Santa Fe Institute Events Wiki - User contributions [en]

Complex Systems Summer School 2019-Project Presentations

2019-06-28T22:15:10Z

ErwinK:

{{Complex Systems Summer School 2019}}

'''TUESDAY'''

Morning

*9.30: Resilience in Conway's Game of Life (Alex, Arta, Elissa, Luther, Kazuya, Patrick, Wenqian)
*9.40: Production Webs in Minecraft (Chris, Erwin, Kate, Bakus, Patrick)
*9.50:
*10.00:
*10.10:
*10.20:
*10.30:
*10.40:
*10.50:

*11.20:
*11.30:
*11.40:
*11.50:
*12.00:
*12.10:
*12.20:

'''WEDNESDAY'''

Morning

*9.30: Modeling and predicting food insecurity using a resilience lens (Erwin, Andrew, Alexander, Pam, Dan, Fabian)
*9.40:
*9.50:
*10.00:
*10.10:
*10.20:
*10.30:
*10.40:
*10.50:

*11.20:
*11.30:
*11.40:
*11.50:
*12.00:
*12.10:
*12.20:

Afternoon

*14.00: Computational Synesthesia
*14.10:
*14.20:
*14.30:
*14.40:
*14.50:
*15.00:
*15.10:
*15.20:

*15.50:
*16.00:
*16.10:
*16.20:
*16.30:
*16.40:
*16.50:
*17.00:
*17.10:
*17.20:

Complex Systems Summer School 2019-Project check-ins

2019-06-27T23:01:09Z

ErwinK:

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

'''Tuesday afternoon'''

'''ROOM 1 - IAIA'''
* 16.30 - 16.40: Looking for resilient patterns in Conway's Game of Life
* 16.40 - 16.50: Chaotic Image Encoding
* 16.50 - 17.00: Sci-Fi ABM-thology (Andrew, Harun, Jeongki)
* 17.10 - 17.20: intersections of community engaged research and css (Robert Dee Winnie Travis)
* 17.20 - 17.30: Paradigmatic Relations (Yuka, Mark)
* 17.30 - 17.40: People Are Good (Ernest, Hunter, Jackie, Brennan)
* 17.40 - 17.50: Topological diversity in complex networks (Andrea, Anton, Keith, Travis, Xin, Yuka)
* 17.50 - 18.00: Is Entropy Sexy? Quantifying the fitness effects of novelty in courtship displays (Kenzie, Henri, Ritu, Pablo Flores)
* 18.00 - 18.10: Modelling the spatial diffusion of human language (Henri, Dee, Harun, Kenzie, Pablo Flores, Ritu)

'''ROOM 2 - IAIA'''
* 16.30 - 16.40: Cities and scale with resource restrictions
* 16.40 - 16.50: What are Californians planning?
* 16.50 - 17.00: Simulating evolution of bacterial cells’ decision to divide (Kunaal, Kazuya)
* 17.10 - 17.20: Computational Synesthesia (Ruggiero, Doug G, Aabir, Bhargav, Ethan, Mark)
* 17.20 - 17.30: CSSSSSS
* 17.30 - 17.40: Perceptions of Aesthetic and Informational Content in Expert and Novice Judgments (Mikaela, Ethan, Mark)
* 17.40 - 17.50: Taming the Complex via Concept Mapping (Pam Dee Wenqian)
* 17.50 - 18.00: Too Much Information and Segregation (Chris Q, Pablo Franco, Wenqian, Jordi, Brennan)
* 18.00 - 18.10: Rules and Regulations (Adam, Bhargav, Aabir, Hunter, Brennan, Ellisa, Andrea)

'''Wednesday afternoon'''

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

'''ROOM 2 - IAIA'''
* 16.30 - 16.40: The Perfect Door: Design by Complexity Science (Jeongki, Kenzie, Luther)
* 16.40 - 16.50: Lingua Technica—Assessing the Cultural Impact of Technology (Dakota, Jeongki, Pablo F, Ignacio, Harun, Doug, John, Chris B-J)
* 16.50 - 17.00: Dynamics of Political Ideas on Social Networks (David, Jackie, Ludvig, Ernest, Robert, Ritu, Kyle)
* 17.00 - 17.10: Housing Market Model
* 17.10 - 17.20: <s>Computational Social Science in Decision-Making: an Opioid Epidemic Case-Study (Kyle, Robert, David, Xin)</s> ''Project Retired''
* 17.20 - 17.30: Science Policy & Communication: A study in information loss (John M, Dakota, Ritu, Chris, Mackenzie, Kyle, Andrew)
* 17.30 - 17.40: Network fossilization (Andrew, Anshuman, Emily, Kate, Dries, Jack)
* 17.40 - 17.50: Lilliput Effect (Anshuman, Jack, Jordi, Yuka)
* 17.50 - 18.00: Code Name: Leaf Hunters (Levi, Anshuman, Emily)

