https://wiki.santafe.edu/api.php?action=feedcontributions&user=AdaRey&feedformat=atomSanta Fe Institute Events Wiki - User contributions [en]2021-07-29T08:58:20ZUser contributionsMediaWiki 1.32.0https://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Project_Presentations&diff=77708Complex Systems Summer School 2019-Project Presentations2019-06-27T21:26:09Z<p>AdaRey: </p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
'''Preference list'''<br />
<br />
<br />
'''Tuesday'''<br />
<br />
# Weighted Expectations (Mikaela, Ahyan, Elissa, Arta, Paula)<br />
# The individual lives of microbial cells (Jessica Lee, Kirtus, Ritu, Daniel, Adam, Pam)<br />
# Modelling the spatial diffusion of human languages (Henri, Ritu, Harun, Kenzie, Pablo Flores)<br />
# Modeling Minecraft's Crafting Web (Erwin, Chris Q., Alexander Bakus, Patrick, Kate)<br />
# Resilience in Conway's Game of Life (Alex, Arta, Elissa, German, Kazuya, Luther, Patrick, Wenqian)<br />
# Housing models from economic and demographic perspectives (John Shuler, Ian)<br />
<br />
'''Wednesday'''<br />
<br />
# Computational Synesthesia (Doug, Ethan, Aabir, Bhargav, Mark, Ruggiero)<br />
# Modeling food insecurity using a resilience lens (Erwin, Andrew, Alexander Bakus, Pam, Dan, Fabian)<br />
# Self-organizing cities ( Bhartendu, Chris, German, Jackie, Kazu, Ludwig, Luther) <br />
<br />
'''Later Wednesday'''<br />
<br />
# Cities and Scaling (Catherine, Jessica B, Ian, Gen)<br />
# Rules and Regulations (Adam, Bhargav, Brennan, Andrea, Elissa, Aabir)</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77525Complex Systems Summer School 2019-Tutorials2019-06-24T23:33:58Z<p>AdaRey: /* Collaborative listening and emergent computation through social dance */</p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
==Introduction to Agent-Based Modelling with NetLogo - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/25)==<br />
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.<br />
<br />
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).<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 25JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Dries<br />
* Luther <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==Introduction to Bayesian hypothesis testing and modeling - Fabian Dablander (Sunday, 6/30, 7:15PM - 9:15PM)==<br />
<br />
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.<br />
<br />
=== Prior to the tutorial ===<br />
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.<br />
<br />
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:<br />
* https://fdabl.github.io/r/Regularization.html (relevant to part (b) of the tutorial)<br />
* https://fdabl.github.io/r/Law-of-Practice.html (relevant to part (c) of the tutorial)<br />
<br />
=== What questions do you have? ===<br />
<br />
=== Interested Participants ===<br />
* Bakus<br />
* Arta<br />
* Elissa<br />
* Robert<br />
* Toni<br />
* Kate<br />
* Bhargav<br />
* Ernest<br />
<br />
== Collaborative listening and emergent computation through social dance == <br />
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!<br />
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!)<br />
<br />
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).<br />
<br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm, dorm 2nd floor - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
* Kate<br />
* Anshuman<br />
* Chris Quarles<br />
* Bhargav<br />
<br />
=== Tuesday, July 25, 7:00-8:30pm, dorm 2nd floor - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*Adam<br />
*Winnie<br />
*Kate<br />
* Anshuman<br />
* Bhargav<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm, dance studio - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
* Kate<br />
* Anshuman<br />
* Bhargav<br />
<br />
== Learning to Flow: Morning Yoga Edition == <br />
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.<br />
<br />
=== Thursday, June 13, 7:00-7:50am ===<br />
<br />
=== Tuesday, June 18 and Thursday, June 20, 7:00-7:50am ===<br />
<br />
=== Thursday, June 27, 7:00-7:50am ===<br />
<br />
=== Tuesday, July 2 and Thursday, July 4, 7:00-7:50am ===<br />
<br />
= Completed Tutorials =<br />
<br />
<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77445Complex Systems Summer School 2019-Tutorials2019-06-24T16:15:22Z<p>AdaRey: /* Collaborative computation and emergent phenomena in social dance */</p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
==Introduction to Bayesian hypothesis testing and modeling - Fabian Dablander (Sunday, 6/30, 7:15PM - 9:15PM)==<br />
<br />
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.<br />
<br />
=== Prior to the tutorial ===<br />
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.<br />
<br />
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:<br />
* https://fdabl.github.io/r/Regularization.html (relevant to part (b) of the tutorial)<br />
* https://fdabl.github.io/r/Law-of-Practice.html (relevant to part (c) of the tutorial)<br />
<br />
=== What questions do you have? ===<br />
<br />
=== Interested Participants ===<br />
<br />
== Collaborative listening and emergent computation through social dance == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice and play time. Come learn to walk with four feet and listen with your heart!<br />
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!)<br />
<br />
All 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).<br />
<br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*Adam<br />
*<br />
<br />
= Completed Tutorials =<br />
<br />
<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77444Complex Systems Summer School 2019-Tutorials2019-06-24T16:14:53Z<p>AdaRey: /* Collaborative computation and emergent phenomena in social dance */</p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
==Introduction to Bayesian hypothesis testing and modeling - Fabian Dablander (Sunday, 6/30, 7:15PM - 9:15PM)==<br />
<br />
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.<br />
<br />
=== Prior to the tutorial ===<br />
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.<br />
<br />
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:<br />
* https://fdabl.github.io/r/Regularization.html (relevant to part (b) of the tutorial)<br />
* https://fdabl.github.io/r/Law-of-Practice.html (relevant to part (c) of the tutorial)<br />
<br />
=== What questions do you have? ===<br />
<br />
=== Interested Participants ===<br />
<br />
== Collaborative computation and emergent phenomena in social dance == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice and play time. Come learn to walk with four feet and listen with your heart!<br />
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!)<br />
<br />
All 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).<br />
<br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*Adam<br />
*<br />
<br />
= Completed Tutorials =<br />
<br />
<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77440Complex Systems Summer School 2019-Tutorials2019-06-24T16:12:12Z<p>AdaRey: </p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
==Introduction to Bayesian hypothesis testing and modeling - Fabian Dablander (Sunday, 6/30, 7:15PM - 9:15PM)==<br />
<br />
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.<br />
<br />
=== Prior to the tutorial ===<br />
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.<br />
<br />
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:<br />
* https://fdabl.github.io/r/Regularization.html (relevant to part (b) of the tutorial)<br />
* https://fdabl.github.io/r/Law-of-Practice.html (relevant to part (c) of the tutorial)<br />
<br />
=== What questions do you have? ===<br />
<br />
=== Interested Participants ===<br />
<br />
== Collaborative computation and emergent phenomena in social dance == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice and play time. Come learn to walk with four feet and listen with your heart!<br />
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) Non-traditional roles and role exchange are welcome: Leaders and followers can be any gender! (In traditional Argentine tango, men are only allowed to dance with women after they've spent 2-3 years learning by following other men!)<br />
<br />
All 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).<br />
<br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*Adam<br />
*<br />
<br />
= Completed Tutorials =<br />
<br />
<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77429Complex Systems Summer School 2019-Tutorials2019-06-24T16:05:56Z<p>AdaRey: /* Collaborative computation and emergent phenomena in social dance: Salsa, swing, and Argentine tango! */</p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
==Introduction to Bayesian hypothesis testing and modeling - Fabian Dablander (Sunday, 6/30, 7:15PM - 9:15PM)==<br />
<br />
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.<br />
<br />
=== Prior to the tutorial ===<br />
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.<br />
<br />
=== What questions do you have? ===<br />
* How to choose a prior?<br />
<br />
=== Interested Participants ===<br />
* Fabian<br />
<br />
== Collaborative computation and emergent phenomena in social dance == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice and play time. Come learn to walk with four feet and listen with your heart!<br />
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) <br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
All 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).<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*Adam<br />
*<br />
<br />
= Completed Tutorials =<br />
<br />
<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77428Complex Systems Summer School 2019-Tutorials2019-06-24T16:05:19Z<p>AdaRey: /* Social dance series: Salsa, Swing, Argentine Tango! */</p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
==Introduction to Bayesian hypothesis testing and modeling - Fabian Dablander (Sunday, 6/30, 7:15PM - 9:15PM)==<br />
<br />
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.<br />
<br />
=== Prior to the tutorial ===<br />
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.<br />
<br />
=== What questions do you have? ===<br />
* How to choose a prior?<br />
<br />
=== Interested Participants ===<br />
* Fabian<br />
<br />
== Collaborative computation and emergent phenomena in social dance: Salsa, swing, and Argentine tango! == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice and play time. Come learn to walk with four feet and listen with your heart!<br />
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) <br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
All 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).<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*Adam<br />
