.github.io/r/Law-of-Practice.html (relevant to part (c) of the tutorial)

=== What questions do you have? ===

=== Interested Participants ===
* Bakus
* Arta
* Elissa
* Robert
* Toni
* Kate
* Bhargav
* Ernest

== Collaborative listening and emergent computation through social dance ==
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us (4:30-5:30) and follow it up with a little social dance practice and play time (5:30-6:00). Come learn to walk with four feet and listen with your heart!
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) Leads and follows can be any gender and role-swap is welcome! (In fact in Argentine tango, this is tradition--back in the day, men were only allowed to dance with women after they've spent 2-3 years learning by following other men!)

The Tuesday workshop will take place in the 2nd floor lounge of the dorms. The Wednesday and Thursday workshops will meet in the dance studio in the fitness center (from the main entrance, go down the hall and turn the corner to the left; the door will be on your right).

Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!

=== Suggested Date and Time ===
We realize this is slightly short notice-- if you're really enthusiastic but can't make these times, please let us know and we'll consider rescheduling for next week.

=== Tuesday, July 25, 4:30-6:00pm, dorm 2nd floor - Salsa [On1] (Luther + Jessica) ===
==== Interested Participants ====
* Kate
* Anshuman
* Bhargav

=== Tuesday, July 25, 7:00-8:30pm, dorm 2nd floor - Argentine Tango (Adam + Jessica) ===
==== Interested Participants ====
*Adam
*Winnie
*Kate
* Anshuman
* Bhargav

=== Wednesday, July 26, 4:30-6:00pm, dance studio - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===
==== Interested Participants ====
* Kate
* Anshuman
* Bhargav

== Learning to Flow: Morning Yoga Edition ==
Join Elissa in the dance studio on Tuesday and Thursday mornings for a relaxing, yet empowering, vinyasa flow! We'll learn together to link breath to movement, as we find space to flow with ease. No experience necessary! Mats available in the dance studio.

=== Thursday, June 13, 7:00-7:50am ===

=== Tuesday, June 18 and Thursday, June 20, 7:00-7:50am ===

=== Thursday, June 27, 7:00-7:50am ===

=== Tuesday, July 2 and Thursday, July 4, 7:00-7:50am ===

= Completed Tutorials =

==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==

Hi I'm Brennan. And I'm Hunter. And I'm Alec. (*in unison*) And we like networks. Specifically we would like to offer some support / tutorials to anyone who would like to learn about network science (e.g., structure, dynamics, visualization, etc.), all in python. We've got a few things we would love to cover, but on top of that, if there are specific questions / tools that anybody would like us to cover, include them below (with hyperlinks if possible), and we'll see if we can tie it in. The goal is that attendees will leave with 1) new friends, 2) a joie de vivre for the network science life and 3) new Jupyter notebook(s) with fun python code that you can build upon in your own work.

=== Prior to the tutorial ===

Github link '''[https://github.com/jkbren/network-tutorial-csss19 here]'''! The README.md will walk you through installing the main packages and software we'll be using. These mainly include:
* Jupyter notebooks
* networkx
* numpy
* scipy
* matplotlib

=== Wish-list of topics ===

* Network visualization in networkx
* Disease / spreading dynamics
* Community detection and modularity in networks

=== Interested Participants ===
* Al(ec)
* Hunter
* Brennan
* David
* Laura
* Patrick
* Erwin
* Bakus
* April
* Arta
* Dries
* Ian
* Elissa
* Andrea
* Kate
* Billy
* Pam
* Luther
* Koissi
* Kazu
* Ludvig

==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==

Classical statistics does not get much love these days with all the newer techniques. While I applaud these new techniques and use them myself, I think there is value in these older methods. In particular, classical statistics is an excellent framework for thinking about replication. I envision this as the first in a series of three talks but for now I am announcing one. I will cover hypothesis testing with minimal prerequisites. My focus will be on the logic behind hypothesis testing and common misunderstandings thereof.

=== Suggested Date and Time ===
I am willing to move this if desired. I will find a classroom and update this space.

=== Interested Participants ===
Sign up is not required but it would be helpful to have some idea.
* Patrick
* Kate
* Pam
* Arta
* Shihui
* Yuka

== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==

I've taught upper division/intro graduate level Nonlinear Dynamics a couple of times before. Given the quick pace of some of the lectures by the SFI faculty and people's various levels of familiarity with this material, I'd be glad to lead a couple of review/question and answer/clarification sessions for any of the Nonlinear Dynamics lectures (Liz Bradley, Josh Garland, Dave Feldman, Vicky Yang) if anybody is interested. I would also be glad to consult on any projects involving dynamical systems. The idea is to keep it pretty informal, low key, and organic. All levels of expertise welcome!

If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].

Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:
* Taylor series and linearization of nonlinear systems
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from
* Floquet multipliers and diverge of trajectories in maps
* Where the quadratic term in the logistic map comes from

== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==
Took an in-depth look at dynamics and bifurcations in 1D flows

==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:
* 1D maps
* Period doubling route to chaos

== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==

I thought that doing an introductory level tutorial in Natural Language Processing and Computational Linguistics in Python would be useful/fun - it usually adds a very informative level of complexity to projects, even when it isn't the primary mode of inquiry. If you don't have textual data, I can also guide you through the process of mining data off the internet, either through web scraping or twitter - you can also do cool stuff like mailing entire WhatsApp chat histories to yourself, which means we could also do some funky meta Santa Fe WhatsApp chat analysis!

I've conducted similar tutorials before ([https://www.youtube.com/watch?v=mWSs325tGoc&t=70s PyData LA 2018], [https://www.youtube.com/watch?v=ZkAFJwi-G98&t=6s PyData Berlin 2017]), and I also share all my material on GitHub in the form of [https://github.com/bhargavvader/personal/tree/master/notebooks/text_analysis_tutorial Jupyter Notebooks].
I've linked the videos and code so that you can have a brief look to see if it's stuff you might be interested in.

I'd be doing:

* finding text data
* pre-processing text data
* identifying your problem
* part-of-speech tagging, named entity recognition
* topic modelling
* text classification
* text generation with neural nets
* word embeddings

=== Preparing for the tutorial ===

Following the instructions under the setup section in [https://github.com/bhargavvader/personal/tree/master/notebooks/text_analysis_tutorial this link] will help a bunch! I will spend the first 20 minutes helping with setup before moving on. If you would want to run all the code in the tutorial while I am, you would need [[pythonhttps://www.python.org/downloads/|python]] and [https://jupyter.org/install jupyter] installed.

=== Suggested Date and Time ===
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!

=== Interested Participants ===
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!) 

# Bhargav (presenter) 
# Arta Cika
# Xin Ran
# Daniel Borrero
# Jackie Brown
# Pam Mantri
# Dee Romo
# Jeongki Lim
# Ernest Aigner
# Robert Coulter
# Winnie Poel
# Travis Moore
# Pablo M. Flores
# Catherine Brinkley
# Andrew Gillreath-Brown
# Kate
# Bakus
# Dries
# Bhartendu
# Kenzie Givens
# Wenqian
# Jordi
# Elissa

=== Note ===

This would require pretty basic python programming skills, but I'll be walking everyone through the code. Even if you can't code it might be useful to know what kind of problems you can solve, and I'd be happy to link to resources to learning enough python to get started on your own. There has been interest in doing a general Machine Learning tutorial too: we can talk about this during the text tutorial to figure out what might be most useful for everyone!

I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described!

([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)

==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==

Agent-based modelling can be a powerful for modelling complex system problems. But what *is* agent-based modelling? And how do we go about it in a structured and scientific way? And once we've made a model... what do we do with it? I have a background in policy analysis and simulation studies, and am offering this tutorial for those interested in using ABM (specifically NetLogo, as it is very accessible) in current or future work. I will cover some basic systems simulation theory, go over one structured method of making ABMs (from Agent-Based Modelling of Socio-Technical Systems, eds. van Dam, Nikolic, and Lukszo), and finally look at some ways the finished model could be used/explored - specifically, sensitivity analysis and scenario discovery. We will also briefly look at how NetLogo can be connected to tools such as Python, R, and Mathematica, and what possibilities that opens up.

I would also be glad to consult on any projects involving ABM/systems simulation.

=== Suggested Date and Time ===
Tuesday 18JUN, 7:00 PM, lecture hall.

=== Interested Participants ===
Sign up below in the bulleted list below if you are interested. If you have experience with ABM and would like to share your expertise, please feel free to join. You can add more slots as needed:
* Patrick Steinmann (presenter)
* Jeongki Lim
* Dries Daems
* Bhartendu Pandey
* Travis
* Pam
* Arta
* Ludvig
* Ian
* Wenqian
* Jordi
* Elissa
* Bakus
* Andrew
* Luther

If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].

==Data Visualization and Aesthetics - [https://github.com/eonadler/Data-Visualization/blob/master/Matplotlib%20and%20Data%20Visualization%20Tutorial.ipynb Ethan Nadler] (8:00 PM 6/19)==

This will be a tutorial/"formal" discussion (i.e. with slides) aimed at data visualization in science, and its relation to art and aesthetics. It will roughly be organized as follows, depending on interest:

1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;

2. Discussion of specific examples: each attendee will send a favorite plot/visualization that *they have made* (likely from past research), and we'll discuss each as a group;

3. Discussion of general principles: interesting topics include, but are not limited to:
* What makes a plot beautiful?
* Do scientific data visualization and art have the same aesthetic aims?
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)

=== Suggested Date and Time ===
8:00 PM on Wednesday, 6/19.

=== Interested Participants ===
* Ethan Nadler (presenter)
* Daniel Borrero
* Arta Cika
* Kenzie Givens
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)
* Patrick
* Erwin
* Kate
* Bakus
* Bhartendu
* Ernest
* Travis
* Pam
* Henri
* Ludvig
* Ignacio
* Mikaela
* Winnie
* Andrew
* John Malloy
* Ian
* Chris B-J

==Distribution Fitting and Maximum Likelihood Estimation - [https://wiki.santafe.edu/index.php/Christopher_Quarles Chris Quarles] (2:00 PM, Thursday 6/20 in Distance Learning Room 2)==

Researchers and statistics students regularly assume that their data is normally distributed, and network degree distributions are often assumed to follow a power law. These are typically incorrect assumptions. It is important to examine the ''shape'' of the data. And, if the data reasonably fits a nice, parametric shape, we might want to infer the best parameter(s) for that shape. For a power law, the parameter is the exponent. For normally distributed data, we might want to infer the mean and standard deviation. This can give insight about the process that generated the data and the analyses that we can do with it. Maximum likelihood estimation (MLE) is the workhorse method to do this distribution fitting.

