Santa Fe Institute Events Wiki - User contributions [en]

Complex Systems Summer School 2019-Social Shuttle Times

2019-06-25T21:29:27Z

GRomo: /* Social Shuttle Times with Lorenzo */

{{Complex Systems Summer School 2019}}

==Social Shuttle Times with Lorenzo==

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

Shuttle schedule:

'''SATURDAY, JUNE 22'''

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

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

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

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

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

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

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

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

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

'''TUESDAY, JUNE 25'''

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

Shuttle sign-up:
7:00 pm
* Jackie
* Chris
* Emily
* Elissa
* Pablo
* Mackenzie
* Dee

2019 Dome Showing 7:00PM

2019-06-19T00:28:11Z

GRomo:

{{Complex Systems Summer School 2019}}

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

#Pablo M. Flores
#Jessica Brumley
#Erwin Knippenberg
#Jordi Piñero
#Jack Shaw
#Arta Cika
#Elissa Cohen
#Anton Pichler
#Ignacio Garnham
#Germán Kruszewski
#Mackenzie Johnson
#April Kleppe
#Chris Boyce-Jacino
#John Malloy
#Alec Kirkley
#Shihui Feng
#Dee Romo
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
Limit

Complex Systems Summer School 2019-Tutorials

2019-06-18T13:36:20Z

GRomo: /* Interested Participants */

{{Complex Systems Summer School 2019}}

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

= Upcoming Tutorials =

==Nonlinear Dynamics Discussion Session - [https://wiki.santafe.edu/index.php/Daniel_Borrero Daniel Borrero] (9:30 AM 6/16)==

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!

=== Suggested Date and Time ===
Given that brunch will not be served till 11 AM on Sunday (6/16), I propose meeting in the lecture hall at 9:30 AM.

=== Interested Participants ===
Sign up below in the bulleted list below if you are interested. If you have experience with dynamical systems and would like to share your expertise, please feel free to join. You can add more slots as needed:
* Daniel Borrero (presenter)
* Jeongki Lim
* Andrea Bacilieri
* John Schuler
* Kazuya Horibe

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

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

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

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

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

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

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

==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (3:15 PM 6/19)==

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

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

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

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

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

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

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

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

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

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

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

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

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

I'd be doing:

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

=== Preparing for the tutorial ===

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

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

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

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

=== Note ===

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

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

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

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

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

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

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

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

=== Interested Participants ===
* Jeongki Lim
* Jessica Lee
* Bhartendu
* Henri
* Wenqian
* Arta

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

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

From her description:

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

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

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

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

= Past Tutorials =
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:
* Taylor series and linearization of nonlinear systems
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from
* Floquet multipliers and diverge of trajectories in maps
* Where the quadratic term in the logistic map comes from

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

Complex Systems Summer School 2019-Tutorials

2019-06-18T03:42:14Z

GRomo: /* Interested Participants */

{{Complex Systems Summer School 2019}}

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

= Upcoming Tutorials =

==Nonlinear Dynamics Discussion Session - [https://wiki.santafe.edu/index.php/Daniel_Borrero Daniel Borrero] (9:30 AM 6/16)==

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!

=== Suggested Date and Time ===
Given that brunch will not be served till 11 AM on Sunday (6/16), I propose meeting in the lecture hall at 9:30 AM.

=== Interested Participants ===
Sign up below in the bulleted list below if you are interested. If you have experience with dynamical systems and would like to share your expertise, please feel free to join. You can add more slots as needed:
* Daniel Borrero (presenter)
* Jeongki Lim
* Andrea Bacilieri
* John Schuler
* Kazuya Horibe

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

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

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

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

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

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

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

==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (3:15 PM 6/19)==

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

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

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

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

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

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

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

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

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

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

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

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

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

I'd be doing:

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

=== Preparing for the tutorial ===

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

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

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

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

=== Note ===

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

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

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

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

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

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

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

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

=== Interested Participants ===
* Jeongki Lim
* Jessica Lee
* Bhartendu
* Henri
* Wenqian
* Arta

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

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

From her description:

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

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

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

# JP
# Ahyan Panjwani
# Dee
# Ignacio
#
#
#
#
#

= Past Tutorials =
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:
* Taylor series and linearization of nonlinear systems
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from
* Floquet multipliers and diverge of trajectories in maps
* Where the quadratic term in the logistic map comes from

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

Complex Systems Summer School 2019-Tutorials

2019-06-18T03:41:16Z

GRomo: /* Kirsten Moy, 7:00pm, Tuesday June 17 */

{{Complex Systems Summer School 2019}}

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

= Upcoming Tutorials =

==Nonlinear Dynamics Discussion Session - [https://wiki.santafe.edu/index.php/Daniel_Borrero Daniel Borrero] (9:30 AM 6/16)==

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!

=== Suggested Date and Time ===
Given that brunch will not be served till 11 AM on Sunday (6/16), I propose meeting in the lecture hall at 9:30 AM.