*<br />
<br />
= Completed Tutorials =<br />
<br />
<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77425Complex Systems Summer School 2019-Tutorials2019-06-24T15:57:08Z<p>AdaRey: /* Social dance series */</p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
<br />
== Social dance series: Salsa, Swing, Argentine Tango! == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice. <br />
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) <br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
All 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).<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*Adam<br />
*<br />
<br />
= Completed Tutorials =<br />
<br />
<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77423Complex Systems Summer School 2019-Tutorials2019-06-24T15:49:56Z<p>AdaRey: </p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
<br />
== Social dance series == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice. <br />
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) <br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
All 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).<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*Adam<br />
*<br />
<br />
<br />
<br />
= Completed Tutorials =<br />
<br />
<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77422Complex Systems Summer School 2019-Tutorials2019-06-24T15:48:36Z<p>AdaRey: /* Social dance series */</p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
<br />
<br />
= Completed Tutorials =<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa<br />
<br />
<br />
== Social dance series == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice. <br />
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) <br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
All 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).<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*Adam<br />
*</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77421Complex Systems Summer School 2019-Tutorials2019-06-24T15:47:44Z<p>AdaRey: /* Suggested Date and Time */</p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
<br />
<br />
= Completed Tutorials =<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa<br />
<br />
<br />
== Social dance series == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice. <br />
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) <br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
All 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).<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77420Complex Systems Summer School 2019-Tutorials2019-06-24T15:47:30Z<p>AdaRey: /* Social dance series */</p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
<br />
<br />
= Completed Tutorials =<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa<br />
<br />
<br />
== Social dance series == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice. <br />
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) <br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
All 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).<br />
<br />
== Suggested Date and Time ==<br />
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.<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77419Complex Systems Summer School 2019-Tutorials2019-06-24T15:46:58Z<p>AdaRey: /* Social dance series */</p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
<br />
<br />
= Completed Tutorials =<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa<br />
<br />
<br />
== Social dance series == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice. <br />
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) <br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
====Location: ====<br />
All 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).<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77418Complex Systems Summer School 2019-Tutorials2019-06-24T15:46:05Z<p>AdaRey: /* Social dance series */</p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
<br />
<br />
= Completed Tutorials =<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa<br />
<br />
<br />
== Social dance series == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice. <br />
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) <br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
All 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).<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica) ===<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Argentine Tango (Adam + Jessica) ===<br />
==== Interested Participants ====<br />
*</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77417Complex Systems Summer School 2019-Tutorials2019-06-24T15:45:13Z<p>AdaRey: </p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
<br />
<br />
= Completed Tutorials =<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa<br />
<br />
<br />
== Social dance series == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice. <br />
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) <br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
All 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).<br />
<br />
=== Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica)<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica)<br />
==== Interested Participants ====<br />
*<br />
<br />
=== Thursday, July 27, 4:30-6:00pm - Tango [Argentine] (Adam + Jessica)<br />
==== Interested Participants ====<br />
*</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Tutorials&diff=77416Complex Systems Summer School 2019-Tutorials2019-06-24T15:40:44Z<p>AdaRey: </p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
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.<br />
<br />
= Upcoming Tutorials =<br />
<br />
==Networks, Network Science, and Python - Brennan Klein, Hunter Wapman, Al Kirkley (Sunday, 6/23, 7:30PM - 9:00PM)==<br />
<br />
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. <br />
<br />
=== Prior to the tutorial ===<br />
<br />
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: <br />
* Jupyter notebooks<br />
* networkx<br />
* numpy<br />
* scipy<br />
* matplotlib<br />
<br />
=== Wish-list of topics ===<br />
<br />
* Network visualization in networkx <br />
* Disease / spreading dynamics <br />
* Community detection and modularity in networks<br />
<br />
=== Interested Participants ===<br />
* Al(ec)<br />
* Hunter<br />
* Brennan<br />
* David<br />
* Laura<br />
* Patrick<br />
* Erwin <br />
* Bakus<br />
* April<br />
* Arta<br />
* Dries<br />
* Ian<br />
* Elissa<br />
* Andrea<br />
* Kate<br />
* Billy<br />
* Pam<br />
* Luther<br />
* Koissi<br />
* Kazu<br />
* Ludvig<br />
<br />
==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (7:00 PM 6/20) NEW TIME Distance Learning 2==<br />
<br />
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. <br />
<br />
=== Suggested Date and Time ===<br />
I am willing to move this if desired. I will find a classroom and update this space. <br />
<br />
=== Interested Participants ===<br />
Sign up is not required but it would be helpful to have some idea. <br />
* Patrick<br />
* Kate<br />
* Pam<br />
* Arta<br />
* Shihui<br />
* Yuka<br />
<br />
<br />
<br />
= Completed Tutorials =<br />
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==<br />
<br />
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! <br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].<br />
<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* Taylor series and linearization of nonlinear systems<br />
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))<br />
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension<br />
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from<br />
* Floquet multipliers and diverge of trajectories in maps<br />
* Where the quadratic term in the logistic map comes from<br />
<br />
== Nonlinear Dynamics Q&A II w/ D. Borrero (6/15) ==<br />
Took an in-depth look at dynamics and bifurcations in 1D flows<br />
<br />
==Nonlinear Dynamics Q&A III w/ D. Borrero (6/16) ==<br />
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:<br />
* 1D maps<br />
* Period doubling route to chaos<br />
<br />
== Natural Language Processing and Computational Linguistics in Python - [[Bhargav_Srinivasa_Desikan|Bhargav Srinivasa Desikan]] ==<br />
<br />
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!<br />
<br />
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].<br />
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.<br />
<br />
I'd be doing:<br />
<br />
* finding text data<br />
* pre-processing text data<br />
* identifying your problem<br />
* part-of-speech tagging, named entity recognition<br />
* topic modelling<br />
* text classification<br />
* text generation with neural nets<br />
* word embeddings<br />
<br />
=== Preparing for the tutorial ===<br />
<br />
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.<br />
<br />
<br />
=== Suggested Date and Time ===<br />
The tutorial will be from 4-6 pm on Monday (17th June), in the main lecture hall.<br />
I'll be happy to do smaller more detailed sessions and maybe a second tutorial if folks want it!<br />
<br />
=== Interested Participants ===<br />
(if anyone would like to conduct the tutorial with me or add more to it, very happy to collaborate!)<br><br />
<br />
# Bhargav (presenter)<br><br />
# Arta Cika<br />
# Xin Ran<br />
# Daniel Borrero<br />
# Jackie Brown<br />
# Pam Mantri<br />
# Dee Romo<br />
# Jeongki Lim<br />
# Ernest Aigner<br />
# Robert Coulter<br />
# Winnie Poel<br />
# Travis Moore<br />
# Pablo M. Flores<br />
# Catherine Brinkley<br />
# Andrew Gillreath-Brown<br />
# Kate<br />
# Bakus<br />
# Dries<br />
# Bhartendu<br />
# Kenzie Givens<br />
# Wenqian<br />
# Jordi<br />
# Elissa<br />
<br />
=== Note ===<br />
<br />
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!<br />
<br />
I'm happy to chat with folks for suggestions on if they'd want more/less than what has been described! <br />
<br />
([[Bhargav_Srinivasa_Desikan|this]] is what I look like if you want to find me)<br />
<br />
<br />
==Agent-Based Modelling of Complex Systems - [https://wiki.santafe.edu/index.php/Patrick_Steinmann Patrick Steinmann] (07:00 PM 6/18)==<br />
<br />
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.<br />
<br />
I would also be glad to consult on any projects involving ABM/systems simulation.<br />
<br />
=== Suggested Date and Time ===<br />
Tuesday 18JUN, 7:00 PM, lecture hall. <br />
<br />
=== Interested Participants ===<br />
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:<br />
* Patrick Steinmann (presenter)<br />
* Jeongki Lim<br />
* Dries Daems<br />
* Bhartendu Pandey<br />
* Travis<br />