In this workshop, you will learn how to fit distributions using MLE, and when it might be useful. I'll go over the basic ideas behind distribution fitting, including likelihood and log-likelihood. We will work through the calculation of a maximum likelihood estimator together, and talk about how to choose the best-fit distibution. You'll get the opportunity to do some hands-on calculation and find a best fit distribution for a dataset.

You'll want to bring a pencil and paper/notebook, and a computer with some basic statistical software that you know how to use (R, Python, Excel, etc.). You also will need to be able to take derivatives to get the most of the workshop.

=== Suggested Date and Time ===
Thursday, June 20th at 2:00 PM. 
This week is filling up with tutorials. If there are enough people interested, I can do this again during week 3. Text me on Slack if you can't make this, and would rather do it the following week.

=== Interested Participants ===
* Jessica Lee
* Bhartendu
* Henri
* Wenqian
* Arta
* Elissa
* Mikaela

==Kirsten Moy, 7:00pm, Tuesday June 17==

[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along

From her description:

A review of highlights from four other case studies in addition to Detroit on the utilization of complexity thinking in community development. Case studies include a microenterprise development organization in the San Francisco area that works from an ecosystem perspective; a national organization that brings NGOs and City Government together in a dozen cities to create greater financial security for low and moderate-income families; an organization that provides support to family networks in different cities to collectively bring people out of poverty; and the only community revitalization nonprofit in the US (now in 18 cities) that consciously and intentionally works from a complexity science framework.

Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.

Please sign up so we have some idea of who will be around and can choose the appropriate room

# JP
# Ahyan Panjwani
# Dee
# Ignacio
# Winnie
# Elissa

Complex Systems Summer School 2019-Project check-ins

2019-06-25T02:10:36Z

ChrQua:

We will be checking in with each project group this week at IAIA either Tuesday, Wednesday or Thursday. We then have time for some optional check-ins on Friday at SFI. We will put real names for the rooms once we figure out which rooms we'll actually be using. '''Please make sure that each group is signed up for at least one check in at IAIA between tomorrow and Thursday. When signing up, please include the project name and the list of participants'''. You don't have to sign up for the second round of optional check-ins until you've had your first round, but please make sure you do so by Thursday evening so we can plan our schedule ahead for Friday.

'''Tuesday afternoon'''

'''ROOM 1 - IAIA'''
* 16.30 - 16.40: Looking for resilient patterns in Conway's Game of Life
* 16.40 - 16.50:
* 16.50 - 17.00:
* 17.10 - 17.20: intersections of community engaged research and css (Robert Dee Winnie Travis Jackie)
* 17.20 - 17.30:
* 17.30 - 17.40:
* 17.40 - 17.50:
* 17.50 - 18.00: Is Entropy Sexy? Quantifying the fitness effects of novelty in courtship displays (Kenzie, Henri, Ritu, Pablo Flores)
* 18.00 - 18.10: Modelling the spatial diffusion of human language (Henri, Dee, Harun, Kenzie, Pablo Flores, Ritu)

'''ROOM 2 - IAIA'''
* 16.30 - 16.40:
* 16.40 - 16.50:
* 16.50 - 17.00:
* 17.10 - 17.20:
* 17.20 - 17.30:
* 17.30 - 17.40:
* 17.40 - 17.50:
* 17.50 - 18.00: Too Much Information and Segregation (Chris Q, Pablo Franco, Wenqian, Jordi, Brennan)
* 18.00 - 18.10:

'''Wednesday afternoon'''

'''ROOM 1 - IAIA'''
* 16.30 - 16.40: Analyzing CSSS (Dakota, Emily, Fabian, Jackie, Kyle)
* 16.40 - 16.50: Does network structure affect incorporation of novel data? (April, Brennan, Keith, Ludvig, Laura, Mackenzie, Doug Reckamp)
* 16.50 - 17.00: Scrutinizing Early Warning Signals of Depression (Fabian, Toni, Andrea, Arta)
* 17.10 - 17.20: Network Control (Billy, Brennan, Alec, Harun)
* 17.20 - 17.30: Cities and Inequality (Alec, Bhartendu, Travis, Dan, Bhargav, Chris, ....)
* 17.30 - 17.40: Self organizing city (Bhartendu, Chris, German, Jackie, Kazu, Ludwig, Luther)
* 17.40 - 17.50: Modeling and predicting food insecurity using a resilience lens (Erwin, Ludvig, Andrew, Alexander, Pam, Dan, Fabian)
* 17.50 - 18.00: Chaos in the Brain (Pablo, Paula, David, Fabian, Levi, Mikaela, Laura)

'''ROOM 2 - IAIA'''
* 16.30 - 16.40:
* 16.40 - 16.50:
* 16.50 - 17.00: Dynamics of Political Ideas on Social Networks
* 17.10 - 17.20:
* 17.20 - 17.30:
* 17.30 - 17.40:
* 17.40 - 17.50:
* 17.50 - 18.00:

'''ROOM 3 - IAIA'''
* 16.30 - 16.40:
* 16.40 - 16.50:
* 16.50 - 17.00:
* 17.10 - 17.20:
* 17.20 - 17.30:
* 17.30 - 17.40:
* 17.40 - 17.50:
* 17.50 - 18.00:

'''Thursday afternoon'''

'''ROOM 1 - IAIA'''
* 16.30 - 16.40:
* 16.40 - 16.50:

'''ROOM 2 - IAIA'''
* 16.30 - 16.40:
* 16.40 - 16.50:

'''Friday afternoon - optional second round of check-ins at SFI'''

'''ROOM 1 - SFI'''
* 14.00 - 14.10: Network fossilization (Andrew, Anshuman, Emily, Kate, Dries, Jack)
* 14.10 - 14.20:
* 14.20 - 14.30:
* 14.30 - 14.40:
* 14.40 - 14.50:
* 14.50 - 15.00:
*
* 15.40 - 15.50:
* 15.50 - 16.00:
* 16.00 - 16.10:
* 16.10 - 16.20:
* 16.20 - 16.30:
* 16.20 - 16.30:

'''ROOM 2 - SFI'''
* 14.00 - 14.10:
* 14.10 - 14.20:
* 14.20 - 14.30:
* 14.30 - 14.40:
* 14.40 - 14.50:
* 14.50 - 15.00:
*
* 15.40 - 15.50:
* 15.50 - 16.00:
* 16.00 - 16.10:
* 16.10 - 16.20:
* 16.20 - 16.30:
* 16.20 - 16.30:

'''ROOM 3 - SFI'''
* 14.00 - 14.10:
* 14.10 - 14.20:
* 14.20 - 14.30:
* 14.30 - 14.40:
* 14.40 - 14.50:
* 14.50 - 15.00:
*
* 15.40 - 15.50:
* 15.50 - 16.00:
* 16.00 - 16.10:
* 16.10 - 16.20:
* 16.20 - 16.30:
* 16.20 - 16.30:

Complex Systems Summer School 2019-Tutorials

2019-06-24T19:57:07Z

ChrQua: /* Interested Participants */

{{Complex Systems Summer School 2019}}

Please use this space to organize any tutorial you would like to offer your peers. It is useful to keep these in chronological order of occurrence (or at least proposed times) and include the time in the title, so that people can see what fits in their schedule at a glance by looking at the table of contents.

= Upcoming Tutorials =
==Introduction to Agent-Based Modelling with NetLogo - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/25)==
We will create an agent-based model in NetLogo together from scratch, and then test it to understand the emergent behavior (and possible interventions). I will go over some NetLogo design philosophy basics, the user interface, the world view, coding, data in- and output including GIS, and verification. We will also use BehaviorSpace (structured simulation experiments) to do some simple sensitivity analysis.

Required software will be NetLogo (v6.0 or higher, you probably have v6.04 or 6.1 if you recently installed) and some data analysis platform that you are comfortable with and can handle CSVs (Excel, R, Python, etc).

I would also be glad to consult on any projects involving ABM/systems simulation.

=== Suggested Date and Time ===
Tuesday 25JUN, 7:00 PM, lecture hall.

=== Interested Participants ===
Sign up below in the bulleted list below if you are interested. If you have experience with ABM and would like to share your expertise, please feel free to join. You can add more slots as needed:
* Patrick Steinmann (presenter)
* Dries
*

If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].

==Introduction to Bayesian hypothesis testing and modeling - Fabian Dablander (Sunday, 6/30, 7:15PM - 9:15PM)==

Over the last thirty years, Bayesian inference has revolutionized statistics --- a discipline that is fraught with controversies, filled with individuals who hold strong opinions, and marred by a poor public image. In this tutorial, I (a) give a brief historical overview of statistics as a discipline; (b) provide a hands-on introduction to Bayesian hypothesis testing which provides a viable alternative to classical hypothesis testing; and (c) discuss good Bayesian modeling practices in the context of more complicated statistical models that go beyond simple hypotheses tests; this includes prior specification, model selection, and model checking.

=== Prior to the tutorial ===
The tutorial will have hands-on exercises, so please bring a Laptop (and possibly pen and paper). For (b), we will use JASP (https://jasp-stats.org/) which is a user-friendly, open-source alternative to SPSS that focuses on Bayesian hypothesis testing. For (c), we will use the R package *brms* which interfaces with Stan (https://mc-stan.org/). If you want to follow the hands-on exercises, please install these software ahead of time.

This tutorial assumes no background knowledge of Bayesian statistics. If you want to prepare a little bit, I recommend you check out the following two blog posts:
* https://fdabl.github.io/r/Regularization.html (relevant to part (b) of the tutorial)
* https://fdabl.github.io/r/Law-of-Practice.html (relevant to part (c) of the tutorial)

=== What questions do you have? ===

=== Interested Participants ===
* Bakus
* Arta
* Elissa
* Robert
* Toni
* Kate

== Collaborative listening and emergent computation through social dance ==
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us (4:30-5:30) and follow it up with a little social dance practice and play time (5:30-6:00). Come learn to walk with four feet and listen with your heart!
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) Leads and follows can be any gender and role-swap is welcome! (In fact in Argentine tango, this is tradition--back in the day, men were only allowed to dance with women after they've spent 2-3 years learning by following other men!)