'''ROOM 3 - IAIA'''
* 16.30 - 16.40: Resilence and presilience in protein network structure (April, Brennan, Keith, Ludvig, Laura, Mackenzie, Doug R, Anshuman)
* 16.40 - 16.50: How does resource availability and usage affect cooperation? (Anshuman, Dries, Marjorie, Ruggerio, Kirtus, Ian, Billy, Kunaal)
* 16.50 - 17.00: Toward an effective control of malaria in Ghana (Savi Koissi, Jeongki Lim, Anshuman Swain, Bhartendu Pandey)
* 17.00 - 17.10: Culture fractal (Marjorie, Dries, Winnie)
* 17.10 - 17.20: Weighted Expectations (Mikaela, Ahyan, Elissa, Arta, Paula)
* 17.20 - 17.30: Concave Utility as Efficient Encoding (Mikaela, Paula, Elissa)
* 17.30 - 17.40: HebbWeb (Pasha, Chiara, Levi, Xin, Billy)
* 17.40 - 17.50: Complex Movements and the City of Detroit (Jackie, Elissa, Travis and Ernest)
* 17.50 - 18.00: Evaluating Two Mechanisms for the Evolution of Social Complexity (Alexander, Marjorie, Ignacio, Dries, Kazuya)

'''Thursday afternoon'''

'''ROOM 1 - IAIA'''
* 16.30 - 16.40: The Individual Lives of Microbes (Jessica L, Kirtus, Daniel, Pam, Ritu, Adam)
* 16.40 - 16.50: HTG Robot Swarm (Levi Fussell, Mackenzie Johnson, Jessica Lee, Kirtus Leyba, Anshuman Swain)

'''ROOM 2 - IAIA'''
* 16.30 - 16.40: Modeling Minecraft's Crafting Web
* 16.40 - 16.50: From metaphors to models. How different modes of thought influence scientific research (Dries, Doug Reckamp, Ethan, Jackie)

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

'''ROOM 1 - SFI'''
* 14.00 - 14.10: Network fossilization (Andrew, Anshuman, Emily, Kate, Dries, Jack)
* 14.10 - 14.20: Regulations
* 14.20 - 14.30: Multi-dimensional money: Aligning social and moral incentives (Shruti, Pasha, Ernest)
* 14.30 - 14.40: Erwin - check-in
* 14.40 - 14.50:
* 14.50 - 15.00:
*
* 15.40 - 15.50:
* 15.50 - 16.00:
* 16.00 - 16.10:
* 16.10 - 16.20:
* 16.20 - 16.30:
* 16.20 - 16.30:

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

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

Complex Systems Summer School 2019-Project Presentations

2019-06-27T19:35:53Z

ErwinK:

{{Complex Systems Summer School 2019}}

'''Preference list'''

'''Tuesday'''

# Weighted Expectations (Mikaela, Ahyan, Elissa, Arta, Paula)
# The individual lives of microbial cells (Jessica Lee, Kirtus, Ritu, Daniel, Adam, Pam)
# Modelling the spatial diffusion of human languages (Henri, Ritu, Harun, Kenzie, Pablo Flores)
# Modeling Minecraft's Crafting Web (Erwin, Chris Q., Alexander Bakus, Patrick, Kate)
# Resilience in Conway's Game of Life (Alex, Arta, Elissa, German, Kazuya, Luther, Patrick, Wenqian)
# Housing models from economic and demographic perspectives (John Shuler, Ian)

'''Wednesday'''

# Computational Synesthesia (Doug, Ethan, Aabir, Bhargav, Mark, Ruggiero)
# Modeling food insecurity using a resilience lens (Erwin, Andrew, Alexander Bakus, Pam, Dan, Fabian)

'''Later Wednesday'''

# Cities and Scaling (Catherine, Jessica B, Ian, Gen)
#

Complex Systems Summer School 2019-Project Presentations

2019-06-27T15:23:12Z

ErwinK:

Complex Systems Summer School 2019-Project check-ins

2019-06-25T14:52:29Z

ErwinK:

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

'''Tuesday afternoon'''

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

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

'''Wednesday afternoon'''