* Pam<br />
* Arta<br />
* Ludvig<br />
* Ian<br />
* Wenqian<br />
* Jordi<br />
* Elissa<br />
* Bakus<br />
* Andrew<br />
* Luther<br />
<br />
If you can't make it, feel free to come chat with [https://wiki.santafe.edu/index.php/Patrick_Steinmann me].<br />
<br />
==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)==<br />
<br />
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:<br />
<br />
1. Overview/live-coding tutorial based on a Python data visualization workshop I've run in the past;<br />
<br />
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;<br />
<br />
3. Discussion of general principles: interesting topics include, but are not limited to:<br />
* What makes a plot beautiful?<br />
* Do scientific data visualization and art have the same aesthetic aims?<br />
* Are aesthetic biases reflected in scientific data visualization? (If so, how?)<br />
<br />
=== Suggested Date and Time ===<br />
8:00 PM on Wednesday, 6/19.<br />
<br />
=== Interested Participants ===<br />
* Ethan Nadler (presenter)<br />
* Daniel Borrero<br />
* Arta Cika<br />
* Kenzie Givens<br />
* Catherine Brinkley (but only if time changes... I have to pick up kids at 5.30pm)<br />
* Patrick<br />
* Erwin<br />
* Kate<br />
* Bakus<br />
* Bhartendu<br />
* Ernest<br />
* Travis<br />
* Pam<br />
* Henri<br />
* Ludvig<br />
* Ignacio<br />
* Mikaela<br />
* Winnie<br />
* Andrew<br />
* John Malloy<br />
* Ian<br />
* Chris B-J<br />
<br />
<br />
<br />
==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)==<br />
<br />
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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
=== Suggested Date and Time ===<br />
Thursday, June 20th at 2:00 PM.<br><br />
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. <br />
<br />
=== Interested Participants ===<br />
* Jessica Lee<br />
* Bhartendu<br />
* Henri<br />
* Wenqian<br />
* Arta<br />
* Elissa<br />
* Mikaela<br />
<br />
==Kirsten Moy, 7:00pm, Tuesday June 17==<br />
<br />
[[Kirsten Moy]] will be leading a tutorial/discussion about her work on complexity in community development. Come on along <br />
<br />
From her description:<br />
<br />
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.<br />
<br />
Following the presentation, there will be an opportunity for participants to present their specific questions to the researcher and the group.<br />
<br />
Please sign up so we have some idea of who will be around and can choose the appropriate room<br />
<br />
# JP<br />
# Ahyan Panjwani<br />
# Dee<br />
# Ignacio<br />
# Winnie <br />
# Elissa<br />
<br />
<br />
== Social dance series == <br />
We're offering a mini-series introducing not one, not two, but THREE different styles of social dance! Come learn the basics with us and follow it up with a little social dance practice. <br />
No partner, no experience, no dance shoes needed! (In fact, we'll all be dancing in socks.) <br />
Bonus: there will be excellent Salsa opportunities downtown later in the week and a tango practica on Friday: here's your chance to prepare!<br />
<br />
=== Suggested Date and Time ===<br />
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.<br />
<br />
* Tuesday, July 25, 4:30-6:00pm - Salsa [On1] (Luther + Jessica)<br />
* Wednesday, July 26, 4:30-6:00pm - Swing [Lindy Hop / East Coast Swing] (Henri + Jessica)<br />
* Thursday, July 27, 4:30-6:00pm - Tango [Argentine] (Adam + Jessica)<br />
<br />
All 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).<br />
<br />
=== Interested Participants ===<br />
*</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Projects_%26_Working_Groups&diff=77163Complex Systems Summer School 2019-Projects & Working Groups2019-06-19T20:12:04Z<p>AdaRey: </p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
Project and working group ideas go here.<br />
<br />
<br />
==Two ideas from Cat==<br />
<br />
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. <br />
<br />
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. <br />
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?)<br />
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)..<br />
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?)<br />
<br />
<br />
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. <br />
<br />
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.<br />
<br />
===Interested Participants ===<br />
Jessica Brumley<br />
Dee Romo<br />
<br />
==Dangerous idea about reviewing==<br />
<br />
Dan and I came up with this really dangerous idea to break academia over lunch. <br />
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). <br />
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)<br />
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.<br />
<br />
<br />
==Emergence of cooperative strategies by means of ''game warping'', using network science==<br />
<br />
(From Shruti)<br />
<br />
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><br />
[[File:Game warping .png]]<br />
<br> <br><br />
Citations: <br><br />
* https://poseidon01.ssrn.com/delivery.php?ID=325026118089093124102093068082080010034050058012070082112080112071106003085090090099038035127124020121002005065018075109121122105060069010052127002094098103004021064093039078084024001019025078027004029068023080086068066082022108116118112010021093014094&EXT=pdf <br><br />
* https://medium.com/@virgilgr/ethereum-is-game-changing-technology-literally-d67e01a01cf8<br />
<br />
===Research questions===<br />
* What gaming strategies emerge when NxN intelligent agents interact in a system that allows game warping? How does the system evolve over time?<br />
* Agents are able to make Bayesian decisions whose vectors adapt as more historical information becomes available<br />
* How are sequential games played on a NxN level? Consider contingency trail using Level-K solution concept<br />
* Simulate an artificial economy with adaptive agents<br />
<br />
===Interested participants===<br />
* Shruti <br />
* Aabir<br />
* Mikaela<br />
<br />
===Slack===<br />
Join #gamewarping channel.<br />
<br />
==Mathematical formalization of cryptoeconomics==<br />
<br />
(From Shruti)<br />
<br />
Create the Maxwell's equations of cryptoeconomics. Cryptoeconomics is a very new field, alike economics but comes with special properties that traditional economic systems cannot achieve. It is fertile ground that requires a foundation (pun), developing fundamentals, and formalization. Non-exhaustive list of potential approaches: network science, adaptive agent-based simulations, systems modeling (bond graphs), hyperparametric optimization, control systems. I've been thinking about developing this foundation for a few months now, and I will express all these ideas in my SFI talk on Jun 21 - open invite to join the collective nerd out! This topic was also hotly discussed as being the next steps in moving the space of cryptonetworks forward, in today's CollectiveCrypto workshop @ SFI by Geoffrey West, Jessica Flack, David Krakauer, David Wolpert et al. We arrived at the conclusion that this type of research is highly valuable. <br />
<br />
This is likely a mini-project, with an intent to publish a paper.<br />
<br />
===Interested participants===<br />
* Shruti <br />
* Mikaela<br />
<br />
==How might we quantify non-monetary value exchanges (like gift giving)?==<br />
<br />
(From Shruti)<br />
<br />
The current financial system doesn't incentivize corporations/individuals to take environmentally and socially positive actions (for the most part). There is no mechanism that accounts for non-monetary value exchanges in today's shareholder model of corporate governance. These exchanges range anywhere from simple gift-giving to a government agreeing to not dump their waste in Vietnam, India etc. Through this project, we want to explore generalizable means to account for currently unquantified (yet important) value exchanges. We're currently toying around with the idea of a human reputation market, we are aware it sounds dystopian and are open to ideas.<br />
<br />
===Interested participants===<br />
* Shruti<br />
* Pavel<br />
* Earnest<br />
<br />
===Slack===<br />
Join #moralmoniezzz<br />
<br />
==Simulating evolution of bacterial cells’ decision to divide==<br />
<br />
(From Kunaal)<br />
<br />
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.<br />
<br />
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.<br />
<br />
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.<br />
<br />
Join #cell-division-sim on Slack if you are interested.<br />
<br />
<br />
==Modelling the spatial diffusion of human languages==<br />
<br />
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.<br />
<br />
'''First meeting: Friday 1pm, lecture room'''<br />
<br />
===Data===<br />
<br />
* [http://wals.info World Atlas of Language Structures]<br />
* [https://github.com/hkauhanen/ritwals Same data for R-users]<br />
<br />
===Papers to read===<br />
<br />
* Let's add them here<br />
<br />
===Interested participants===<br />
<br />
* [http://henr.in Henri]<br />
* Dee Romo<br />
* Kenzie Givens<br />
* Ritu<br />
* Harun<br />
* Xin Ran<br />
* Let's add ourselves here<br />
<br />
===Future plans===<br />
<br />
This is (or can be, if we want) a somewhat ambitious project. I'd be happy to continue working towards a publication after CSSS.<br />
<br />
==Butterflies in Water: Optimal Perturbations for Mixing in Treatment Processes==<br />
<br />
This idea came from Liz Bradley’s last lecture and her showing us the 2D hurricanes in a box experiment and adding the “butterflies”. <br />
<br />
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?<br />
<br />
This is a project that I am seriously thinking about engineering a laboratory model to test as well.<br />
<br />
===Interested Participants===<br />
Jessica Brumley<br />
<br />
<br />
==Computational Social Science in Decision-Making: an Opioid Epidemic Case-Study==<br />
<br />
[[File:Css-opioid-simulator.png|thumb]]<br />
<br />
''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).<br />
<br />
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.<br />
<br />
===Communication Channels===<br />
Slack Channel: '''#compsocialsci-opioids'''<br />
<br />
===Meeting Schedule & Notes===<br />
TBA<br />
<br />
===Interested Participants===<br />
Shamelessly pulled from the whiteboard after the project brainstorming session on 6/13/2019:<br />
* John Malloy<br />
* Winnie Poel<br />
* Robert Coulter<br />
* Dakota Murray<br />
* Xin Ran<br />
* Dee Romo<br />
* Pablo Franco<br />
* David Gier<br />
* Pam Mantri<br />
<br />
==Science Policy & Communication==<br />
<br />
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.<br />
<br />
''Direct questions to John Malloy (Slack preferred)''<br />
<br />