The Tuesday workshop will take place in the 2nd floor lounge of the dorms. The Wednesday and Thursday workshops will meet in the dance studio in the fitness center (from the main entrance, go down the hall and turn the corner to the left; the door will be on your right).

Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!

=== Suggested Date and Time ===
We realize this is slightly short notice-- if you're really enthusiastic but can't make these times, please let us know and we'll consider rescheduling for next week.

=== Tuesday, July 25, 4:30-6:00pm, dorm 2nd floor - Salsa [On1] (Luther + Jessica) ===
==== Interested Participants ====
* Kate
* Anshuman
* Chris Quarles

=== Wednesday, July 26, 4:30-6:00pm, dance studio - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===
==== Interested Participants ====
* Kate
* Anshuman

=== Thursday, July 27, 4:30-6:00pm dance studio - Argentine Tango (Adam + Jessica) ===
==== Interested Participants ====
*Adam
*Winnie
*Kate
*Anshuman

== Learning to Flow: Morning Yoga Edition ==
Join Elissa in the dance studio on Tuesday and Thursday mornings for a relaxing, yet empowering, vinyasa flow! We'll learn together to link breath to movement, as we find space to flow with ease. No experience necessary! Mats available in the dance studio.

=== Thursday, June 13, 7:00-7:50am ===

=== Tuesday, June 18 and Thursday, June 20, 7:00-7:50am ===

=== Thursday, June 27, 7:00-7:50am ===

=== Tuesday, July 2 and Thursday, July 4, 7:00-7:50am ===

= Completed Tutorials =

==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==

Hi I'm Brennan. And I'm Hunter. And I'm Alec. (*in unison*) And we like networks. Specifically we would like to offer some support / tutorials to anyone who would like to learn about network science (e.g., structure, dynamics, visualization, etc.), all in python. We've got a few things we would love to cover, but on top of that, if there are specific questions / tools that anybody would like us to cover, include them below (with hyperlinks if possible), and we'll see if we can tie it in. The goal is that attendees will leave with 1) new friends, 2) a joie de vivre for the network science life and 3) new Jupyter notebook(s) with fun python code that you can build upon in your own work.

=== Prior to the tutorial ===

Github link '''[https://github.com/jkbren/network-tutorial-csss19 here]'''! The README.md will walk you through installing the main packages and software we'll be using. These mainly include:
* Jupyter notebooks
* networkx
* numpy
* scipy
* matplotlib

=== Wish-list of topics ===

* Network visualization in networkx
* Disease / spreading dynamics
* Community detection and modularity in networks

=== Interested Participants ===
* Al(ec)
* Hunter
* Brennan
* David
* Laura
* Patrick
* Erwin
* Bakus
* April
* Arta
* Dries
* Ian
* Elissa
* Andrea
* Kate
* Billy
* Pam
* Luther
* Koissi
* Kazu
* Ludvig

==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==

Classical statistics does not get much love these days with all the newer techniques. While I applaud these new techniques and use them myself, I think there is value in these older methods. In particular, classical statistics is an excellent framework for thinking about replication. I envision this as the first in a series of three talks but for now I am announcing one. I will cover hypothesis testing with minimal prerequisites. My focus will be on the logic behind hypothesis testing and common misunderstandings thereof.

=== Suggested Date and Time ===
I am willing to move this if desired. I will find a classroom and update this space.

=== Interested Participants ===
Sign up is not required but it would be helpful to have some idea.
* Patrick
* Kate
* Pam
* Arta
* Shihui
* Yuka

== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==

I've taught upper division/intro graduate level Nonlinear Dynamics a couple of times before. Given the quick pace of some of the lectures by the SFI faculty and people's various levels of familiarity with this material, I'd be glad to lead a couple of review/question and answer/clarification sessions for any of the Nonlinear Dynamics lectures (Liz Bradley, Josh Garland, Dave Feldman, Vicky Yang) if anybody is interested. I would also be glad to consult on any projects involving dynamical systems. The idea is to keep it pretty informal, low key, and organic. All levels of expertise welcome!

If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].

Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:
* Taylor series and linearization of nonlinear systems
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from
* Floquet multipliers and diverge of trajectories in maps
* Where the quadratic term in the logistic map comes from

== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==
Took an in-depth look at dynamics and bifurcations in 1D flows

==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:
* 1D maps
* Period doubling route to chaos

== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==

I thought that doing an introductory level tutorial in Natural Language Processing and Computational Linguistics in Python would be useful/fun - it usually adds a very informative level of complexity to projects, even when it isn't the primary mode of inquiry. If you don't have textual data, I can also guide you through the process of mining data off the internet, either through web scraping or twitter - you can also do cool stuff like mailing entire WhatsApp chat histories to yourself, which means we could also do some funky meta Santa Fe WhatsApp chat analysis!

I've conducted similar tutorials before ([https://www.youtube.com/watch?v=mWSs325tGoc&t=70s PyData LA 2018], [https://www.youtube.com/watch?v=ZkAFJwi-G98&t=6s PyData Berlin 2017]), and I also share all my material on GitHub in the form of [https://github.com/bhargavvader/personal/tree/master/notebooks/text_analysis_tutorial Jupyter Notebooks].
I've linked the videos and code so that you can have a brief look to see if it's stuff you might be interested in.

I'd be doing:

* finding text data
* pre-processing text data
* identifying your problem
* part-of-speech tagging, named entity recognition
* topic modelling
* text classification
* text generation with neural nets
* word embeddings

=== Preparing for the tutorial ===

Following the instructions under the setup section in [https://github.com/bhargavvader/personal/tree/master/notebooks/text_analysis_tutorial this link] will help a bunch! I will spend the first 20 minutes helping with setup before moving on. If you would want to run all the code in the tutorial while I am, you would need [[pythonhttps://www.python.org/downloads/|python]] and [https://jupyter.org/install jupyter] installed.

=== Suggested Date and Time ===
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!

=== Interested Participants ===
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!) 

# Bhargav (presenter) 
# Arta Cika
# Xin Ran
# Daniel Borrero
# Jackie Brown
# Pam Mantri
# Dee Romo
# Jeongki Lim
# Ernest Aigner
# Robert Coulter
# Winnie Poel
# Travis Moore
# Pablo M. Flores
# Catherine Brinkley
# Andrew Gillreath-Brown
# Kate
# Bakus
# Dries
# Bhartendu
# Kenzie Givens
# Wenqian
# Jordi
# Elissa

=== Note ===

This would require pretty basic python programming skills, but I'll be walking everyone through the code. Even if you can't code it might be useful to know what kind of problems you can solve, and I'd be happy to link to resources to learning enough python to get started on your own. There has been interest in doing a general Machine Learning tutorial too: we can talk about this during the text tutorial to figure out what might be most useful for everyone!

I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described!

([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)

==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==

Agent-based modelling can be a powerful for modelling complex system problems. But what *is* agent-based modelling? And how do we go about it in a structured and scientific way? And once we've made a model... what do we do with it? I have a background in policy analysis and simulation studies, and am offering this tutorial for those interested in using ABM (specifically NetLogo, as it is very accessible) in current or future work. I will cover some basic systems simulation theory, go over one structured method of making ABMs (from Agent-Based Modelling of Socio-Technical Systems, eds. van Dam, Nikolic, and Lukszo), and finally look at some ways the finished model could be used/explored - specifically, sensitivity analysis and scenario discovery. We will also briefly look at how NetLogo can be connected to tools such as Python, R, and Mathematica, and what possibilities that opens up.

I would also be glad to consult on any projects involving ABM/systems simulation.

=== Suggested Date and Time ===
Tuesday 18JUN, 7:00 PM, lecture hall.

=== Interested Participants ===
Sign up below in the bulleted list below if you are interested. If you have experience with ABM and would like to share your expertise, please feel free to join. You can add more slots as needed:
* Patrick Steinmann (presenter)
* Jeongki Lim
* Dries Daems
* Bhartendu Pandey
* Travis
* Pam
* Arta
* Ludvig
* Ian
* Wenqian
* Jordi
* Elissa
* Bakus
* Andrew
* Luther

If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].

==Data Visualization and Aesthetics - [https://github.com/eonadler/Data-Visualization/blob/master/Matplotlib%20and%20Data%20Visualization%20Tutorial.ipynb Ethan Nadler] (8:00 PM 6/19)==

This will be a tutorial/"formal" discussion (i.e. with slides) aimed at data visualization in science, and its relation to art and aesthetics. It will roughly be organized as follows, depending on interest:

1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;

2. Discussion of specific examples: each attendee will send a favorite plot/visualization that *they have made* (likely from past research), and we'll discuss each as a group;

3. Discussion of general principles: interesting topics include, but are not limited to:
* What makes a plot beautiful?
* Do scientific data visualization and art have the same aesthetic aims?
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)

=== Suggested Date and Time ===
8:00 PM on Wednesday, 6/19.

=== Interested Participants ===
* Ethan Nadler (presenter)
* Daniel Borrero
* Arta Cika
* Kenzie Givens
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)
* Patrick
* Erwin
* Kate
* Bakus
* Bhartendu
* Ernest
* Travis
* Pam
* Henri
* Ludvig
* Ignacio
* Mikaela
* Winnie
* Andrew
* John Malloy
* Ian
* Chris B-J

==Distribution Fitting and Maximum Likelihood Estimation - [https://wiki.santafe.edu/index.php/Christopher_Quarles Chris Quarles] (2:00 PM, Thursday 6/20 in Distance Learning Room 2)==

Researchers and statistics students regularly assume that their data is normally distributed, and network degree distributions are often assumed to follow a power law. These are typically incorrect assumptions. It is important to examine the ''shape'' of the data. And, if the data reasonably fits a nice, parametric shape, we might want to infer the best parameter(s) for that shape. For a power law, the parameter is the exponent. For normally distributed data, we might want to infer the mean and standard deviation. This can give insight about the process that generated the data and the analyses that we can do with it. Maximum likelihood estimation (MLE) is the workhorse method to do this distribution fitting.