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

'''ROOM 2 - IAIA'''
* 16.30 - 16.40: The Perfect Door: Design by Complexity Science (Jeongki, Kenzie, Luther)
* 16.40 - 16.50: Lingua Technica—Assessing the Cultural Impact of Technology (Dakota, Jeongki, Pablo F, Ignacio, Harun, Doug, John, Chris B-J)
* 16.50 - 17.00: Dynamics of Political Ideas on Social Networks (David, Jackie, Ludvig, Ernest, Robert, Ritu, Kyle)
* 17.10 - 17.20: Computational Social Science in Decision-Making: an Opioid Epidemic Case-Study (Kyle, Robert, David, Xin)
* 17.20 - 17.30: Science Policy & Communication: A study in information loss (John M, Dakota, Ritu, Chris, Mackenzie, Kyle, Jackie)
* 17.30 - 17.40:
* 17.40 - 17.50:
* 17.50 - 18.00:

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

'''Thursday afternoon'''

'''ROOM 1 - IAIA'''
* 16.30 - 16.40: The Individual Lives of Microbes (Jessica L, Kirtus, Daniel, Pam, Ritu, Daniel)
* 16.40 - 16.50: HTG Robot Swarm (Levi Fussell, Mackenzie Johnson, Jessica Lee, Kirtus Leyba, Anshuman Swain)

'''ROOM 2 - IAIA'''
* 16.30 - 16.40: Modeling Minecraft's Crafting Web
* 16.40 - 16.50:

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

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

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

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

Complex Systems Summer School 2019-After Hours

2019-06-22T03:27:56Z

ErwinK:

{{Complex Systems Summer School 2019}}

Please use this space to plan social events.

==White Sands==

Hello! Some of us are going to white sands on Saturday and have some spots left in cars if anyone wants to join

Car 1: Amy – leaving by 5:00 AM on Saturday morning (to get permits!)

* Lou
* Doug
* Jack
* Anshuman

Car 2: Adam (also leaving at 5am)

* Bhargav
* Paula
* Pablo Flores

Car 3: David (leaving at 5AM also)
*Aabir
*Chiara
*Ruggiero
*Shruti

Car 5 (rental):
*Patrick
*Backus
*Fabian
*Erwin

We can get permits for 2 campsites (up to 12 people). So, first come/first serve on the remaining spots. The drive is 4 hours from IAIA.

The spots are backpacking only – about 2 km walk from the car to a site (on sand). Please be sure to read this info page (https://www.nps.gov/whsa/planyourvisit/backpacking.htm). Expect it to be both very hot and probably quite chilly at night. A sleeping bag is necessary, lots of water, the ability to carry in/out food, and comfortable with no-bathroom conditions.

I think it’s easiest for each car to organize themselves – other than the permits (which I will grab), the rest of the details should be self-organized :)

==Weekend Shuttles==

A shuttle will be available to get you to and from downtown Santa Fe on Friday evening and Saturday mid-morning through the afternoon. The shuttle will be making runs back and forth between the downtown area and IAIA campus.

Shuttle schedule:

FRIDAY: 10:00pm - 1:00am

SATURDAY: 11:30am - 2:00pm and 10:30pm - 1:00am

We have two pickup spots:

Water & Sandoval at 269 W. Water Street

Railyard Pavillion at 1609 Paseo De Peralta

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.

The plan is to have the "beginning" return trip of the schedule depart from the Railyard location, with the final pickup night will departing from Water & Sandoval at 1:00am.

Final pickups for both days will be the Water & Sandoval location, so your worst-case scenario will be to meet the shuttle at 1:00am.

As a reminder, Uber and Lyft are also available and efficient ways of getting around.

 Completed Activities 

==Monday Shopping==

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

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

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

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

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

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

==Second St. Rufina==

===June 13 ===

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

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

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

Larremore Workshop: How To Rank Things & Pairwise Comparisons 2019

2019-06-21T01:59:58Z

ErwinK:

{{Complex Systems Summer School 2019}}

Dan Larremore Workshop

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

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

LARREMORE WORKSHOP LOTTO SIGNUP!!

# JP
# Andrew Gillreath-Brown
# Ritwika
# Ernest
# Aabir
# Alec
# Brennan
# Fabian
# Elissa
# Erwin

Complex Systems Summer School 2019-After Hours

2019-06-20T18:34:45Z

ErwinK:

{{Complex Systems Summer School 2019}}

Please use this space to plan social events.

==White Sands==

Hello! Some of us are going to white sands on Saturday and have some spots left in cars if anyone wants to join

Car 1: Amy – leaving by 5:00 AM on Saturday morning (to get permits!)

* Lou
* Erwin
*Jack
*Anshuman

Car 2: Adam (also leaving at 5am)

* Bhargav
* Kenzie (tentatively? plans may change tomorrow!)
* David (I can also drive a third car if necessary)
* Pablo Flores

We can get permits for 2 campsites (up to 12 people). So, first come/first serve on the remaining spots. The drive is 4 hours from IAIA.