===Communication Channels===<br />
<br />
Slack channel: '''science-policy'''<br />
<br />
===Interested Participants (taken from Slack)===<br />
*Andrew Gillreath-Brown<br />
*Chris Boyce-Jacino<br />
*Dakota Murrary<br />
*Jackie Brown<br />
*Mackenzie Johnson<br />
*Elissa Cohen<br />
*Jessica Brumley<br />
*Majorie<br />
*Mikaela Akrenius<br />
*Aabir<br />
*Kyle Furlong<br />
*Patrick Steinmann<br />
*Ritu<br />
<br />
==Modeling and predicting food insecurity using a resilience lens==<br />
or<br />
Can complex systems help feed the hungry?<br />
<br />
Slack channel: '''food-security'''<br />
<br />
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.<br />
<br />
===Participants===<br />
* Erwin Knippenberg<br />
* Travis Moore<br />
* Ludvig Holmér<br />
* Andrew Gillreath-Brown<br />
* Alexander Bakus<br />
* Pam Mantri<br />
* Dan Krofcheck<br />
* Fabian Dablander<br />
<br />
==Modeling Minecraft's Crafting Web==<br />
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.<br />
<br />
===Participants===<br />
* Kate Wootton<br />
* Alexander Bakus<br />
* Chris Quarles<br />
* Patrick Steinmann<br />
* Erwin Knippenberg<br />
<br />
<br />
== Looking for resilient patterns in Conway's Game of Life ==<br />
<br />
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.<br />
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].<br />
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.<br />
<br />
[1] https://www.youtube.com/watch?v=ouipbDkwHWA<br />
<br />
[2] https://imgur.com/T1h2VVS<br />
<br />
=== Participants ===<br />
* Alexander Schaefer<br />
* Dan Krofcheck<br />
* Kazuya Horibe<br />
* Arta Cika<br />
* Elissa Cohen<br />
* Luther Seet<br />
* Patrick Steinmann<br />
* Germán Kruszewski<br />
* Wenqian Yin<br />
<br />
== Analyzing Collaboration Throughout CSSS History ==<br />
<br />
How has the nature of collaboration at CSSS changed over time? Using project and participant data from the last 20 years of the program, we plan to explore how topics and group structures have changed over time. Have groups become more interdisciplinary? Is there a pattern to the types of projects that individuals from particular fields tend to work on?<br />
<br />
=== Participants ===<br />
* Dakota<br />
* Emily<br />
* Fabian<br />
* Jackie<br />
* Kyle<br />
<br />
== Multi-scale inequalities and cities ==<br />
<br />
Increases in inequality and urbanization are two of the challenges facing global sustainable development. However, inequalities in the urban context are conventionally understood by analyzing one city at a time, which limits a multi-scalar understanding. This project proposes to investigate whether there are general patterns in how inequalities manifest across spatial scales and regional contexts and examine the relationships between urban networks and inequalities.<br />
<br />
=== Participants ===<br />
<br />
* Bhartendu Pandey<br />
* Christopher Quarles<br />
* Alec Kirkley<br />
* Luther Seet<br />
<br />
== Lingua Technica: The impact of technology on language ==<br />
<br />
Technology and language are related—words like "delete", "reboot", and "reset" only became prominent in our language with the introduction of computing. In other cases, language adopts metaphors of technology such as in phrases like "I'm Dying", "I'm losing you", and "They act like a robot". In this project we will analyze the uptake of such terms in English language text over the past decades. We hope to assess the extent and speed to which technical metaphors are adopted in a variety of mediums. We We will begin with words relating to computing and extent to other technologies such as cars, medicine, and more. <br />
<br />
=== Participants ===<br />
<br />
* Dakota Murray<br />
* Chris Joyce-Jacino<br />
* Doug Reckamp<br />
* Harun<br />
* Ignacio<br />
* Jeongki<br />
* John Malloy<br />
* Pablo Flores<br />
<br />
==Artificial fossilization of animal interaction networks==<br />
<br />
There has been a rapid increase in the number of papers applying network analysis to ancient communities, inferred from the fossil record. However, many of these studies don't account for the fact that the fossil record is incomplete. For example, most soft-bodied organisms don't preserve well. We hope to ground-truth investigations of past processes by analyzing how information loss affects the structure of modern interaction networks (co-occurrence, food webs, etc) and the inferences we make from them.<br />
<br />
=== Participants ===<br />
<br />
* Emily Coco<br />
* Jack Shaw<br />
* Andrew Gillreath-Brown<br />
* Anshuman Swain<br />
* Kate Wootton<br />
* Dries Daems<br />
<br />
== The Time Traveler's Tree: What Did Sci-Fi Writers want? ==<br />
<br />
Throughout the 20th century, science fiction writers were busy imagining possible futures, using advanced scientific and technological concepts as a vehicle for their thoughts about the present and the future of the human race. When did we start talking about flying cars, when did we foreshadow the invention of waterbeds (Heinlein did it!) and where do the branches of the fictional tree loop into the branches of the real technological tree of the 20th and 21st century? We explore this by creating a dataset of fundamental scientific and technological ideas appearing in sci-fi classics of our time, primarily novels that have won the Hugo or Nebula award.<br />
<br />
=== Participants ===<br />
<br />
* Harun Siljak<br />
<br />
== Big Brother's Agents: Modelling Sci-Fi Communities ==<br />
<br />
How to start a rebellion in the total surveillance society of Orwell's 1984? Is it a case for an agent-based model, or maybe a network, or a cellular automaton? Could an emergent strategy bring down the Death Star? What made the Battle of Winterfell so wrong? This project investigates the great narratives of fiction and fantasy through complex systems modelling. <br />
<br />
=== Participants ===<br />
<br />
* Harun Siljak<br />
* Andrew Gillreath-Brown<br />
<br />
== CSSS Social Network Study ==<br />
<br />
Investigating the structural and dynamical properties of the social network formed by participants in the CSSS, incorporating node-level metadata. <br />
<br />
=== Participants ===<br />
<br />
* Alec Kirkley<br />
* Shihui Feng<br />
* Dr. Kenneth Hunter Wapman III, MD<br />
* Kate Wootton<br />
<br />
==Self organizing city==<br />
<br />
Exploring emergence and how a city can evolve and be shaped by social interactions. Planned cities and organically developed cities all have a network of public spaces. This looks at the use of agent based modelling and adaptive networks to study both the formation and resilience of public space networks in cities.<br />
<br />
How do do public spaces and active functions of the city influence the flow of people?<br />
<br />
Slack Channel: '''#selforganizing-city'''<br />
<br />
=== Participants ===<br />
<br />
* Luther Seet<br />
* German Kruszewski <br />
* Chris Boyce-Jacino<br />
* Kazuya Horibe<br />
* Jackie Brown<br />
* Bhartendu Pandey<br />
* Ludwig Holmer<br />
* Travis Moore<br />
<br />
==Too Much Information and Segregation==<br />
<br />
Every entity has a limited capacity to process information. So, when there is too much information, entities need to exclude information that does not benefit them. What happens when there are increases in the amount of information available, such as when technology allows a place-based society to transition to a more connected one? Individuals will have more options, and will also need to be more selective about the information they receive. Does this lead to increased segregation and/or specialization in a social system and/or biological system? We are approaching these questions using a network model, where nodes update their filters based on a utility function.<br />
<br />
=== Participants ===<br />
<br />
* Christopher Quarles<br />
* Wenqian Yin<br />
* Jordi Piñero<br />
* Xin Ran<br />
* Pablo Franco<br />
<br />
==Scrutinizing Early Warning Signals for Depression==<br />
Historically, depression has been understood within a 'common cause' framework in which the associations between symptoms such as worry, sadness, and lack of sleep is due to an underlying latent variable. This is an extremely successful approach in medicine, where symptoms usually are due to some underlying biological disease. In psychology, this has been less successful. An emerging 'network perspective' instead abandons the assumption of an underlying common cause and views depression as arising out of symptoms that directly influence each other, that is, as a complex system. There has been some work on 'early warning signals' which indicate 'tipping points', i.e., transitions to an alternative stable state. This project aims to extend and critically evaluate how these approaches have been applied to predict the onset and termination of depression.<br />
<br />
=== Participants ===<br />
<br />
* Fabian<br />
* Toni<br />
* Andrea<br />
* Arta<br />
<br />
== Network Control == <br />
<br />
Once one discovers how the structure effects the function of a network, a possible next consideration is controllability of the network. For control to be possible, one must have a reliable map of the interactions occurring between nodes, a formulation of the dynamical equations governing the behavior of each node, and the capability to effect change in the behavior and state of some collection of the nodes. We will examine the roles that network topology and dynamical equations play in the context of network controllability. Our particular interest is in how noise induced on the network topology or dynamics will influence controllability. A few examples of systems relevant to this line of inquiry could include regulatory processes in cells or economies and the operation of power grids.<br />
<br />
=== Participants ===<br />
<br />
* Billy Braasch<br />
* Alec Kirkley<br />
* Brennan Klein<br />
* Harun Šiljak<br />
<br />
== Cultural Fractals ==<br />
<br />
We are looking at different cladistics-like datasets on human culture, discussion on what ever kinds of complexity themes arise from them. Examples: fractals, chaos, punctuated equilibria. We are examining a social media dataset to better understand human community interaction, which evolved over sixteen years. In the social media dataset, participants mostly are not registered or have a network of friends. The way the forum evolved was that it began with a couple of discussion areas, but as the discussion evolves, these areas are divided (by human moderators who label the new areas) into subareas. A timestamp defines when new subarea appears.<br />
<br />
=== Slack ===<br />
'''#culture-fractal'''<br />
<br />
=== Participants ===<br />
* Marjorie<br />
* Dries<br />
* Andrew<br />
* Kenzie<br />
<br />