In this workshop, you will learn how to fit distributions using MLE, and when it might be useful. I'll go over the basic ideas behind distribution fitting, including likelihood and log-likelihood. We will work through the calculation of a maximum likelihood estimator together, and talk about how to choose the best-fit distibution. You'll get the opportunity to do some hands-on calculation and find a best fit distribution for a dataset.

You'll want to bring a pencil and paper/notebook, and a computer with some basic statistical software that you know how to use (R, Python, Excel, etc.). You also will need to be able to take derivatives to get the most of the workshop.

=== Suggested Date and Time ===
Thursday, June 20th at 2:00 PM. 
This week is filling up with tutorials. If there are enough people interested, I can do this again during week 3. Text me on Slack if you can't make this, and would rather do it the following week.

=== Interested Participants ===
* Jessica Lee
* Bhartendu
* Henri
* Wenqian
* Arta
* Elissa
* Mikaela

==Kirsten Moy, 7:00pm, Tuesday June 17==

[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along

From her description:

A review of highlights from four other case studies in addition to Detroit on the utilization of complexity thinking in community development. Case studies include a microenterprise development organization in the San Francisco area that works from an ecosystem perspective; a national organization that brings NGOs and City Government together in a dozen cities to create greater financial security for low and moderate-income families; an organization that provides support to family networks in different cities to collectively bring people out of poverty; and the only community revitalization nonprofit in the US (now in 18 cities) that consciously and intentionally works from a complexity science framework.

Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.

Please sign up so we have some idea of who will be around and can choose the appropriate room

# JP
# Ahyan Panjwani
# Dee
# Ignacio
# Winnie
# Elissa

Larremore Workshop: How To Rank Things & Pairwise Comparisons 2019

2019-06-24T00:38:49Z

ChrQua:

{{Complex Systems Summer School 2019}}

Dan Larremore Workshop

Monday, June 24 @ 3:15pm in the conference room.

During lectures on networks and hierarchies, we discussed how a network representing the outcomes of discrete choices—Which wine is preferred? Swipe right or swipe left? Which team won the match?—can be used to find a ranking. In this workshop, we'll apply these methods by tasting, comparing, and ranking dozes of different salsas to find the most delicious. We'll also discuss topics like tournament design, adaptive tournaments, and the difference between a marketing study of salsa quality and a bracket-based tournament like the World Cup.

LARREMORE WORKSHOP LOTTO SIGNUP!!

# JP
# Andrew Gillreath-Brown
# Ritwika
# Ernest
# Aabir
# Alec
# Brennan
# Fabian
# Elissa
# Erwin
# Andrea
# Hunter
# Ian
# Pam
# Jessica Lee
# Dries
# Yuka
# Arta
# Kazu
# Chris Quarles

Complex Systems Summer School 2019-Tutorials

2019-06-20T16:14:38Z

ChrQua:

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-17T23:42:59Z

ChrQua:

{{Complex Systems Summer School 2019}}

Project and working group ideas go here.

==Two ideas from Cat==

The first two ideas are related to datasets that I can make available. I am dedicated to publishing results from both- and co-authorship is welcome if you are interested.

This first idea relates is a Natural Language Processing project with spatial aspects. I have gathered all 482 city and 58 county general plans for California. I have these plans available as both PDFs and with text extracted. These are 400+ page documents that communities put together in order to set the course for developing housing, transportation systems, green space, conservation, etc. This dataset is exciting because no state has a database of city/county plans- and these plans govern land-use. California offers an interesting case because there are mountains, beaches, rural areas, agricultural areas, dessert landscapes and the coast. Each landscape and population will require unique planning. We could use the dataset to answer a variety of questions.
We could ask some simple questions with sentiment analysis (who wrote the happiest plans? Are rural areas the most disparaging in their plans- or are urban areas?)
We could train a model on state recommendations for plans and see which plans fit (my hypothesis is that plans closest to Sacramento, the state capitol, fit the best). The take away would be that providing 'best practices' for planning is difficult because places and communities are so different in resources and objectives (eg. most rural areas do not want population growth, many urban areas measure success by population growth)..
We could also take a topical approach. How much housing is each city/county planning to build in housing-stressed California? How do plans talk about fire prevention management (eg. in the context of housing? transportation? forest management?). How are communities planning for GHG reduction (with a focus mainly on air quality? A focus mainly on transportation? what about energy systems?)

The second project relates to my dissertation and builds into the science of cities. This project would use spatial regression. I hypothesize that cities are like coral reef ecosystems where structural complexity begets more habitat niches and more species diversity, leading to greater total ecosystem resilience g. faster recovery from disease or disaster). I hypothesize that cities might be the same way- more structural complexity (longer urban perimeters in the case of my dataset- but we could use 3d city models as well) would lead to greater land-use diversity and more job diversity- which would help protect against economic downturn. None of the data is normally distributed- so the spatial regression is challenging.

Added by Jessica: So a way that we could evaluate the complexity and information is a method called ascendency. It is basically the same information index calculated Joshua Garland showed us and informs us about the diversity of the networks. Interestingly, years ago when I plotted this information against productivity/Biomass/energy, it got some Lorenz patterns. If we could find a way to model a perturbation in the system, that would make for some interesting predictive analysis.

===Interested Participants ===
Jessica Brumley
Dee Romo

==Dangerous idea about reviewing==

Dan and I came up with this really dangerous idea to break academia over lunch.
Reviewer # 2 is AI: We could use existing publications (eg. PlosOne) to train a model. Any paper that is uploaded for review would be reviewed by AI Reviewer #2. The review would take minutes, and would likely result in rejection or accept with modification. The AI could tell you where your paper fits in the broader scholarship on this topic. Does your paper bring together unique disciplines/ideas or test new hypotheses? How many papers have already been published on this topic- and how do your findings compare with regard to sample size, methodology, spatial and temporal context? In essence, have you found an anomaly- or is there more evidence to support a general theory. Where publicly available data exists, the AI could repeat analyses to verify findings. The AI could easily tell you where you have missed out on citing important works- or have been biased in citing the later work of a man over the foundational work of a woman or person of color (eg. everyone cites Robert Putnam for social capital and not Jane Jacobs).
Such a reviewer would provide sentiment analyses by discipline (eg. Economics still loves Garrett Hardin's Tragedy of the Commons over Elinor Ostrom's work on the Commons. But all other disciplines are ready to kill Hardin's work)
The second phase of this would use predictive modeling. reviewer #2 would write papers- predict new theories. This work would start with literature reviews (as any good PhD student would)- and then move into analyzing public datasets to answer new questions. We could check in after 10 years of human publication time had elapsed (eg. about 5-10 papers)- or 50 years... and see where science went. We could toggle the inputs (more hard sciences or more social sciences) to see how this changed the output and trajectory of science. The real world application could mean that we could do science with very little funding- and we would all be out of a job.

==Emergence of cooperative strategies by means of ''game warping'', using network science==

(From Shruti)

What if players can transform a noncooperative game to a cooperative positive-sum game? This is possible in certain digital economic systems (such as those on a blockchain) because all contracts are strictly enforceable. These type of "game-warping" transformations are interesting because given any economic model with pre-defined rules, the agents are able to develop unforeseeable cooperation strategies, form coalitions, and expand the scope of potential actions over time. Effectively, players are collectively able to overturn the system dynamics. The economy evolves because the economic rules effectively change w/ time (anyone play Baba Is You?). "Game warping" is defined as using transparent, triggerable, unstoppable punishments to move game-theoretic equilibria. We can extend this to multiple players and model the system using a graph/network, to explore what different cooperation strategies emerge. I trust that studying these systems at a macro-level, using simulations or networks will bring greatest degree of insight and set this research apart. David Wolpert's (SFI) work on "game mining" is also relevant. <ref>https://www.santafe.edu/news-center/news/wolpert-aaec-game-mining</ref>
[[File:Game warping .png]]
Citation: https://medium.com/@virgilgr/ethereum-is-game-changing-technology-literally-d67e01a01cf8

==Simulating evolution of bacterial cells’ decision to divide==

(From Kunaal)

How do cells decide when is the right time to divide? From a purely efficiency-based perspective, cells can obtain nutrients at a rate proportional to their surface area, but nutrient requirement for growth has a rate proportional to volume of the cell. Thus, there will be a cell size that is optimum for division.

The problem with this reasoning is, cells will tend to divide at the same size on average, irrespective of their initial size. But we know that in most bacterial species, cells that start out small (large) tend to divide at a size smaller (larger) than the average size at division.

This indicates there is a different reason behind cells’ decision to divide. It is an optimal path chosen by evolution, and I intend to simulate cells susceptible to mutations under different conditions to understand how this division mechanism arises through evolution and why it is optimal.

Join #cell-division-sim on Slack if you are interested.

==Modelling the spatial diffusion of human languages==

The diversification of human languages is a bit like speciation in biology: using comparative and cladistic methods, linguists can group languages into language families and further subgroup them into "phylogenetic" trees or networks. At the same time, we know where these languages are spoken today. The question, then: putting these two sources of data together, can we model the diffusion of languages over physical space and work backwards from the present day to infer the most likely homelands of the corresponding protolanguages? Can the predictions of such a model be made to align what we otherwise know about human migrations in the past? And most importantly (I think), from a complex systems perspective: ''what facets of the processes of linguistic diffusion and diversification are universal'' (i.e. not due to accidental historical events)? We could start with a simple random-walk model and take it from there. Slack channel is #language-diffusion.

'''First meeting: Friday 1pm, lecture room'''

===Data===

* [http://wals.info World Atlas of Language Structures]
* [https://github.com/hkauhanen/ritwals Same data for R-users]

===Papers to read===

* Let's add them here

===Interested participants===

* [http://henr.in Henri]
* Dee Romo
* Kenzie Givens
* Ritu
* Harun
* Let's add ourselves here

===Future plans===

This is (or can be, if we want) a somewhat ambitious project. I'd be happy to continue working towards a publication after CSSS.

==Butterflies in Water: Optimal Perturbations for Mixing in Treatment Processes==

This idea came from Liz Bradley’s last lecture and her showing us the 2D hurricanes in a box experiment and adding the “butterflies”.

