Santa Fe Institute Events Wiki - User contributions [en]

Complex Systems Summer School 2019-Tutorials

2019-07-01T18:09:45Z

GKruszewski: /* Implementing Neural Network models with PyTorch - Germán Kruszewski (Monday 6/31, 4pm - 5:30pm) */

{{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 Bayesian hypothesis testing and modeling - Fabian Dablander (Sunday, 6/30, 3:30PM - 5:30PM)==
Slides available from https://fdabl.github.io/assets/talks/Bayes-Tutorial-Santa-Fe.

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
* Andrea
* Henri
* Bhartendu

==Implementing Neural Network models with PyTorch - Germán Kruszewski (Monday 6/31, 4pm - 5:30pm)==
I will be showing how you can use thee PyTorch library to quickly write and train a Neural Network model.
I will be mostly following the examples available here: https://github.com/pytorch/tutorials and here https://github.com/init27Lab/DL-Toolkit

=== Interested Participants ===
*April
*Elissa
* Paula

==Regression and the Central Limit Theorem - John Schuler Cancelled==
Given the short list of interested participants, I will happily just talk about this to anyone who asks. I know everyone is busy with projects at this point.

=== Interested Participants ===
*A.Schaefer

== 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
* Yuka

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

=== [CANCELLED - for now, anyway - we may try again next week!] Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===
==== Interested Participants ====
* Kate
* Anshuman
* Bhargav
* Yuka
* Andrea

== 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

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

Complex Systems Summer School 2019-Tutorials

2019-06-29T22:35:10Z

GKruszewski: /* Implementing Neural Network models with PyTorch - Germán Kruszewski (Monday 6/31, 4pm - 5pm) */

{{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 Bayesian hypothesis testing and modeling - Fabian Dablander (Sunday, 6/30, 3:30PM - 5:30PM)==

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
* Andrea
* Henri

==Implementing Neural Network models with PyTorch - Germán Kruszewski (Monday 6/31, 4pm - 5:30pm)==
I will be showing how you can use thee PyTorch library to quickly write and train a Neural Network model.
I will be mostly following the examples available here: https://github.com/pytorch/tutorials

=== Interested Participants ===
*

==Regression and the Central Limit Theorem - John Schuler (Monday, 6/31, 7:30PM - 9:00PM)==
This is the follow up to my first lecture. Sorry I had to compress it into one session. Those who could not make
my first talk, I am happy to meet with you to get you up to speed. I will discuss ways the central limit theorem fails. There
will be power laws.

=== Interested Participants ===
*A.Schaefer

== 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
* Yuka

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

=== [CANCELLED - for now, anyway - we may try again next week!] Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===
==== Interested Participants ====
* Kate
* Anshuman
* Bhargav
* Yuka
* Andrea

== 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

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

Complex Systems Summer School 2019-Tutorials

2019-06-29T22:34:51Z

GKruszewski: /* Implementing Neural Network models with PyTorch - Germán Kruszewski (Monday 6/31, 4pm - 5pm) */

{{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 Bayesian hypothesis testing and modeling - Fabian Dablander (Sunday, 6/30, 3:30PM - 5:30PM)==

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
* Andrea
* Henri

==Implementing Neural Network models with PyTorch - Germán Kruszewski (Monday 6/31, 4pm - 5pm)==
I will be showing how you can use thee PyTorch library to quickly write and train a Neural Network model.
I will be mostly following the examples available here: https://github.com/pytorch/tutorials

=== Interested Participants ===
*

==Regression and the Central Limit Theorem - John Schuler (Monday, 6/31, 7:30PM - 9:00PM)==
This is the follow up to my first lecture. Sorry I had to compress it into one session. Those who could not make
my first talk, I am happy to meet with you to get you up to speed. I will discuss ways the central limit theorem fails. There
will be power laws.

=== Interested Participants ===
*A.Schaefer

== 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
* Yuka

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

=== [CANCELLED - for now, anyway - we may try again next week!] Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===
==== Interested Participants ====
* Kate
* Anshuman
* Bhargav
* Yuka
* Andrea

== 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

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

Complex Systems Summer School 2019-Tutorials

2019-06-29T22:33:57Z

GKruszewski:

{{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 Bayesian hypothesis testing and modeling - Fabian Dablander (Sunday, 6/30, 3:30PM - 5:30PM)==

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
* Andrea
* Henri

==Implementing Neural Network models with PyTorch - Germán Kruszewski (Monday 6/31, 4pm - 5pm)==
I will be showing how you can use thee PyTorch library to quickly write and train a Neural Network model.

=== Interested Participants ===
*

==Regression and the Central Limit Theorem - John Schuler (Monday, 6/31, 7:30PM - 9:00PM)==
This is the follow up to my first lecture. Sorry I had to compress it into one session. Those who could not make
my first talk, I am happy to meet with you to get you up to speed. I will discuss ways the central limit theorem fails. There
will be power laws.