== A chaos metaphor for network topology ==<br />
<br />
We are studying the relationship between generative mechanisms and emergent topology of network ensembles. The aim is to probe for a new conceptual landscape for understanding complexity in network topologies.<br />
<br />
=== Participants ===<br />
* Andrea<br />
* Anton<br />
* Keith<br />
* Shruti (?)<br />
* Travis<br />
* Xin<br />
<br />
== Does network structure affect incorporation of novel data? ==<br />
<br />
What happens when one introduces a novel piece of information to a semi structured system? Is the information purged, incorporated, or persist neutrally. There is a neat study of how networks seemingly become more redundant over time, specifically, the data describes protein interaction network across ~1800 species. Idea is to re-create this data and play around with it, ideally to check how the structure of the network may influence whether or not a new protein can be incorporated.<br />
<br />
Why proteins? Mutations are the source of evolutionary novelty. Without evolutionary novelty, an organism will struggle to adapt to an alternating environment. Simultaneously, mutations are perceived to mostly be deleterious, causing negative fitness effects. Effectively, we are facing an evolutionary balance act between maintaining current functionality (selection against deleterious mutations), but also incorporating novelty (mutations, diversity) in order to stay opt in a constantly alternating environment. So, how can a systems incorporate novel information, without breaking the current functionality? You can think of proteins as a network of friends, and friends who introduce you to new friends. The practical nature of proteins, is that they have limited personality, thus they are easier to model. At least so we imagine. <br />
<br />
=== Participants ===<br />
* April <br />
* Brennan<br />
* Keith<br />
* Ludvig<br />
* Laura<br />
<br />
== Computational Synesthesia: A Multi-modal Approach to Automated Text Analysis == <br />
<br />
What data comprises the meaning of a word? Extant approaches to automated text analysis assume that the meaning of words can be inferred by examining co-occurrence relations (the bag-of-words approach) and formal semantic relationships among words (the linguistics approach). However, a large body of work illustrates that meaning is multimodal; i.e. words gain their meaning through their use in embodied multimodal contexts as labels that refer to “sensory images” – a property of meaning long known to artists, as in the use of visual imagery to inspire linguistically-mediated interpretations in the domain of painting, or in the use of words to evoke mental representations of sensory images in the domain of poetry. Here we propose a method for automated textual analysis that clusters words not based on text-to-text relations, but rather, in text-to-image relations, based on the structure of the sensory images associated with words. Specifically, we develop an automated approach for using large-scale search data from Google to retrieve a continuous set of images associated with search terms, and we provide information-theoretic measures for clustering search terms based on similarities in the color profile (such as ‘palette’) of . We explore how this clustering method can reveal novel dimensions of meaning previously unavailable through purely text-based methods of automated text analysis, for example, by finding unexpected similarities among search terms based on their color profile, or by providing novel measures for how abstract a word is based on the diversity of images associated with its use as a search term. <br />
<br />
=== Participants ===<br />
* Douglas<br />
* Ethan<br />
* Mark<br />
* Aabir<br />
* Bhargav<br />
* Ruggiero<br />
<br />
== Expected utility, information, and psychophysics ==<br />
<br />
Slack channel: '''#information-utility'''<br />
<br />
=== Project 1 ===<br />
<br />
==== Description ====<br />
<br />
Applying Valence-Weighted Distance (VWD), a novel probability weighting function developed by Mikaela, to a dynamic asset pricing model to explain emergent aggregate behavior.<br />
<br />
Probability functions are applied in the context of non-expected utility theories to explain deviations of individual decision makers from the predictions of expected utility theory. A plethora of research has found that people tend to overweight small probabilities and underweight larger probabilities -- however, the psychological bases of this tendency remain unclear.<br />
<br />
Grounded in psychophysics, VWD builds on information theoretic principles and aims to provide a psychological explanation for the shape and parameter fits of existing probability weighting functions (Tversky & Kahneman, Prelec, LinLog). In addition, VWD introduces novel (empirically testable) predictions, is sensitive to choice context, and has less free parameters than existing probability weighting functions.<br />
<br />
==== Participants ====<br />
* Mikaela Akrenius<br />
* Elissa Cohen<br />
<br />
=== Project 2 ===<br />
<br />
Analogously, it is well established within economics and psychology that the utility of money (or any other commodity) is concave -- i.e. that decision makers perceive the difference between e.g. $1 and $2 as greater than the difference between $101 and $102. The concavity of utility has been previously explained e.g. with outcome sampling and comparison in the choice environment (Stewart, 2009), the dissociation between large monetary outcomes and outcomes experienced in everyday life, and the notion of the human mind/brain as an efficient transduction system (e.g. Arkes, 1991; Summerfield & Tsetsos, 2015). As far as Mikaela knows, the latter notion has not yet been formally explored outside an experimental context, i.e. through simulating distributions of economic transactions in the actual environment in which decision makers tested in psychological or economic studies make their purchases.<br />
<br />
==== Provisional idea ====<br />
* Get distributions of economic transactions in different societies<br />
* Simulate a decision maker that encodes monetary value optimally given the prevalence of transactions in that society<br />
* Compare shapes of resulting utility functions to existing empirical data (observed utility functions)<br />
<br />
==== Participants ====<br />
* Mikaela Akrenius<br />
<br />
== Perceptions of aesthetic and informational content in expert and novice judgments ==<br />
<br />
Slack channel: '''#aesthetic-information'''<br />
<br />
=== Provisional idea ===<br />
* Method: Ratings of bifurcation and state space diagrams varied in their aesthetic and informational properties<br />
* Goal: Assess differences in expert and novice perception<br />
* Subject pool: SFI CSSS 2019 (pilot)<br />
* Hypothesis and applications of results: ---<br />
<br />
=== Participants ===<br />
* Mikaela<br />
<br />
==Code Name: Leaf Hunters==<br />
<br />
Previous work has identified quantitative measurements of leaves such as persistent homology, machine learning (e.g. CNNs), and fractal dimensionality as a method to predict the phylogenetic origin of leaves (a classification task of whether the leaf belongs to the species or not). Although interesting, we find this work isn’t useful for building theoretical models of plants within their environments. It is an interesting question whether environmental complexity (however that is measured!) might have relationships to leaf complexity (however that is measured!). This analysis could be useful for developing knew understandings into how and why complexity emerges in plant evolution.<br />
<br />
=== Participants ===<br />
<br />
* Levi Fussell<br />
* Anshuman Swain<br />
* Emily Coco<br />
<br />
== Toward an effective control of malaria in Ghana==<br />
Malaria is a vector-borne disease endemic to many countries in Africa as well as in Asia. This disease results from an interaction between Anopheles gambiae (mosquito), Plasmodium falciparum (parasite) and the human (host). Since Ross division of the host population in purely homogenous compartments, many variants of the model were used to get insights into the transmission and some strategy to control the disease. Thanks to such a simplistic representation of the epidemic, malaria control is remaining since 1950 a nightmare for policymakers since the disease is still persisting though numerous control measures were applied. This study aims at testing the dilution theory while accounting for the spatial repartition of the disease incidence using an adaptive ABM.<br />
<br />
=== Participants ===<br />
*Savi Koissi<br />
*Jeongki Lim<br />
*Anshuman Swain<br />
*Bhartendu Pandey<br />
<br />
<br />
== Chaos in the Brain ==<br />
<br />
Some of us are looking at chaos in the brain and how state-transitions in EEG waves may relate to learning/uncertainty-reduction/entropy. This refers to what Liz mentioned on day 1, where EEG waves of a healthy brain displayed a qualitative change in dynamics (bifurcation) when a parameter was changed, i.e., from *chaotic to more periodic*, under the influence of specific drugs. We also learned that the rabbit brain, when remembering a familiar smell as opposed to an unfamiliar smell, displayed a switch from chaotic to periodic attractor. We are asking the question of what other events may trigger a qualitative change in dynamics from chaotic to periodic, e.g., meditation, sleep or flow experiences, and what they have in common? The first step is to scan the literature.<br />
<br />
Secondly, could it be that the above bifurcations in the brain correspond to human learning and reducing uncertainty in the sense of information theory, i.e., where a state of higher unfamiliarity and novelty corresponds to more chaotic states and familiarity to more periodic states? This is mostly speculation at this point<br />
<br />
General relevant topics<br />
* Chaos in EEG ,<br />
* Complex system modelling in cognitive science<br />
* Chaos related to learning/search/reinforcement learning<br />
* Information theory and entropy in cognition<br />
<br />
=== Participants ===<br />
* Pablo Franco<br />
* David Gier<br />
<br />
== Chaotic Image Encoding ==<br />
<br />
I was (like most folks) really stoked on the ability of chaotic mapping (e.g., Darbby's example) to introduce some seemingly random patterns to some series that preserves a bit of the 'flavor' of the original. I like simple images and pixel art / animations -- in that spirit, I want to employ similar approaches to investigate how we can use chaotic attractors to manipulate images. Specifically, many processes that we see in the natural world are beautiful yet complicated to recreate in faithful ways - as an example, reflections of light on the surface of a fluid, or the dynamics of flames in a fire. <br />