Water treatment processes often need perturbations to mix the water, especially if you need to oxidize and precipitate out a contaminant (iron is a common example). Ultimately you want to do this in the most energy efficient way. The goal when building these systems is to expose the water to the surface area and mix in oxygen (from the atmosphere) for as long as possible. There are various ways to do this: make large surface area ponds; make a “Stream like” pond to make the water flow longer; add small dams for the water to go around; Some people have tried adding poles/sticks to the water; etc. It is yet to be understood which is the most successful method or which might be the optimal level of perturbations for mixing. Could agent based modeling help? Does the mixing and oxidation processes express chaotic behavior?

This is a project that I am seriously thinking about engineering a laboratory model to test as well.

===Interested Participants===
Jessica Brumley

==Computational Social Science in Decision-Making: an Opioid Epidemic Case-Study==

[[File:Css-opioid-simulator.png|thumb]]

''Project write-up from Slack:'' As a part of my ([[Kyle Furlong]]) work, I’ve been developing a tool/application that uses computational social science/agent-based modeling to help decision-makers make better data-driven decisions. I’m using the opioid epidemic as a “case study” for this tool. Using NetLogo and R (RShiny), the tool allows the user to explore the multiple social science theories that describe addiction and perform what-if analyses to determine which public policies/programs might be most effective in reducing negative outcomes (overdoses, deaths, etc).

I’ve got an early prototype UI/code (pictured below) running and have built in some basic theories of addiction that I’ve pulled from the literature, but I’d love to collaborate with anyone who is interested in the topic (addiction, drug use, public health), the methods (NetLogo/ABMs, social networks), and/or the approach. Open to informal coffee/not coffee drinking groups to crowd-source on a conceptual level or more technical groups working to improve my admittedly unrefined addiction models.

===Communication Channels===
Slack Channel: '''#compsocialsci-opioids'''

===Meeting Schedule & Notes===
TBA

===Interested Participants===
Shamelessly pulled from the whiteboard after the project brainstorming session on 6/13/2019:
* John Malloy
* Winnie Poel
* Robert Coulter
* Fabian Dablander
* Dakota Murray
* Xin Ran
* Dee Romo
* Pablo Franco
* David Gier

==Science Policy & Communication==

How is information transferred from scientists to policymakers to constituents? How much information is lost in translation from scientific papers to news articles and tweets? This group will explore the (potential) information loss along each transition, along with other policy-based issues that will emerge from the interaction between scientists and policymakers.

''Direct questions to John Malloy (Slack preferred)''

===Communication Channels===

Slack channel: '''science-policy'''

===Interested Participants (taken from Slack)===
*Andrew GB
*Chris Boyce-Jacino
*Dakota Murrary
*David Gier
*Jackie Brown
*Mackenzie Johnson
*Elissa Cohen
*Jessica Brumley
*Majorie
*Mikaela Akrenius
*Aabir
*Kyle Furlong
*Patrick Steinmann
*Ritu

==Modeling and predicting food insecurity using a resilience lens==
or
Can complex systems help feed the hungry?

Slack channel: '''food-security'''

Over 800 million people are hungry today, and vulnerable to drought, floods and crop-disease driven by climate change. I’m interested in modeling the incidence of hunger as a dynamic, stochastic system using a resilience lens. Would like to see if we can predict the incidence of hunger in response to shocks using a neural net. Got some data to play with and open to exploring different models and predictive algorithms. If we get some promising results, we can showcase them to policymakers at USAID and the World Bank who are very interested in this space.

===Participants===
* Erwin Knippenberg
* Travis Moore
* Ludvig Holmér
* Andrew Gillreath-Brown
* Alexander Bakus
* Pam Mantri
* Dan Krofcheck

==Modeling Minecraft's Crafting Web==
Map the web of natural resource use in Minecraft and its hierarchy of dependencies, including the potentially circular dependencies (ie you need spider silk to make a bow, which you can then use to kill spiders). Can then infer which resources are most used, their trophic level, and what tools are required to produce them.

===Participants===
* Kate Wootton
* Alexander Bakus
* Chris Quarles
* Patrick Steinmann
* Erwin Knippenberg

== Looking for resilient patterns in Conway's Game of Life ==

Resilience to environmental challenges is one of the hallmarks of life. The goal of this project would be to search for patterns in Conway's Game of Life that can cope with external perturbations and self-organize back into their original forms.
Conway's Game of Life[1] is a cellular automaton that has raised a lot of attention due to the life-like forms that it generates. Cellular automata are computational models composed of a grid of cells that can be on either of two (or more) states. At every generation, each of these cells can change according to the state of their neighbours. Interestingly, Conway's Game of Life is Turing-complete, meaning that it can compute any computable function, including the Game of Life itself [2].
For this reason, one should expect to find a wide range of interesting patterns, including those that can detect external perturbations and repair themselves. By finding them, we would be providing a compelling example of one of life's key traits as an emergent behaviour in a simple computational environment.

[1] https://www.youtube.com/watch?v=ouipbDkwHWA

[2] https://imgur.com/T1h2VVS

=== Participants ===
* Alexander Schaefer
* Dan Krofcheck
* Kazuya Horibe
* Arta Cika
* Elissa Cohen
* Luther Seet
* Patrick Steinmann
* Germán Kruszewski
* Wenqian Yin

== Analyzing Collaboration Throughout CSSS History ==

How has the nature of collaboration at CSSS changed over time? Using project and participant data from the last 20 years of the program, we plan to explore how topics and group structures have changed over time. Have groups become more interdisciplinary? Is there a pattern to the types of projects that individuals from particular fields tend to work on?

=== Participants ===
* Dakota
* Emily
* Fabian
* Jackie
* Kyle

== Multi-scale inequalities and cities ==

Increases in inequality and urbanization are two of the challenges facing global sustainable development. However, inequalities in the urban context are conventionally understood by analyzing one city at a time, which limits a multi-scalar understanding. This project proposes to investigate whether there are general patterns in how inequalities manifest across spatial scales and regional contexts and examine the relationships between urban networks and inequalities.

=== Participants ===

* Bhartendu Pandey
* Christopher Quarles
* Alec Kirkley

== Lingua Technica: The impact of technology on language ==

Technology and language are related—words like "delete", "reboot", and "reset" only became prominent in our language with the introduction of computing. In other cases, language adopts metaphors of technology such as in phrases like "I'm Dying", "I'm losing you", and "They act like a robot". In this project we will analyze the uptake of such terms in English language text over the past decades. We hope to assess the extent and speed to which technical metaphors are adopted in a variety of mediums. We We will begin with words relating to computing and extent to other technologies such as cars, medicine, and more.

=== Participants ===

* Dakota Murray

==Artificial fossilization of animal interaction networks==

There has been a rapid increase in the number of papers applying network analysis to ancient communities, inferred from the fossil record. However, many of these studies don't account for the fact that the fossil record is incomplete. For example, most soft-bodied organisms don't preserve well. We hope to ground-truth investigations of past processes by analyzing how information loss affects the structure of modern interaction networks (co-occurrence, food webs, etc) and the inferences we make from them.

=== Participants ===

* Emily Coco
* Jack Shaw
* Andrew Gillreath-Brown
* Anshuman Swain
* Kate Wootton
* Dries Daems

== The Time Traveler's Tree: What Did Sci-Fi Writers want? ==

Throughout the 20th century, science fiction writers were busy imagining possible futures, using advanced scientific and technological concepts as a vehicle for their thoughts about the present and the future of the human race. When did we start talking about flying cars, when did we foreshadow the invention of waterbeds (Heinlein did it!) and where do the branches of the fictional tree loop into the branches of the real technological tree of the 20th and 21st century? We explore this by creating a dataset of fundamental scientific and technological ideas appearing in sci-fi classics of our time, primarily novels that have won the Hugo or Nebula award.

=== Participants ===

* Harun Siljak

== Big Brother's Agents: Modelling Sci-Fi Communities ==

How to start a rebellion in the total surveillance society of Orwell's 1984? Is it a case for an agent-based model, or maybe a network, or a cellular automaton? Could an emergent strategy bring down the Death Star? What made the Battle of Winterfell so wrong? This project investigates the great narratives of fiction and fantasy through complex systems modelling.

=== Participants ===

* Harun Siljak

== CSSS Social Network Study ==

Investigating the structural and dynamical properties of the social network formed by participants in the CSSS, incorporating node-level metadata.

=== Participants ===

* Alec Kirkley
* Shihui Feng
* Dr. Kenneth Hunter Wapman III, MD
* Kate Wootton

==Self organizing city==

Exploring emergence and how a city can evolve and be shaped by social interactions. Planned cities and organically developed cities all have a network of public spaces. This looks at the use of agent based modelling and adaptive networks to study both the formation and resilience of public space networks in cities.

=== Participants ===

* Luther Seet
* German Kruszewski
* Chris Boyce-Jacino
* Kazuya Horibe
* Jackie Brown
* Bhartendu Pandey
* Please add on

==Too Much Information and Segregation==

Every entity has a limited capacity to process information. So, when there is too much information, entities need to exclude information that does not benefit them. What happens when there are increases in the amount of information available, such as when technology allows a place-based society to transition to a more connected one? Individuals will have more options, and will also need to be more selective about the information they receive. Does this lead to increased segregation and/or specialization in a social system and/or biological system? We are approaching these questions using a network model, where nodes update their filters based on a utility function.

=== Participants ===

* Christopher Quarles

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-17T22:40:01Z

ChrQua:

{{Complex Systems Summer School 2019}}

Project and working group ideas go here.

==Two ideas from Cat==

The first two ideas are related to datasets that I can make available. I am dedicated to publishing results from both- and co-authorship is welcome if you are interested.

This first idea relates is a Natural Language Processing project with spatial aspects. I have gathered all 482 city and 58 county general plans for California. I have these plans available as both PDFs and with text extracted. These are 400+ page documents that communities put together in order to set the course for developing housing, transportation systems, green space, conservation, etc. This dataset is exciting because no state has a database of city/county plans- and these plans govern land-use. California offers an interesting case because there are mountains, beaches, rural areas, agricultural areas, dessert landscapes and the coast. Each landscape and population will require unique planning. We could use the dataset to answer a variety of questions.
We could ask some simple questions with sentiment analysis (who wrote the happiest plans? Are rural areas the most disparaging in their plans- or are urban areas?)
We could train a model on state recommendations for plans and see which plans fit (my hypothesis is that plans closest to Sacramento, the state capitol, fit the best). The take away would be that providing 'best practices' for planning is difficult because places and communities are so different in resources and objectives (eg. most rural areas do not want population growth, many urban areas measure success by population growth)..
We could also take a topical approach. How much housing is each city/county planning to build in housing-stressed California? How do plans talk about fire prevention management (eg. in the context of housing? transportation? forest management?). How are communities planning for GHG reduction (with a focus mainly on air quality? A focus mainly on transportation? what about energy systems?)