=== Interested Participants ===
*A.Schaefer

== 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
* Yuka

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

=== [CANCELLED - for now, anyway - we may try again next week!] Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===
==== Interested Participants ====
* Kate
* Anshuman
* Bhargav
* Yuka
* Andrea

== 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

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

Complex Systems Summer School 2019-Project check-ins

2019-06-25T01:46:36Z

GKruszewski:

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:
* 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:
* 17.10 - 17.20: Network Control (Billy, Brennan, Alec, Harun)
* 17.20 - 17.30:
* 17.30 - 17.40: Self organizing city (Bhartendu, Chris, German, Jackie, Kazu, Ludwig, Luther)
* 17.40 - 17.50:
* 17.50 - 18.00:

'''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:

Larremore Workshop: How To Rank Things & Pairwise Comparisons 2019

2019-06-24T18:44:27Z

GKruszewski:

{{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
# Bhargav
# Emily
# Luther
# Ignacio
# Koissi
# Bhartendu
# Jeongki
# Toni
# Dakota
# Jackie
# Kyle
# Chris B-J
# Patrick
# Bakus
# Ethan
# Pablo Franco
# Billy
# Mark Chu
# Kenzie
# Pablo M. Flores
# robert
# Douglas Guilbeault
# April Kleppe
# Winnie
# Germán

2019 Dome Showing 7:00PM

2019-06-18T23:40:50Z

GKruszewski:

{{Complex Systems Summer School 2019}}

'''7:00 PM Dome Showing''' 
Please sign up to reserve a seat. 
Limit of 33 for each showing but there are three (3) showings: 
7:00 PM and 8:00 PM and 9:00 PM, so everyone will get a seat to the show.
 

#Pablo M. Flores
#Jessica Brumley
#Erwin Knippenberg
#Jordi Piñero
#Jack Shaw
#Arta Cika
#Elissa Cohen
#Anton Pichler
#Ignacio Garnham
#Germán Kruszewski
#.
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
Limit

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-17T20:27:53Z

GKruszewski:

{{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

Complex Systems Summer School 2019-After Hours

2019-06-14T01:30:27Z

GKruszewski:

{{Complex Systems Summer School 2019}}

Please use this space to plan social events.

==Second St. Rufina==

===June 13 ===

Off-campus hangout at Second St. Rufina! Transportation to and from, 9:00-midnight. They know we're coming so it's all copacetic!

Sign-up is to get a general idea of who wants to go, shuttle will loop around during the night.

1. Winnie 
2. Bakus 
3. Brennan 
4. Dries 
5. John 
6. Paula 
7. Elissa 
8. Germán 
9. 
10. 
...

 Completed Activities 

==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 
6. Bakus 
7. Kunaal Joshi 
8. Dakota 
9. Wenqian 
10. Ritu 
11. Germán 
12. Winnie 
13. Andrew G.-B. 
14. Pablo (Melbourne) 
15. Yuka 

Second Run (~8:00pm) 
1. 
2. Mikaela 
3. Jackie 
4. Dee 
5. Shruti 
6. Andrea 
7. Chiara 
8.Bhargav 
9. Arta 
10. 
11. 
12. 
13. 
14. 
15. 

===JP's super cool VW (~7:00pm)===

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

Complex Systems Summer School 2019-Icebreaker papers

2019-06-14T01:19:20Z

GKruszewski:

{{Complex Systems Summer School 2019}}

Titles that came up during the icebreaker at SFI:

* R you ready for this? An agent-based model of the propagation of programming languages and their impact on data analysis methods across disciplines
* Danger Danger! How social information processing saves communities
* Modelling the limits. Biological and social information processing
* From metaphors to models. How modes of thought influence scientific research
* The cognitive limits of decision-making affects social organisation
* Archaeogaming to generate urban design
* Population on the move. Modelling social networks of people and communities.
* A turbulent state of mind
* Designing for death
* Social networks for plumbers
* Turbulent dynamics of riots
* Basins of attraction for social groups
* Self-organizing power grids by Reinforcement Learning for maximal resilience.
* Modelling the dynamics of wireless communications through reservoir computing.
* Automating unbiased learning.

Complex Systems Summer School 2019-After Hours

2019-06-10T22:53:37Z

GKruszewski:

{{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 
6. Bakus 
7. Kunaal Joshi 
8. Dakota 
9. Wenqian 
10. Ritu 
11. Germán 
12. 
13. 
14. 
15. 

Second Run (~8:00pm) 
1. 
2. 
3. 
4. 
5. 
6. 
7. 
8. 
9. 
10. 
11. 
12. 
13. 
14. 
15. 

===JP's super cool VW (~7:00pm)===

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

Complex Systems Summer School 2019-After Hours

2019-06-10T22:53:12Z

GKruszewski:

{{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 
6. Bakus 
7. Kunaal Joshi 
8. Dakota 
9. Wenqian 
10.Ritu 
11.Germán 
12. 
13. 
14. 
15. 

Second Run (~8:00pm) 
1. 
2. 
3. 
4. 
5. 
6. 
7. 
8. 
9. 
10. 
11. 
12. 
13. 
14. 
15. 

===JP's super cool VW (~7:00pm)===

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