<br />
<br />
=== Participants ===<br />
* Dan Krofcheck<br />
* Ludvig Holmér<br />
<br />
<br />
== Rules and Regulations ==<br />
<br />
We are working to understand the dynamics of U.S. federal rulemaking over a period of 20 years using topic modeling and dynamical networks.<br />
<br />
=== Participants ===<br />
Adam<br />
Bhargav<br />
Andrea<br />
Elissa<br />
Hunter<br />
Brennan<br />
Aabir</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Complex_Systems_Summer_School_2019-Projects_%26_Working_Groups&diff=77161Complex Systems Summer School 2019-Projects & Working Groups2019-06-19T20:11:37Z<p>AdaRey: </p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
Project and working group ideas go here.<br />
<br />
<br />
==Two ideas from Cat==<br />
<br />
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. <br />
<br />
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. <br />
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?)<br />
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)..<br />
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?)<br />
<br />
<br />
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. <br />
<br />
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.<br />
<br />
===Interested Participants ===<br />
Jessica Brumley<br />
Dee Romo<br />
<br />
==Dangerous idea about reviewing==<br />
<br />
Dan and I came up with this really dangerous idea to break academia over lunch. <br />
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). <br />
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)<br />
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.<br />
<br />
<br />
==Emergence of cooperative strategies by means of ''game warping'', using network science==<br />
<br />
(From Shruti)<br />
<br />
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><br />
[[File:Game warping .png]]<br />
<br> <br><br />
Citations: <br><br />
* https://poseidon01.ssrn.com/delivery.php?ID=325026118089093124102093068082080010034050058012070082112080112071106003085090090099038035127124020121002005065018075109121122105060069010052127002094098103004021064093039078084024001019025078027004029068023080086068066082022108116118112010021093014094&EXT=pdf <br><br />
* https://medium.com/@virgilgr/ethereum-is-game-changing-technology-literally-d67e01a01cf8<br />
<br />
===Research questions===<br />
* What gaming strategies emerge when NxN intelligent agents interact in a system that allows game warping? How does the system evolve over time?<br />
* Agents are able to make Bayesian decisions whose vectors adapt as more historical information becomes available<br />
* How are sequential games played on a NxN level? Consider contingency trail using Level-K solution concept<br />
* Simulate an artificial economy with adaptive agents<br />
<br />
===Interested participants===<br />
* Shruti <br />
* Aabir<br />
* Mikaela<br />
<br />
===Slack===<br />
Join #gamewarping channel.<br />
<br />
==Mathematical formalization of cryptoeconomics==<br />
<br />
(From Shruti)<br />
<br />
Create the Maxwell's equations of cryptoeconomics. Cryptoeconomics is a very new field, alike economics but comes with special properties that traditional economic systems cannot achieve. It is fertile ground that requires a foundation (pun), developing fundamentals, and formalization. Non-exhaustive list of potential approaches: network science, adaptive agent-based simulations, systems modeling (bond graphs), hyperparametric optimization, control systems. I've been thinking about developing this foundation for a few months now, and I will express all these ideas in my SFI talk on Jun 21 - open invite to join the collective nerd out! This topic was also hotly discussed as being the next steps in moving the space of cryptonetworks forward, in today's CollectiveCrypto workshop @ SFI by Geoffrey West, Jessica Flack, David Krakauer, David Wolpert et al. We arrived at the conclusion that this type of research is highly valuable. <br />
<br />
This is likely a mini-project, with an intent to publish a paper.<br />
<br />
===Interested participants===<br />
* Shruti <br />
* Mikaela<br />
<br />
==How might we quantify non-monetary value exchanges (like gift giving)?==<br />
<br />
(From Shruti)<br />
<br />
The current financial system doesn't incentivize corporations/individuals to take environmentally and socially positive actions (for the most part). There is no mechanism that accounts for non-monetary value exchanges in today's shareholder model of corporate governance. These exchanges range anywhere from simple gift-giving to a government agreeing to not dump their waste in Vietnam, India etc. Through this project, we want to explore generalizable means to account for currently unquantified (yet important) value exchanges. We're currently toying around with the idea of a human reputation market, we are aware it sounds dystopian and are open to ideas.<br />
<br />
===Interested participants===<br />
* Shruti<br />
* Pavel<br />
* Earnest<br />
<br />
===Slack===<br />
Join #moralmoniezzz<br />
<br />
==Simulating evolution of bacterial cells’ decision to divide==<br />
<br />
(From Kunaal)<br />
<br />
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.<br />
<br />
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.<br />
<br />
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.<br />
<br />
Join #cell-division-sim on Slack if you are interested.<br />
<br />
<br />
==Modelling the spatial diffusion of human languages==<br />
<br />
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.<br />
<br />
'''First meeting: Friday 1pm, lecture room'''<br />
<br />
===Data===<br />
<br />
* [http://wals.info World Atlas of Language Structures]<br />
* [https://github.com/hkauhanen/ritwals Same data for R-users]<br />
<br />
===Papers to read===<br />
<br />
* Let's add them here<br />
<br />
===Interested participants===<br />
<br />
* [http://henr.in Henri]<br />
* Dee Romo<br />
* Kenzie Givens<br />
* Ritu<br />
* Harun<br />
* Xin Ran<br />
* Let's add ourselves here<br />
<br />
===Future plans===<br />
<br />
This is (or can be, if we want) a somewhat ambitious project. I'd be happy to continue working towards a publication after CSSS.<br />
<br />
==Butterflies in Water: Optimal Perturbations for Mixing in Treatment Processes==<br />
<br />
This idea came from Liz Bradley’s last lecture and her showing us the 2D hurricanes in a box experiment and adding the “butterflies”. <br />
<br />
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?<br />
<br />
This is a project that I am seriously thinking about engineering a laboratory model to test as well.<br />
<br />
===Interested Participants===<br />
Jessica Brumley<br />
<br />
<br />
==Computational Social Science in Decision-Making: an Opioid Epidemic Case-Study==<br />
<br />
[[File:Css-opioid-simulator.png|thumb]]<br />
<br />
''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).<br />
<br />
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.<br />
<br />
===Communication Channels===<br />
Slack Channel: '''#compsocialsci-opioids'''<br />
<br />
===Meeting Schedule & Notes===<br />
TBA<br />
<br />
===Interested Participants===<br />
Shamelessly pulled from the whiteboard after the project brainstorming session on 6/13/2019:<br />
* John Malloy<br />
* Winnie Poel<br />
* Robert Coulter<br />
* Dakota Murray<br />
* Xin Ran<br />
* Dee Romo<br />
* Pablo Franco<br />
* David Gier<br />
* Pam Mantri<br />
<br />
==Science Policy & Communication==<br />
<br />
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.<br />
<br />
''Direct questions to John Malloy (Slack preferred)''<br />
<br />
===Communication Channels===<br />
<br />
Slack channel: '''science-policy'''<br />
<br />
===Interested Participants (taken from Slack)===<br />
*Andrew Gillreath-Brown<br />
*Chris Boyce-Jacino<br />
*Dakota Murrary<br />
*Jackie Brown<br />
*Mackenzie Johnson<br />
*Elissa Cohen<br />
*Jessica Brumley<br />
*Majorie<br />
*Mikaela Akrenius<br />
*Aabir<br />
*Kyle Furlong<br />
*Patrick Steinmann<br />
*Ritu<br />
<br />
==Modeling and predicting food insecurity using a resilience lens==<br />
or<br />
Can complex systems help feed the hungry?<br />
<br />
Slack channel: '''food-security'''<br />
<br />
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.<br />
<br />
===Participants===<br />
* Erwin Knippenberg<br />
* Travis Moore<br />
* Ludvig Holmér<br />
* Andrew Gillreath-Brown<br />
* Alexander Bakus<br />
* Pam Mantri<br />
* Dan Krofcheck<br />
* Fabian Dablander<br />
<br />
==Modeling Minecraft's Crafting Web==<br />
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.<br />
<br />
===Participants===<br />
* Kate Wootton<br />
* Alexander Bakus<br />
* Chris Quarles<br />
* Patrick Steinmann<br />
* Erwin Knippenberg<br />
<br />
<br />
== Looking for resilient patterns in Conway's Game of Life ==<br />
<br />
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.<br />
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].<br />
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.<br />
<br />
[1] https://www.youtube.com/watch?v=ouipbDkwHWA<br />
<br />
[2] https://imgur.com/T1h2VVS<br />
<br />
=== Participants ===<br />
* Alexander Schaefer<br />
* Dan Krofcheck<br />
* Kazuya Horibe<br />
* Arta Cika<br />
* Elissa Cohen<br />
* Luther Seet<br />
* Patrick Steinmann<br />
* Germán Kruszewski<br />
* Wenqian Yin<br />
<br />
== Analyzing Collaboration Throughout CSSS History ==<br />
<br />
How has the nature of collaboration at CSSS changed over time? Using project and participant data from the last 20 years of the program, we plan to explore how topics and group structures have changed over time. Have groups become more interdisciplinary? Is there a pattern to the types of projects that individuals from particular fields tend to work on?<br />
<br />
=== Participants ===<br />
* Dakota<br />
* Emily<br />
* Fabian<br />
* Jackie<br />
* Kyle<br />
<br />
== Multi-scale inequalities and cities ==<br />
<br />
Increases in inequality and urbanization are two of the challenges facing global sustainable development. However, inequalities in the urban context are conventionally understood by analyzing one city at a time, which limits a multi-scalar understanding. This project proposes to investigate whether there are general patterns in how inequalities manifest across spatial scales and regional contexts and examine the relationships between urban networks and inequalities.<br />
<br />
=== Participants ===<br />
<br />
* Bhartendu Pandey<br />
* Christopher Quarles<br />
* Alec Kirkley<br />
* Luther Seet<br />
<br />
== Lingua Technica: The impact of technology on language ==<br />
<br />