The second project relates to my dissertation and builds into the science of cities. This project would use spatial regression. I hypothesize that cities are like coral reef ecosystems where structural complexity begets more habitat niches and more species diversity, leading to greater total ecosystem resilience g. faster recovery from disease or disaster). I hypothesize that cities might be the same way- more structural complexity (longer urban perimeters in the case of my dataset- but we could use 3d city models as well) would lead to greater land-use diversity and more job diversity- which would help protect against economic downturn. None of the data is normally distributed- so the spatial regression is challenging.

Added by Jessica: So a way that we could evaluate the complexity and information is a method called ascendency. It is basically the same information index calculated Joshua Garland showed us and informs us about the diversity of the networks. Interestingly, years ago when I plotted this information against productivity/Biomass/energy, it got some Lorenz patterns. If we could find a way to model a perturbation in the system, that would make for some interesting predictive analysis.

===Interested Participants ===
Jessica Brumley
Dee Romo

==Dangerous idea about reviewing==

Dan and I came up with this really dangerous idea to break academia over lunch.
Reviewer # 2 is AI: We could use existing publications (eg. PlosOne) to train a model. Any paper that is uploaded for review would be reviewed by AI Reviewer #2. The review would take minutes, and would likely result in rejection or accept with modification. The AI could tell you where your paper fits in the broader scholarship on this topic. Does your paper bring together unique disciplines/ideas or test new hypotheses? How many papers have already been published on this topic- and how do your findings compare with regard to sample size, methodology, spatial and temporal context? In essence, have you found an anomaly- or is there more evidence to support a general theory. Where publicly available data exists, the AI could repeat analyses to verify findings. The AI could easily tell you where you have missed out on citing important works- or have been biased in citing the later work of a man over the foundational work of a woman or person of color (eg. everyone cites Robert Putnam for social capital and not Jane Jacobs).
Such a reviewer would provide sentiment analyses by discipline (eg. Economics still loves Garrett Hardin's Tragedy of the Commons over Elinor Ostrom's work on the Commons. But all other disciplines are ready to kill Hardin's work)
The second phase of this would use predictive modeling. reviewer #2 would write papers- predict new theories. This work would start with literature reviews (as any good PhD student would)- and then move into analyzing public datasets to answer new questions. We could check in after 10 years of human publication time had elapsed (eg. about 5-10 papers)- or 50 years... and see where science went. We could toggle the inputs (more hard sciences or more social sciences) to see how this changed the output and trajectory of science. The real world application could mean that we could do science with very little funding- and we would all be out of a job.

==Emergence of cooperative strategies by means of ''game warping'', using network science==

(From Shruti)

What if players can transform a noncooperative game to a cooperative positive-sum game? This is possible in certain digital economic systems (such as those on a blockchain) because all contracts are strictly enforceable. These type of "game-warping" transformations are interesting because given any economic model with pre-defined rules, the agents are able to develop unforeseeable cooperation strategies, form coalitions, and expand the scope of potential actions over time. Effectively, players are collectively able to overturn the system dynamics. The economy evolves because the economic rules effectively change w/ time (anyone play Baba Is You?). "Game warping" is defined as using transparent, triggerable, unstoppable punishments to move game-theoretic equilibria. We can extend this to multiple players and model the system using a graph/network, to explore what different cooperation strategies emerge. I trust that studying these systems at a macro-level, using simulations or networks will bring greatest degree of insight and set this research apart. David Wolpert's (SFI) work on "game mining" is also relevant. <ref>https://www.santafe.edu/news-center/news/wolpert-aaec-game-mining</ref>
[[File:Game warping .png]]
Citation: https://medium.com/@virgilgr/ethereum-is-game-changing-technology-literally-d67e01a01cf8

==Simulating evolution of bacterial cells’ decision to divide==

(From Kunaal)

How do cells decide when is the right time to divide? From a purely efficiency-based perspective, cells can obtain nutrients at a rate proportional to their surface area, but nutrient requirement for growth has a rate proportional to volume of the cell. Thus, there will be a cell size that is optimum for division.

The problem with this reasoning is, cells will tend to divide at the same size on average, irrespective of their initial size. But we know that in most bacterial species, cells that start out small (large) tend to divide at a size smaller (larger) than the average size at division.

This indicates there is a different reason behind cells’ decision to divide. It is an optimal path chosen by evolution, and I intend to simulate cells susceptible to mutations under different conditions to understand how this division mechanism arises through evolution and why it is optimal.

Join #cell-division-sim on Slack if you are interested.

==Modelling the spatial diffusion of human languages==

The diversification of human languages is a bit like speciation in biology: using comparative and cladistic methods, linguists can group languages into language families and further subgroup them into "phylogenetic" trees or networks. At the same time, we know where these languages are spoken today. The question, then: putting these two sources of data together, can we model the diffusion of languages over physical space and work backwards from the present day to infer the most likely homelands of the corresponding protolanguages? Can the predictions of such a model be made to align what we otherwise know about human migrations in the past? And most importantly (I think), from a complex systems perspective: ''what facets of the processes of linguistic diffusion and diversification are universal'' (i.e. not due to accidental historical events)? We could start with a simple random-walk model and take it from there. Slack channel is #language-diffusion.

'''First meeting: Friday 1pm, lecture room'''

===Data===

* [http://wals.info World Atlas of Language Structures]
* [https://github.com/hkauhanen/ritwals Same data for R-users]

===Papers to read===

* Let's add them here

===Interested participants===

* [http://henr.in Henri]
* Dee Romo
* Kenzie Givens
* Ritu
* Harun
* Let's add ourselves here

===Future plans===

This is (or can be, if we want) a somewhat ambitious project. I'd be happy to continue working towards a publication after CSSS.

==Butterflies in Water: Optimal Perturbations for Mixing in Treatment Processes==

This idea came from Liz Bradley’s last lecture and her showing us the 2D hurricanes in a box experiment and adding the “butterflies”.

Water treatment processes often need perturbations to mix the water, especially if you need to oxidize and precipitate out a contaminant (iron is a common example). Ultimately you want to do this in the most energy efficient way. The goal when building these systems is to expose the water to the surface area and mix in oxygen (from the atmosphere) for as long as possible. There are various ways to do this: make large surface area ponds; make a “Stream like” pond to make the water flow longer; add small dams for the water to go around; Some people have tried adding poles/sticks to the water; etc. It is yet to be understood which is the most successful method or which might be the optimal level of perturbations for mixing. Could agent based modeling help? Does the mixing and oxidation processes express chaotic behavior?

This is a project that I am seriously thinking about engineering a laboratory model to test as well.

===Interested Participants===
Jessica Brumley

==Computational Social Science in Decision-Making: an Opioid Epidemic Case-Study==

[[File:Css-opioid-simulator.png|thumb]]

''Project write-up from Slack:'' As a part of my ([[Kyle Furlong]]) work, I’ve been developing a tool/application that uses computational social science/agent-based modeling to help decision-makers make better data-driven decisions. I’m using the opioid epidemic as a “case study” for this tool. Using NetLogo and R (RShiny), the tool allows the user to explore the multiple social science theories that describe addiction and perform what-if analyses to determine which public policies/programs might be most effective in reducing negative outcomes (overdoses, deaths, etc).

I’ve got an early prototype UI/code (pictured below) running and have built in some basic theories of addiction that I’ve pulled from the literature, but I’d love to collaborate with anyone who is interested in the topic (addiction, drug use, public health), the methods (NetLogo/ABMs, social networks), and/or the approach. Open to informal coffee/not coffee drinking groups to crowd-source on a conceptual level or more technical groups working to improve my admittedly unrefined addiction models.

===Communication Channels===
Slack Channel: '''#compsocialsci-opioids'''

===Meeting Schedule & Notes===
TBA

===Interested Participants===
Shamelessly pulled from the whiteboard after the project brainstorming session on 6/13/2019:
* John Malloy
* Winnie Poel
* Robert Coulter
* Fabian Dablander
* Dakota Murray
* Xin Ran
* Dee Romo
* Pablo Franco
* David Gier

==Science Policy & Communication==

How is information transferred from scientists to policymakers to constituents? How much information is lost in translation from scientific papers to news articles and tweets? This group will explore the (potential) information loss along each transition, along with other policy-based issues that will emerge from the interaction between scientists and policymakers.

''Direct questions to John Malloy (Slack preferred)''

===Communication Channels===

Slack channel: '''science-policy'''

===Interested Participants (taken from Slack)===
*Andrew GB
*Chris Boyce-Jacino
*Dakota Murrary
*David Gier
*Jackie Brown
*Mackenzie Johnson
*Elissa Cohen
*Jessica Brumley
*Majorie
*Mikaela Akrenius
*Aabir
*Kyle Furlong
*Patrick Steinmann
*Ritu

==Modeling and predicting food insecurity using a resilience lens==
or
Can complex systems help feed the hungry?

Slack channel: '''food-security'''

Over 800 million people are hungry today, and vulnerable to drought, floods and crop-disease driven by climate change. I’m interested in modeling the incidence of hunger as a dynamic, stochastic system using a resilience lens. Would like to see if we can predict the incidence of hunger in response to shocks using a neural net. Got some data to play with and open to exploring different models and predictive algorithms. If we get some promising results, we can showcase them to policymakers at USAID and the World Bank who are very interested in this space.

===Participants===
* Erwin Knippenberg
* Travis Moore
* Ludvig Holmér
* Andrew Gillreath-Brown
* Alexander Bakus
* Pam Mantri
* Dan Krofcheck

==Modeling Minecraft's Crafting Web==
Map the web of natural resource use in Minecraft and its hierarchy of dependencies, including the potentially circular dependencies (ie you need spider silk to make a bow, which you can then use to kill spiders). Can then infer which resources are most used, their trophic level, and what tools are required to produce them.