=== Interested Participants ===
Sign up below in the bulleted list below if you are interested. If you have experience with dynamical systems and would like to share your expertise, please feel free to join. You can add more slots as needed:
* Daniel Borrero (presenter)
* Jeongki Lim
* Andrea Bacilieri
* John Schuler
* Kazuya Horibe

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

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

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

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

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

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

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

==Classical Hypothesis Testing- The Course You Think You Don't Need - John S. Schuler (3:15 PM 6/19)==

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

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

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

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

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

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

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

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

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

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

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

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

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

I'd be doing:

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

=== Preparing for the tutorial ===

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

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

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

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

=== Note ===

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

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

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

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

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

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

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

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

=== Interested Participants ===
* Jeongki Lim
* Jessica Lee
* Bhartendu
* Henri
* Wenqian
* Arta

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

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

From her description:

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

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

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

# JP
# Ahyan Panjwani
# Dee
#
#
#
#
#
#

= Past Tutorials =
== Nonlinear Dynamics Q&A I w/ D. Borrero (6/10) ==
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:
* Taylor series and linearization of nonlinear systems
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from
* Floquet multipliers and diverge of trajectories in maps
* Where the quadratic term in the logistic map comes from

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

Complex Systems Summer School 2019-Tutorials

2019-06-14T04:08:32Z

GRomo: /* Interested Participants */

{{Complex Systems Summer School 2019}}

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

= Upcoming Tutorials =

==Nonlinear Dynamics Discussion Session - [https://wiki.santafe.edu/index.php/Daniel_Borrero Daniel Borrero] (9:30 AM 6/15 & 6/16)==

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!

=== Suggested Date and Time ===
Given that brunch will not be served till 11 AM on Saturday and Sunday, I propose meeting in the lecture hall at 9:30 on Saturday 6/15 (and maybe on Sunday 6/16 if needed).

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

If you can't make it and want to talk about any of these subjects, feel free to come chat with [https://wiki.santafe.edu/index.php/Daniel_Borrero me].

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

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

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

I'd be doing:

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

=== Suggested Date and Time ===
The tutorial usually runs for an hour and a half (maybe 45 mins - break - 45 mins?). I was thinking of doing this some time next week, either on Tuesday (the 18th) or Thursday (the 20th). I reckon that would be enough time for people to figure out if this might be relevant to their work!
I'll update this section on Monday (17th) with exact time/place.

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

# Bhargav (presenter) 
# Arta Cika
# Xin Ran
# Daniel Borrero
# Jackie Brown
# Pam Mantri
# Dee Romo

=== Note ===

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

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

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

= Past Tutorials =
== Nonlinear Dynamics Q&A w/ D. Borrero (6/10) ==
Informal discussion of various topics in Nonlinear Dynamics. Topics covered included:
* Taylor series and linearization of nonlinear systems
* Why the stability of the fixed point has to do with the slope of map at the fixed point (i.e., f'(x*))
* How to think about dynamical systems with continuous time systems ("flows") that are governed by differential equations in 1-dimension
* Why trajectories in chaotic systems diverge exponentially and where exactly a Lyapunov exponent comes from
* Floquet multipliers and diverge of trajectories in maps
* Where the quadratic term in the logistic map comes from

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-14T03:51:24Z

GRomo: /* Interested participants */

{{Complex Systems Summer School 2019}}

Project and working group ideas go here.

==Two ideas from Cat==

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

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

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

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

==Dangerous idea about reviewing==

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

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

(From Shruti)

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

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

(From Kunaal)

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

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

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

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

==Modelling the spatial diffusion of human languages==

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

===Data===

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

===Papers to read===

* Let's add them here

===Interested participants===

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

===Future plans===

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

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-14T03:51:02Z

GRomo: /* Interested Participants */

{{Complex Systems Summer School 2019}}

Project and working group ideas go here.

==Two ideas from Cat==

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

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

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

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

==Dangerous idea about reviewing==

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

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

(From Shruti)

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

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

(From Kunaal)

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

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

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

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

==Modelling the spatial diffusion of human languages==

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

===Data===

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

===Papers to read===

* Let's add them here

===Interested participants===

* [http://henr.in Henri]
* Let's add ourselves here

===Future plans===

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

Complex Systems Summer School 2019-After Hours

2019-06-10T23:54:02Z

GRomo: /* Lorenzo's Shuttle (15 seats) */

{{Complex Systems Summer School 2019}}

Please use this space to plan social events.

==Monday Shopping==

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

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

 First Run (~7:00pm) 
1. Henri 
2. Mackenzie Johnson 
3. Paula Parpart 
4. Pam Mantri 
5. Chris Quarles 
6. Bakus 
7. Kunaal Joshi 
8. Dakota 
9. Wenqian 
10. Ritu 
11. Germán 
12. Winnie 
13. Andrew G.-B. 
14. Pablo (Melbourne) 
15. Yuka 

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

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

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