Technology and language are related—words like "delete", "reboot", and "reset" only became prominent in our language with the introduction of computing. In other cases, language adopts metaphors of technology such as in phrases like "I'm Dying", "I'm losing you", and "They act like a robot". In this project we will analyze the uptake of such terms in English language text over the past decades. We hope to assess the extent and speed to which technical metaphors are adopted in a variety of mediums. We We will begin with words relating to computing and extent to other technologies such as cars, medicine, and more. <br />
<br />
=== Participants ===<br />
<br />
* Dakota Murray<br />
* Chris Joyce-Jacino<br />
* Doug Reckamp<br />
* Harun<br />
* Ignacio<br />
* Jeongki<br />
* John Malloy<br />
* Pablo Flores<br />
<br />
==Artificial fossilization of animal interaction networks==<br />
<br />
There has been a rapid increase in the number of papers applying network analysis to ancient communities, inferred from the fossil record. However, many of these studies don't account for the fact that the fossil record is incomplete. For example, most soft-bodied organisms don't preserve well. We hope to ground-truth investigations of past processes by analyzing how information loss affects the structure of modern interaction networks (co-occurrence, food webs, etc) and the inferences we make from them.<br />
<br />
=== Participants ===<br />
<br />
* Emily Coco<br />
* Jack Shaw<br />
* Andrew Gillreath-Brown<br />
* Anshuman Swain<br />
* Kate Wootton<br />
* Dries Daems<br />
<br />
== The Time Traveler's Tree: What Did Sci-Fi Writers want? ==<br />
<br />
Throughout the 20th century, science fiction writers were busy imagining possible futures, using advanced scientific and technological concepts as a vehicle for their thoughts about the present and the future of the human race. When did we start talking about flying cars, when did we foreshadow the invention of waterbeds (Heinlein did it!) and where do the branches of the fictional tree loop into the branches of the real technological tree of the 20th and 21st century? We explore this by creating a dataset of fundamental scientific and technological ideas appearing in sci-fi classics of our time, primarily novels that have won the Hugo or Nebula award.<br />
<br />
=== Participants ===<br />
<br />
* Harun Siljak<br />
<br />
== Big Brother's Agents: Modelling Sci-Fi Communities ==<br />
<br />
How to start a rebellion in the total surveillance society of Orwell's 1984? Is it a case for an agent-based model, or maybe a network, or a cellular automaton? Could an emergent strategy bring down the Death Star? What made the Battle of Winterfell so wrong? This project investigates the great narratives of fiction and fantasy through complex systems modelling. <br />
<br />
=== Participants ===<br />
<br />
* Harun Siljak<br />
* Andrew Gillreath-Brown<br />
<br />
== CSSS Social Network Study ==<br />
<br />
Investigating the structural and dynamical properties of the social network formed by participants in the CSSS, incorporating node-level metadata. <br />
<br />
=== Participants ===<br />
<br />
* Alec Kirkley<br />
* Shihui Feng<br />
* Dr. Kenneth Hunter Wapman III, MD<br />
* Kate Wootton<br />
<br />
==Self organizing city==<br />
<br />
Exploring emergence and how a city can evolve and be shaped by social interactions. Planned cities and organically developed cities all have a network of public spaces. This looks at the use of agent based modelling and adaptive networks to study both the formation and resilience of public space networks in cities.<br />
<br />
How do do public spaces and active functions of the city influence the flow of people?<br />
<br />
Slack Channel: '''#selforganizing-city'''<br />
<br />
=== Participants ===<br />
<br />
* Luther Seet<br />
* German Kruszewski <br />
* Chris Boyce-Jacino<br />
* Kazuya Horibe<br />
* Jackie Brown<br />
* Bhartendu Pandey<br />
* Ludwig Holmer<br />
* Travis Moore<br />
<br />
==Too Much Information and Segregation==<br />
<br />
Every entity has a limited capacity to process information. So, when there is too much information, entities need to exclude information that does not benefit them. What happens when there are increases in the amount of information available, such as when technology allows a place-based society to transition to a more connected one? Individuals will have more options, and will also need to be more selective about the information they receive. Does this lead to increased segregation and/or specialization in a social system and/or biological system? We are approaching these questions using a network model, where nodes update their filters based on a utility function.<br />
<br />
=== Participants ===<br />
<br />
* Christopher Quarles<br />
* Wenqian Yin<br />
* Jordi Piñero<br />
* Xin Ran<br />
* Pablo Franco<br />
<br />
==Scrutinizing Early Warning Signals for Depression==<br />
Historically, depression has been understood within a 'common cause' framework in which the associations between symptoms such as worry, sadness, and lack of sleep is due to an underlying latent variable. This is an extremely successful approach in medicine, where symptoms usually are due to some underlying biological disease. In psychology, this has been less successful. An emerging 'network perspective' instead abandons the assumption of an underlying common cause and views depression as arising out of symptoms that directly influence each other, that is, as a complex system. There has been some work on 'early warning signals' which indicate 'tipping points', i.e., transitions to an alternative stable state. This project aims to extend and critically evaluate how these approaches have been applied to predict the onset and termination of depression.<br />
<br />
=== Participants ===<br />
<br />
* Fabian<br />
* Toni<br />
* Andrea<br />
* Arta<br />
<br />
== Network Control == <br />
<br />
Once one discovers how the structure effects the function of a network, a possible next consideration is controllability of the network. For control to be possible, one must have a reliable map of the interactions occurring between nodes, a formulation of the dynamical equations governing the behavior of each node, and the capability to effect change in the behavior and state of some collection of the nodes. We will examine the roles that network topology and dynamical equations play in the context of network controllability. Our particular interest is in how noise induced on the network topology or dynamics will influence controllability. A few examples of systems relevant to this line of inquiry could include regulatory processes in cells or economies and the operation of power grids.<br />
<br />
=== Participants ===<br />
<br />
* Billy Braasch<br />
* Alec Kirkley<br />
* Brennan Klein<br />
* Harun Šiljak<br />
<br />
== Cultural Fractals ==<br />
<br />
We are looking at different cladistics-like datasets on human culture, discussion on what ever kinds of complexity themes arise from them. Examples: fractals, chaos, punctuated equilibria. We are examining a social media dataset to better understand human community interaction, which evolved over sixteen years. In the social media dataset, participants mostly are not registered or have a network of friends. The way the forum evolved was that it began with a couple of discussion areas, but as the discussion evolves, these areas are divided (by human moderators who label the new areas) into subareas. A timestamp defines when new subarea appears.<br />
<br />
=== Slack ===<br />
'''#culture-fractal'''<br />
<br />
=== Participants ===<br />
* Marjorie<br />
* Dries<br />
* Andrew<br />
* Kenzie<br />
<br />
== A chaos metaphor for network topology ==<br />
<br />
We are studying the relationship between generative mechanisms and emergent topology of network ensembles. The aim is to probe for a new conceptual landscape for understanding complexity in network topologies.<br />
<br />
=== Participants ===<br />
* Andrea<br />
* Anton<br />
* Keith<br />
* Shruti (?)<br />
* Travis<br />
* Xin<br />
<br />
== Does network structure affect incorporation of novel data? ==<br />
<br />
What happens when one introduces a novel piece of information to a semi structured system? Is the information purged, incorporated, or persist neutrally. There is a neat study of how networks seemingly become more redundant over time, specifically, the data describes protein interaction network across ~1800 species. Idea is to re-create this data and play around with it, ideally to check how the structure of the network may influence whether or not a new protein can be incorporated.<br />
<br />
Why proteins? Mutations are the source of evolutionary novelty. Without evolutionary novelty, an organism will struggle to adapt to an alternating environment. Simultaneously, mutations are perceived to mostly be deleterious, causing negative fitness effects. Effectively, we are facing an evolutionary balance act between maintaining current functionality (selection against deleterious mutations), but also incorporating novelty (mutations, diversity) in order to stay opt in a constantly alternating environment. So, how can a systems incorporate novel information, without breaking the current functionality? You can think of proteins as a network of friends, and friends who introduce you to new friends. The practical nature of proteins, is that they have limited personality, thus they are easier to model. At least so we imagine. <br />
<br />
=== Participants ===<br />
* April <br />
* Brennan<br />
* Keith<br />
* Ludvig<br />
* Laura<br />
<br />
== Computational Synesthesia: A Multi-modal Approach to Automated Text Analysis == <br />
<br />
What data comprises the meaning of a word? Extant approaches to automated text analysis assume that the meaning of words can be inferred by examining co-occurrence relations (the bag-of-words approach) and formal semantic relationships among words (the linguistics approach). However, a large body of work illustrates that meaning is multimodal; i.e. words gain their meaning through their use in embodied multimodal contexts as labels that refer to “sensory images” – a property of meaning long known to artists, as in the use of visual imagery to inspire linguistically-mediated interpretations in the domain of painting, or in the use of words to evoke mental representations of sensory images in the domain of poetry. Here we propose a method for automated textual analysis that clusters words not based on text-to-text relations, but rather, in text-to-image relations, based on the structure of the sensory images associated with words. Specifically, we develop an automated approach for using large-scale search data from Google to retrieve a continuous set of images associated with search terms, and we provide information-theoretic measures for clustering search terms based on similarities in the color profile (such as ‘palette’) of . We explore how this clustering method can reveal novel dimensions of meaning previously unavailable through purely text-based methods of automated text analysis, for example, by finding unexpected similarities among search terms based on their color profile, or by providing novel measures for how abstract a word is based on the diversity of images associated with its use as a search term. <br />