===Participants===
* Kate Wootton
* Alexander Bakus
* Chris Quarles
* Patrick Steinmann
* Erwin Knippenberg

== Looking for resilient patterns in Conway's Game of Life ==

Resilience to environmental challenges is one of the hallmarks of life. The goal of this project would be to search for patterns in Conway's Game of Life that can cope with external perturbations and self-organize back into their original forms.
Conway's Game of Life[1] is a cellular automaton that has raised a lot of attention due to the life-like forms that it generates. Cellular automata are computational models composed of a grid of cells that can be on either of two (or more) states. At every generation, each of these cells can change according to the state of their neighbours. Interestingly, Conway's Game of Life is Turing-complete, meaning that it can compute any computable function, including the Game of Life itself [2].
For this reason, one should expect to find a wide range of interesting patterns, including those that can detect external perturbations and repair themselves. By finding them, we would be providing a compelling example of one of life's key traits as an emergent behaviour in a simple computational environment.

[1] https://www.youtube.com/watch?v=ouipbDkwHWA

[2] https://imgur.com/T1h2VVS

=== Participants ===
* Alexander Schaefer
* Dan Krofcheck
* Kazuya Horibe
* Arta Cika
* Elissa Cohen
* Luther Seet
* Patrick Steinmann
* Germán Kruszewski

== Analyzing Collaboration Throughout CSSS History ==

How has the nature of collaboration at CSSS changed over time? Using project and participant data from the last 20 years of the program, we plan to explore how topics and group structures have changed over time. Have groups become more interdisciplinary? Is there a pattern to the types of projects that individuals from particular fields tend to work on?

=== Participants ===
* Dakota
* Emily
* Fabian
* Jackie
* Kyle

== Multi-scale inequalities and cities ==

Increases in inequality and urbanization are two of the challenges facing global sustainable development. However, inequalities in the urban context are conventionally understood by analyzing one city at a time, which limits a multi-scalar understanding. This project proposes to investigate whether there are general patterns in how inequalities manifest across spatial scales and regional contexts and examine the relationships between urban networks and inequalities.

=== Participants ===

* Bhartendu Pandey
* Christopher Quarles

== Lingua Technica: The impact of technology on language ==

Technology and language are related—words like "delete", "reboot", and "reset" only became prominent in our language with the introduction of computing. In other cases, language adopts metaphors of technology such as in phrases like "I'm Dying", "I'm losing you", and "They act like a robot". In this project we will analyze the uptake of such terms in English language text over the past decades. We hope to assess the extent and speed to which technical metaphors are adopted in a variety of mediums. We We will begin with words relating to computing and extent to other technologies such as cars, medicine, and more.

=== Participants ===

* Dakota Murray

==Artificial fossilization of animal interaction networks==

There has been a rapid increase in the number of papers applying network analysis to ancient communities, inferred from the fossil record. However, many of these studies don't account for the fact that the fossil record is incomplete. For example, most soft-bodied organisms don't preserve well. We hope to ground-truth investigations of past processes by analyzing how information loss affects the structure of modern interaction networks (co-occurrence, food webs, etc) and the inferences we make from them.

=== Participants ===

* Emily Coco
* Jack Shaw
* Andrew Gillreath-Brown
* Anshuman Swain
* Kate Wootton
* Dries Daems

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-17T22:39:37Z

ChrQua:

{{Complex Systems Summer School 2019}}

Project and working group ideas go here.

==Two ideas from Cat==

The first two ideas are related to datasets that I can make available. I am dedicated to publishing results from both- and co-authorship is welcome if you are interested.

This first idea relates is a Natural Language Processing project with spatial aspects. I have gathered all 482 city and 58 county general plans for California. I have these plans available as both PDFs and with text extracted. These are 400+ page documents that communities put together in order to set the course for developing housing, transportation systems, green space, conservation, etc. This dataset is exciting because no state has a database of city/county plans- and these plans govern land-use. California offers an interesting case because there are mountains, beaches, rural areas, agricultural areas, dessert landscapes and the coast. Each landscape and population will require unique planning. We could use the dataset to answer a variety of questions.
We could ask some simple questions with sentiment analysis (who wrote the happiest plans? Are rural areas the most disparaging in their plans- or are urban areas?)
We could train a model on state recommendations for plans and see which plans fit (my hypothesis is that plans closest to Sacramento, the state capitol, fit the best). The take away would be that providing 'best practices' for planning is difficult because places and communities are so different in resources and objectives (eg. most rural areas do not want population growth, many urban areas measure success by population growth)..
We could also take a topical approach. How much housing is each city/county planning to build in housing-stressed California? How do plans talk about fire prevention management (eg. in the context of housing? transportation? forest management?). How are communities planning for GHG reduction (with a focus mainly on air quality? A focus mainly on transportation? what about energy systems?)

The second project relates to my dissertation and builds into the science of cities. This project would use spatial regression. I hypothesize that cities are like coral reef ecosystems where structural complexity begets more habitat niches and more species diversity, leading to greater total ecosystem resilience g. faster recovery from disease or disaster). I hypothesize that cities might be the same way- more structural complexity (longer urban perimeters in the case of my dataset- but we could use 3d city models as well) would lead to greater land-use diversity and more job diversity- which would help protect against economic downturn. None of the data is normally distributed- so the spatial regression is challenging.

Added by Jessica: So a way that we could evaluate the complexity and information is a method called ascendency. It is basically the same information index calculated Joshua Garland showed us and informs us about the diversity of the networks. Interestingly, years ago when I plotted this information against productivity/Biomass/energy, it got some Lorenz patterns. If we could find a way to model a perturbation in the system, that would make for some interesting predictive analysis.

===Interested Participants ===
Jessica Brumley
Dee Romo

==Dangerous idea about reviewing==

Dan and I came up with this really dangerous idea to break academia over lunch.
Reviewer # 2 is AI: We could use existing publications (eg. PlosOne) to train a model. Any paper that is uploaded for review would be reviewed by AI Reviewer #2. The review would take minutes, and would likely result in rejection or accept with modification. The AI could tell you where your paper fits in the broader scholarship on this topic. Does your paper bring together unique disciplines/ideas or test new hypotheses? How many papers have already been published on this topic- and how do your findings compare with regard to sample size, methodology, spatial and temporal context? In essence, have you found an anomaly- or is there more evidence to support a general theory. Where publicly available data exists, the AI could repeat analyses to verify findings. The AI could easily tell you where you have missed out on citing important works- or have been biased in citing the later work of a man over the foundational work of a woman or person of color (eg. everyone cites Robert Putnam for social capital and not Jane Jacobs).
Such a reviewer would provide sentiment analyses by discipline (eg. Economics still loves Garrett Hardin's Tragedy of the Commons over Elinor Ostrom's work on the Commons. But all other disciplines are ready to kill Hardin's work)
The second phase of this would use predictive modeling. reviewer #2 would write papers- predict new theories. This work would start with literature reviews (as any good PhD student would)- and then move into analyzing public datasets to answer new questions. We could check in after 10 years of human publication time had elapsed (eg. about 5-10 papers)- or 50 years... and see where science went. We could toggle the inputs (more hard sciences or more social sciences) to see how this changed the output and trajectory of science. The real world application could mean that we could do science with very little funding- and we would all be out of a job.

==Emergence of cooperative strategies by means of ''game warping'', using network science==

(From Shruti)

What if players can transform a noncooperative game to a cooperative positive-sum game? This is possible in certain digital economic systems (such as those on a blockchain) because all contracts are strictly enforceable. These type of "game-warping" transformations are interesting because given any economic model with pre-defined rules, the agents are able to develop unforeseeable cooperation strategies, form coalitions, and expand the scope of potential actions over time. Effectively, players are collectively able to overturn the system dynamics. The economy evolves because the economic rules effectively change w/ time (anyone play Baba Is You?). "Game warping" is defined as using transparent, triggerable, unstoppable punishments to move game-theoretic equilibria. We can extend this to multiple players and model the system using a graph/network, to explore what different cooperation strategies emerge. I trust that studying these systems at a macro-level, using simulations or networks will bring greatest degree of insight and set this research apart. David Wolpert's (SFI) work on "game mining" is also relevant. <ref>https://www.santafe.edu/news-center/news/wolpert-aaec-game-mining</ref>
[[File:Game warping .png]]
Citation: https://medium.com/@virgilgr/ethereum-is-game-changing-technology-literally-d67e01a01cf8

==Simulating evolution of bacterial cells’ decision to divide==

(From Kunaal)

How do cells decide when is the right time to divide? From a purely efficiency-based perspective, cells can obtain nutrients at a rate proportional to their surface area, but nutrient requirement for growth has a rate proportional to volume of the cell. Thus, there will be a cell size that is optimum for division.

The problem with this reasoning is, cells will tend to divide at the same size on average, irrespective of their initial size. But we know that in most bacterial species, cells that start out small (large) tend to divide at a size smaller (larger) than the average size at division.

This indicates there is a different reason behind cells’ decision to divide. It is an optimal path chosen by evolution, and I intend to simulate cells susceptible to mutations under different conditions to understand how this division mechanism arises through evolution and why it is optimal.

Join #cell-division-sim on Slack if you are interested.

==Modelling the spatial diffusion of human languages==

The diversification of human languages is a bit like speciation in biology: using comparative and cladistic methods, linguists can group languages into language families and further subgroup them into "phylogenetic" trees or networks. At the same time, we know where these languages are spoken today. The question, then: putting these two sources of data together, can we model the diffusion of languages over physical space and work backwards from the present day to infer the most likely homelands of the corresponding protolanguages? Can the predictions of such a model be made to align what we otherwise know about human migrations in the past? And most importantly (I think), from a complex systems perspective: ''what facets of the processes of linguistic diffusion and diversification are universal'' (i.e. not due to accidental historical events)? We could start with a simple random-walk model and take it from there. Slack channel is #language-diffusion.

'''First meeting: Friday 1pm, lecture room'''

===Data===

* [http://wals.info World Atlas of Language Structures]
* [https://github.com/hkauhanen/ritwals Same data for R-users]

===Papers to read===

* Let's add them here

===Interested participants===

* [http://henr.in Henri]
* Dee Romo
* Kenzie Givens
* Ritu
* Harun
* Let's add ourselves here

===Future plans===

This is (or can be, if we want) a somewhat ambitious project. I'd be happy to continue working towards a publication after CSSS.