The spots are backpacking only – about 2 km walk from the car to a site (on sand). Please be sure to read this info page (https://www.nps.gov/whsa/planyourvisit/backpacking.htm). Expect it to be both very hot and probably quite chilly at night. A sleeping bag is necessary, lots of water, the ability to carry in/out food, and comfortable with no-bathroom conditions.

I think it’s easiest for each car to organize themselves – other than the permits (which I will grab), the rest of the details should be self-organized :)

==Weekend Shuttles==

A shuttle will be available to get you to and from downtown Santa Fe on Friday evening and Saturday mid-morning through the afternoon. The shuttle will be making runs back and forth between the downtown area and IAIA campus.

Shuttle schedule:

FRIDAY: 10:00pm - 1:00am

SATURDAY: 11:30am - 2:00pm and 10:30pm - 1:00am

We have two pickup spots:

Water & Sandoval at 269 W. Water Street

Railyard Pavillion at 1609 Paseo De Peralta

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.

The plan is to have the "beginning" return trip of the schedule depart from the Railyard location, with the final pickup night will departing from Water & Sandoval at 1:00am.

Final pickups for both days will be the Water & Sandoval location, so your worst-case scenario will be to meet the shuttle at 1:00am.

As a reminder, Uber and Lyft are also available and efficient ways of getting around.

 Completed Activities 

==Monday Shopping==

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

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

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

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

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

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

==Second St. Rufina==

===June 13 ===

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

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

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

2019 Dome Showing 7:00PM

2019-06-18T21:32:25Z

ErwinK:

{{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
#
#
#
#
#
#
#.
#
#
#
#
#
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#
#
#
#
#
#
#
#
#
#
#
#
#
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Limit

Zefferman Workshop 2019

2019-06-17T21:23:53Z

ErwinK:

{{Complex Systems Summer School 2019}}

Signup page for Matt Zefferman's workshop!

Limited to 20 spots, so we're going to run a lotto to decide seats. If you're interested, sign up here!

# JP
# Pam Mantri
# Henri
# Arta
# Elissa
# Levi
# Alex
# Emily Coco
# Gen
# Dries
# Andrew Gillreath-Brown
# Harun
# Mackenzie
# Jessica Brumley
# Ritwika
# Erwin

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-17T16:08:08Z

ErwinK:

{{Complex Systems Summer School 2019}}

Project and working group ideas go here.

==Two ideas from Cat==

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

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

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

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

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

==Dangerous idea about reviewing==

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

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

(From Shruti)

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

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

(From Kunaal)

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

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

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

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

==Modelling the spatial diffusion of human languages==

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

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

===Data===

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

===Papers to read===

* Let's add them here

===Interested participants===

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

===Future plans===

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

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

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

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

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

===Interested Participants===
Jessica Brumley

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

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

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

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

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

===Meeting Schedule & Notes===
TBA

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

==Science Policy & Communication==

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

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

===Communication Channels===

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

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

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

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

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

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

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

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

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-17T15:24:22Z

ErwinK:

{{Complex Systems Summer School 2019}}

Project and working group ideas go here.

==Two ideas from Cat==

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

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

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

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

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

==Dangerous idea about reviewing==

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

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

(From Shruti)

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

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

(From Kunaal)

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

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

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

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

==Modelling the spatial diffusion of human languages==

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

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

===Data===

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

===Papers to read===

* Let's add them here

===Interested participants===

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

===Future plans===

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

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

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

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

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

===Interested Participants===
Jessica Brumley

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

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

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

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

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

===Meeting Schedule & Notes===
TBA

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

==Science Policy & Communication==

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

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

===Communication Channels===

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

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

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

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

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

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

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

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

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-17T15:23:34Z

ErwinK:

{{Complex Systems Summer School 2019}}

Project and working group ideas go here.

==Two ideas from Cat==

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

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

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

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

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

==Dangerous idea about reviewing==

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

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

(From Shruti)

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

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

(From Kunaal)

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

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

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

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

==Modelling the spatial diffusion of human languages==

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

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

===Data===

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

===Papers to read===

* Let's add them here

===Interested participants===

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

===Future plans===

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

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

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

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

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

===Interested Participants===
Jessica Brumley

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

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

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

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

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

===Meeting Schedule & Notes===
TBA

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

==Science Policy & Communication==

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

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

===Communication Channels===

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

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

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

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

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

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

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

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

Complex Systems Summer School 2019-Tutorials

2019-06-14T22:30:56Z

ErwinK: /* 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 6/15 & 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)
* Jeongki Lim
* Andrea Bacilieri
* John Schuler

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

==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

== 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
# Jeongki Lim
# Ernest Aigner
# Robert Coulter
# Winnie Poel
# Travis Moore
# Pablo M. Flores
# Catherine Brinkley

=== 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