<br />
=== Participants ===<br />
* Douglas<br />
* Ethan<br />
* Mark<br />
* Aabir<br />
* Bhargav<br />
* Ruggiero<br />
<br />
== Expected utility, information, and psychophysics ==<br />
<br />
Slack channel: '''#information-utility'''<br />
<br />
=== Project 1 ===<br />
<br />
==== Description ====<br />
<br />
Applying Valence-Weighted Distance (VWD), a novel probability weighting function developed by Mikaela, to a dynamic asset pricing model to explain emergent aggregate behavior.<br />
<br />
Probability functions are applied in the context of non-expected utility theories to explain deviations of individual decision makers from the predictions of expected utility theory. A plethora of research has found that people tend to overweight small probabilities and underweight larger probabilities -- however, the psychological bases of this tendency remain unclear.<br />
<br />
Grounded in psychophysics, VWD builds on information theoretic principles and aims to provide a psychological explanation for the shape and parameter fits of existing probability weighting functions (Tversky & Kahneman, Prelec, LinLog). In addition, VWD introduces novel (empirically testable) predictions, is sensitive to choice context, and has less free parameters than existing probability weighting functions.<br />
<br />
==== Participants ====<br />
* Mikaela Akrenius<br />
* Elissa Cohen<br />
<br />
=== Project 2 ===<br />
<br />
Analogously, it is well established within economics and psychology that the utility of money (or any other commodity) is concave -- i.e. that decision makers perceive the difference between e.g. $1 and $2 as greater than the difference between $101 and $102. The concavity of utility has been previously explained e.g. with outcome sampling and comparison in the choice environment (Stewart, 2009), the dissociation between large monetary outcomes and outcomes experienced in everyday life, and the notion of the human mind/brain as an efficient transduction system (e.g. Arkes, 1991; Summerfield & Tsetsos, 2015). As far as Mikaela knows, the latter notion has not yet been formally explored outside an experimental context, i.e. through simulating distributions of economic transactions in the actual environment in which decision makers tested in psychological or economic studies make their purchases.<br />
<br />
==== Provisional idea ====<br />
* Get distributions of economic transactions in different societies<br />
* Simulate a decision maker that encodes monetary value optimally given the prevalence of transactions in that society<br />
* Compare shapes of resulting utility functions to existing empirical data (observed utility functions)<br />
<br />
==== Participants ====<br />
* Mikaela Akrenius<br />
<br />
== Perceptions of aesthetic and informational content in expert and novice judgments ==<br />
<br />
Slack channel: '''#aesthetic-information'''<br />
<br />
=== Provisional idea ===<br />
* Method: Ratings of bifurcation and state space diagrams varied in their aesthetic and informational properties<br />
* Goal: Assess differences in expert and novice perception<br />
* Subject pool: SFI CSSS 2019 (pilot)<br />
* Hypothesis and applications of results: ---<br />
<br />
=== Participants ===<br />
* Mikaela<br />
<br />
==Code Name: Leaf Hunters==<br />
<br />
Previous work has identified quantitative measurements of leaves such as persistent homology, machine learning (e.g. CNNs), and fractal dimensionality as a method to predict the phylogenetic origin of leaves (a classification task of whether the leaf belongs to the species or not). Although interesting, we find this work isn’t useful for building theoretical models of plants within their environments. It is an interesting question whether environmental complexity (however that is measured!) might have relationships to leaf complexity (however that is measured!). This analysis could be useful for developing knew understandings into how and why complexity emerges in plant evolution.<br />
<br />
=== Participants ===<br />
<br />
* Levi Fussell<br />
* Anshuman Swain<br />
* Emily Coco<br />
<br />
== Toward an effective control of malaria in Ghana==<br />
Malaria is a vector-borne disease endemic to many countries in Africa as well as in Asia. This disease results from an interaction between Anopheles gambiae (mosquito), Plasmodium falciparum (parasite) and the human (host). Since Ross division of the host population in purely homogenous compartments, many variants of the model were used to get insights into the transmission and some strategy to control the disease. Thanks to such a simplistic representation of the epidemic, malaria control is remaining since 1950 a nightmare for policymakers since the disease is still persisting though numerous control measures were applied. This study aims at testing the dilution theory while accounting for the spatial repartition of the disease incidence using an adaptive ABM.<br />
<br />
=== Participants ===<br />
*Savi Koissi<br />
*Jeongki Lim<br />
*Anshuman Swain<br />
*Bhartendu Pandey<br />
<br />
<br />
== Chaos in the Brain ==<br />
<br />
Some of us are looking at chaos in the brain and how state-transitions in EEG waves may relate to learning/uncertainty-reduction/entropy. This refers to what Liz mentioned on day 1, where EEG waves of a healthy brain displayed a qualitative change in dynamics (bifurcation) when a parameter was changed, i.e., from *chaotic to more periodic*, under the influence of specific drugs. We also learned that the rabbit brain, when remembering a familiar smell as opposed to an unfamiliar smell, displayed a switch from chaotic to periodic attractor. We are asking the question of what other events may trigger a qualitative change in dynamics from chaotic to periodic, e.g., meditation, sleep or flow experiences, and what they have in common? The first step is to scan the literature.<br />
<br />
Secondly, could it be that the above bifurcations in the brain correspond to human learning and reducing uncertainty in the sense of information theory, i.e., where a state of higher unfamiliarity and novelty corresponds to more chaotic states and familiarity to more periodic states? This is mostly speculation at this point<br />
<br />
General relevant topics<br />
* Chaos in EEG ,<br />
* Complex system modelling in cognitive science<br />
* Chaos related to learning/search/reinforcement learning<br />
* Information theory and entropy in cognition<br />
<br />
=== Participants ===<br />
* Pablo Franco<br />
* David Gier<br />
<br />
== Chaotic Image Encoding ==<br />
<br />
I was (like most folks) really stoked on the ability of chaotic mapping (e.g., Darbby's example) to introduce some seemingly random patterns to some series that preserves a bit of the 'flavor' of the original. I like simple images and pixel art / animations -- in that spirit, I want to employ similar approaches to investigate how we can use chaotic attractors to manipulate images. Specifically, many processes that we see in the natural world are beautiful yet complicated to recreate in faithful ways - as an example, reflections of light on the surface of a fluid, or the dynamics of flames in a fire. <br />
<br />
<br />
=== Participants ===<br />
* Dan Krofcheck<br />
* Ludvig Holmér<br />
<br />
<br />
== Rules and Regulations ==<br />
<br />
We are working to understand the dynamics of U.S. federal rulemaking over a period of 20 years using a mixture of topic modeling and dynamical networks.<br />
<br />
=== Participants ===<br />
Adam<br />
Bhargav<br />
Andrea<br />
Elissa<br />
Hunter<br />
Brennan<br />
Aabir</div>AdaReyhttps://wiki.santafe.edu/index.php?title=2019_Dome_Showing_9:00PM&diff=770852019 Dome Showing 9:00PM2019-06-19T07:06:49Z<p>AdaRey: </p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
'''9:00 PM Dome Showing'''<br><br />
Please sign up to reserve a seat.<br><br />
Limit of 33 for each showing but there are three (3) showings:<br><br />
7:00 PM and 8:00 PM and 9:00 PM, so everyone will get a seat to the show.<br />
<br><br><br />
<br />
# Sage Crump<br />
# ill Weaver<br />
# Carlos (Los) Garcia<br />
# Wesley (Wes) Taylor<br />
# Teianna Mitchell<br />
# Bakus<br />
# Pattrick<br />
# Ian<br />
# Ethan<br />
# Laura<br />
# Dries<br />
# Mikaela<br />
# Adam<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
#<br />
Limit</div>AdaReyhttps://wiki.santafe.edu/index.php?title=Zefferman_Workshop_2019&diff=76917Zefferman Workshop 20192019-06-18T01:16:01Z<p>AdaRey: </p>
<hr />
<div>{{Complex Systems Summer School 2019}}<br />
<br />
<br />
Signup page for Matt Zefferman's workshop!<br />
<br />
How do researchers reason through the steps of making evolutionary models? Here is your chance to try. On Monday Matt Zefferman will introduce evolutionary game theory and on Wednesday Liz Hobson will describe her empirical work on hierarchy and parakeet aggression. However, there currently isn't a theory about what hierarchical aggression strategies we should expect to evolve under different conditions. Matt and Liz have been thinking about modeling this and thought it would be a useful example for CSSS students interested in walking through the modeling process. When canonical models are presented in papers, coursework or textbooks there is often little information about how the sausage was made. Much like sausage-making - model-making is often not pretty. But hopefully you get delicious sausage at the end of the process. Also, like sausage-making most modeling is done in private. This will be Liz and Matt's first attempt to try it in public. The effort might fail spectacularly (this often happens more often than not), but going through the process can still be useful.<br />
<br />
Limited to 20 spots, so we're going to run a lotto to decide seats. If you're interested, sign up here!<br />
<br />
<br />
# JP<br />
# Pam Mantri<br />
# Henri<br />
# Arta <br />
# Elissa<br />
# Levi<br />
# Alex<br />
# Emily Coco<br />
# Gen<br />
# Dries<br />
# Andrew Gillreath-Brown<br />
# Harun<br />
# Mackenzie<br />
# Jessica Brumley<br />
# Ritwika<br />
# Erwin<br />
# Mark Chu<br />
# Mikaela<br />
# Ian<br />
# Fabian<br />
# Toni<br />
# Adam</div>AdaRey