==Butterflies in Water: Optimal Perturbations for Mixing in Treatment Processes==

This idea came from Liz Bradley’s last lecture and her showing us the 2D hurricanes in a box experiment and adding the “butterflies”.

Water treatment processes often need perturbations to mix the water, especially if you need to oxidize and precipitate out a contaminant (iron is a common example). Ultimately you want to do this in the most energy efficient way. The goal when building these systems is to expose the water to the surface area and mix in oxygen (from the atmosphere) for as long as possible. There are various ways to do this: make large surface area ponds; make a “Stream like” pond to make the water flow longer; add small dams for the water to go around; Some people have tried adding poles/sticks to the water; etc. It is yet to be understood which is the most successful method or which might be the optimal level of perturbations for mixing. Could agent based modeling help? Does the mixing and oxidation processes express chaotic behavior?

This is a project that I am seriously thinking about engineering a laboratory model to test as well.

===Interested Participants===
Jessica Brumley

==Computational Social Science in Decision-Making: an Opioid Epidemic Case-Study==

[[File:Css-opioid-simulator.png|thumb]]

''Project write-up from Slack:'' As a part of my ([[Kyle Furlong]]) work, I’ve been developing a tool/application that uses computational social science/agent-based modeling to help decision-makers make better data-driven decisions. I’m using the opioid epidemic as a “case study” for this tool. Using NetLogo and R (RShiny), the tool allows the user to explore the multiple social science theories that describe addiction and perform what-if analyses to determine which public policies/programs might be most effective in reducing negative outcomes (overdoses, deaths, etc).

I’ve got an early prototype UI/code (pictured below) running and have built in some basic theories of addiction that I’ve pulled from the literature, but I’d love to collaborate with anyone who is interested in the topic (addiction, drug use, public health), the methods (NetLogo/ABMs, social networks), and/or the approach. Open to informal coffee/not coffee drinking groups to crowd-source on a conceptual level or more technical groups working to improve my admittedly unrefined addiction models.

===Communication Channels===
Slack Channel: '''#compsocialsci-opioids'''

===Meeting Schedule & Notes===
TBA

===Interested Participants===
Shamelessly pulled from the whiteboard after the project brainstorming session on 6/13/2019:
* John Malloy
* Winnie Poel
* Robert Coulter
* Fabian Dablander
* Dakota Murray
* Xin Ran
* Dee Romo
* Pablo Franco
* David Gier

==Science Policy & Communication==

How is information transferred from scientists to policymakers to constituents? How much information is lost in translation from scientific papers to news articles and tweets? This group will explore the (potential) information loss along each transition, along with other policy-based issues that will emerge from the interaction between scientists and policymakers.

''Direct questions to John Malloy (Slack preferred)''

===Communication Channels===

Slack channel: '''science-policy'''

===Interested Participants (taken from Slack)===
*Andrew GB
*Chris Boyce-Jacino
*Dakota Murrary
*David Gier
*Jackie Brown
*Mackenzie Johnson
*Elissa Cohen
*Jessica Brumley
*Majorie
*Mikaela Akrenius
*Aabir
*Kyle Furlong
*Patrick Steinmann
*Ritu

==Modeling and predicting food insecurity using a resilience lens==
or
Can complex systems help feed the hungry?

Slack channel: '''food-security'''

Over 800 million people are hungry today, and vulnerable to drought, floods and crop-disease driven by climate change. I’m interested in modeling the incidence of hunger as a dynamic, stochastic system using a resilience lens. Would like to see if we can predict the incidence of hunger in response to shocks using a neural net. Got some data to play with and open to exploring different models and predictive algorithms. If we get some promising results, we can showcase them to policymakers at USAID and the World Bank who are very interested in this space.

===Participants===
* Erwin Knippenberg
* Travis Moore
* Ludvig Holmér
* Andrew Gillreath-Brown
* Alexander Bakus
* Pam Mantri
* Dan Krofcheck

==Modeling MinceCraft's Crafting Web==
Map the web of natural resource use in Minecraft and its hierarchy of dependencies, including the potentially circular dependencies (ie you need spider silk to make a bow, which you can then use to kill spiders). Can then infer which resources are most used, their trophic level, and what tools are required to produce them.

===Participants===
* Kate Wootton
* Alexander Bakus
* Chris Quarles
* Patrick Steinmann
* Erwin Knippenberg

== Looking for resilient patterns in Conway's Game of Life ==

Resilience to environmental challenges is one of the hallmarks of life. The goal of this project would be to search for patterns in Conway's Game of Life that can cope with external perturbations and self-organize back into their original forms.
Conway's Game of Life[1] is a cellular automaton that has raised a lot of attention due to the life-like forms that it generates. Cellular automata are computational models composed of a grid of cells that can be on either of two (or more) states. At every generation, each of these cells can change according to the state of their neighbours. Interestingly, Conway's Game of Life is Turing-complete, meaning that it can compute any computable function, including the Game of Life itself [2].
For this reason, one should expect to find a wide range of interesting patterns, including those that can detect external perturbations and repair themselves. By finding them, we would be providing a compelling example of one of life's key traits as an emergent behaviour in a simple computational environment.

[1] https://www.youtube.com/watch?v=ouipbDkwHWA

[2] https://imgur.com/T1h2VVS

=== Participants ===
* Alexander Schaefer
* Dan Krofcheck
* Kazuya Horibe
* Arta Cika
* Elissa Cohen
* Luther Seet
* Patrick Steinmann
* Germán Kruszewski

== Analyzing Collaboration Throughout CSSS History ==

How has the nature of collaboration at CSSS changed over time? Using project and participant data from the last 20 years of the program, we plan to explore how topics and group structures have changed over time. Have groups become more interdisciplinary? Is there a pattern to the types of projects that individuals from particular fields tend to work on?

=== Participants ===
* Dakota
* Emily
* Fabian
* Jackie
* Kyle

== Multi-scale inequalities and cities ==

Increases in inequality and urbanization are two of the challenges facing global sustainable development. However, inequalities in the urban context are conventionally understood by analyzing one city at a time, which limits a multi-scalar understanding. This project proposes to investigate whether there are general patterns in how inequalities manifest across spatial scales and regional contexts and examine the relationships between urban networks and inequalities.

=== Participants ===

* Bhartendu Pandey
* Christopher Quarles

== Lingua Technica: The impact of technology on language ==

Technology and language are related—words like "delete", "reboot", and "reset" only became prominent in our language with the introduction of computing. In other cases, language adopts metaphors of technology such as in phrases like "I'm Dying", "I'm losing you", and "They act like a robot". In this project we will analyze the uptake of such terms in English language text over the past decades. We hope to assess the extent and speed to which technical metaphors are adopted in a variety of mediums. We We will begin with words relating to computing and extent to other technologies such as cars, medicine, and more.

=== Participants ===

* Dakota Murray

==Artificial fossilization of animal interaction networks==

There has been a rapid increase in the number of papers applying network analysis to ancient communities, inferred from the fossil record. However, many of these studies don't account for the fact that the fossil record is incomplete. For example, most soft-bodied organisms don't preserve well. We hope to ground-truth investigations of past processes by analyzing how information loss affects the structure of modern interaction networks (co-occurrence, food webs, etc) and the inferences we make from them.

=== Participants ===

* Emily Coco
* Jack Shaw
* Andrew Gillreath-Brown
* Anshuman Swain
* Kate Wootton
* Dries Daems

Complex Systems Summer School 2019-Lecture Slides

2019-06-15T18:19:34Z

ChrQua:

{{Complex Systems Summer School 2019}}

==Dave Feldman==

[[Media:IntroToCSSS2019.pdf | Introduction to CSSS]]

[[Media:Fractal_crash_course_2019.pdf | Scaling Crash Course]]

==Liz Bradley==

[[www.cs.colorado.edu/~lizb/BradleyCSSS2019Slides.pdf | CSSS 2019 Slides (Nonlinear Dynamics)]]

==Joshua Garland==

[[Media:NLTSA.pdf | Nonlinear Time Series Analysis]]

==Srividya Iyer Biswas==

[https://www.youtube.com/watch?v=wV1mWE8Zyi0 Laws of Life, Time & Chance (SFI Community Lecture Video)]

==Stefani Crabtree==

[[Media:SFI_CSSS_Crabtree_Powerpoint.pdf | Modeling Past Human Societies]]

==Jennifer Dunne==

[[Media:19-CSSS_Humans_Ecological_Networks.pdf | The Roles, Function and Impacts of Humans in Complex Ecological Networks]]

==Geoffrey West==

[[media:CSSS_2018.pdf ‎| Searching for Simplicity and Unity in the Complexity of Life: Cells to Cities, Companies to Ecosystems, Milliseconds to Millenia]] (aka "Scaling")

==Cris Moore==

[https://wiki.santafe.edu/images/6/63/Csss18-algorithms.pdf Computational Complexity] (2018 version, 2019 coming soon)

[[Media:Csss19-fairness.pdf | Data, Algorithms, Fairness, and Justice]]

==Melanie Mitchell==

[http://web.cecs.pdx.edu/~mm/AscentsAndCollapsesOfAISlides.pdf Ascents and Collapses of AI]

Complex Systems Summer School 2019-Tutorials

2019-06-15T16:44:10Z

ChrQua: /* Distribution Fitting and Maximum Likelihood Estimation - Chris Quarles (2:00 PM, Thursday 6/20) */

Complex Systems Summer School 2019-Tutorials

2019-06-15T16:43:18Z

ChrQua: /* Distribution Fitting and Maximum Likelihood Estimation - Chris Quarles (2:00 PM, Thursday 6/20) */

Complex Systems Summer School 2019-Tutorials

2019-06-15T16:41:32Z

ChrQua:

Complex Systems Summer School 2019-After Hours

2019-06-10T21:38:54Z

ChrQua:

{{Complex Systems Summer School 2019}}

Please use this space to plan social events.

==Monday Shopping==

Supplies Run: 7:00pm to Walmart: Huge store with just about anything you'll need.

===Lorenzo's Shuttle (15 seats)===

 First Run (~7:00pm) 
1. Henri 
2. Mackenzie Johnson 
3. Paula Parpart 
4. Pam Mantri 
5. Chris Quarles 
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Second Run (~8:00pm) 
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===JP's super cool VW (~7:00pm)===

1.JP 
2.Arta 
3.Elissa 
4.shihui 
5.april