Santa Fe Institute Events Wiki - User contributions [en]

Complex Systems Summer School 2019-Project Presentations

2019-07-03T03:29:50Z

Shruti:

{{Complex Systems Summer School 2019}}

'''TUESDAY'''

Morning

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

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

'''WEDNESDAY'''

Morning

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

*11.20: Science Policy and Communication (John M, Chris B-J, Dakota Murray, Mackenzie Johnson, Kyle, Ritu, Andrew G-B)
*11.30: Lingua Technia (Dakota, John M, Chris B-J, Jeongki, Ignacio, Pablo F, Doug)
*11.40: Taming the Complex via Concept Mapping (Pam Dee Wenqian)

*12.00: Artificial fossilization of animal interaction networks (Jack, Kate, Andrew, Anshuman, Dries, Emily)
*12.10: Toward an effective control of malaria in Ghana (Koissi, Jeongki, Anshuman, Bhartendu)
*12.20: Codename Leaf hunters (Levi, Emily, Anshuman)

Afternoon

*14.00: Computational Synesthesia (Doug, Bhargav, Aabir, Mark, Ruggerio, Ethan)
*14.10: HTG in Robotic Swarms (Kirtus/Levi/Jessica/Mackenzie/Anshuman)
*14.20: The Perfect Door: Design & Complexity (Jeongki, Kenzie, Luther)
*14.30: Scaling of water resources in US cities (Catherine, Jessica Brumley, Gen, Ian)
*14.40: Concave Utility as Efficient Encoding (Mikaela, Paula, Elissa)
*14.50: Sci-fi ABM (Jeongki, Harun, Andrew)
*15.00: Evolution of Cells' Decision to Divide (Kunaal, Kazuya)
*15.10: Models and Metaphors (Dries, Doug Reckamp, Ethan)
*15.20: Paradigmatic Relations! (Yuka, Mark)

*15.50: Perception of Aesthetic Information (Mikaela, Ethan, Mark)
*16.00: Chaotic Brain (Pablo, Paula, Keith, Laura, David, Mikaela, Levi)
*16.10: HebbWeb (Pasha, Chiara, Levi, Xin, Billy)
*16.20: River networks (Gen, Brennan, Alec, Dan)
*16.30: How does resource availability and usage affect cooperation? (Anshuman, Dries, Marjorie, Ruggiero, Kirtus, Ian, Billy, Kunaal)
*16.40: Modelling the spatial diffusion of human language (Henri, Harun, Kenzie, Pablo Flores, Ritu)
*16.50:
*17.00:
*17.10:
*17.20:

Complex Systems Summer School 2019-Project Presentations

2019-07-03T00:13:59Z

Shruti:

{{Complex Systems Summer School 2019}}

'''TUESDAY'''

Morning

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

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

'''WEDNESDAY'''

Morning

*9.30: Modeling and predicting food insecurity using a resilience lens (Erwin, Andrew, Alexander, Pam, Dan, Fabian)
*9.40: Complexity & Consent (Jackie, Elissa, Travis and Ernest)
*9.50: Dynamics of Political Ideas on Social Networks (David, Jackie, Ludvig, Ernest, Ritu, Kyle)
*10.00: Resilence and presilience in protein network structure (April, Brennan, Keith, Ludvig, Laura, Mackenzie, Doug R, Anshuman)
*10.10: Is entropy sexy? (Kenzie, Henri, Ritu, Pablo)
*10.20: Evaluating Two Mechanisms for the Evolution of Social Complexity (Alex, Dries, Marjorie, Ignacio, Kazuya)
*10.30: Cultural fractals: Evidence for punctuated equilibria and other themes in a time series of online interaction (Marjorie, Winnie, Dries, Jessica)
*10.40: Network Control with Graph Signal Processing (Alec, Billy, Brennan, Harun)
*10.50: Game Warping (Shruti, Aabir)
*11.00: Multi-dimensional money (Shruti, Ernest, Pavel)

*11.20: Science Policy and Communication (John M, Chris B-J, Dakota Murray, Mackenzie Johnson, Kyle, Ritu, Andrew G-B)
*11.30: Lingua Technia (Dakota, John M, Chris B-J, Jeongki, Ignacio, Pablo F, Doug)
*11.40: Taming the Complex via Concept Mapping (Pam Dee Wenqian)

*12.00: Artificial fossilization of animal interaction networks (Jack, Kate, Andrew, Anshuman, Dries, Emily)
*12.10: Toward an effective control of malaria in Ghana (Koissi, Jeongki, Anshuman, Bhartendu)
*12.20: Codename Leaf hunters (Levi, Emily, Anshuman)

Afternoon

*14.00: Computational Synesthesia (Doug, Bhargav, Aabir, Mark, Ruggerio, Ethan)
*14.10: HTG in Robotic Swarms (Kirtus/Levi/Jessica/Mackenzie)
*14.20: The Perfect Door: Design & Complexity (Jeongki, Kenzie, Luther)
*14.30: Scaling of water resources in US cities (Catherine, Jessica Brumley, Gen, Ian)
*14.40: Concave Utility as Efficient Encoding (Mikaela, Paula, Elissa)
*14.50: Sci-fi ABM (Jeongki, Harun, Andrew)
*15.00: Evolution of Cells' Decision to Divide (Kunaal, Kazuya)
*15.10: Models and Metaphors (Dries, Doug Reckamp, Ethan)
*15.20: Paradigmatic Relations! (Yuka, Mark)

*15.50: Perception of Aesthetic Information (Mikaela, Ethan, Mark)
*16.00: Chaotic Brain (Pablo, Paula, Keith, Laura, David, Mikaela, Levi)
*16.10: HebbWeb (Pasha, Chiara, Levi, Xin, Billy)
*16.20: River networks (Gen, Brennan, Alec, Dan)
*16.30: How does resource availability and usage affect cooperation? (Anshuman, Dries, Marjorie, Ruggiero, Kirtus, Ian, Billy, Kunaal)
*16.40: Modelling the spatial diffusion of human language (Henri, Harun, Kenzie, Pablo Flores, Ritu)
*16.50:
*17.00:
*17.10:
*17.20:

Complex Systems Summer School 2019-After Hours

2019-07-02T18:25:33Z

Shruti:

{{Complex Systems Summer School 2019}}

Please use this space to plan social events.

==Going to Albuquerque on July 4th - 9AM departure==

Organizing Ubers/Lyfts or personal cars for people needing to go to Albuquerque on July 4th.

# Jack (flight at 12:10pm)
# Shruti (flight at 23:00)
#
#

==Going to Albuquerque on July 5th==

Organizing Ubers/Lyfts or personal cars for people needing to go to Albuquerque on July 5th. Please list your name if you need a ride to Albuquerque airport on July 5th. Also, list what time you need to be there for your flight. My flight (Andrew) is not until 16:30, but am happy to go earlier.

# Andrew (16:30 departure, but will probably hang out in Albuquerque)
# Harun (14:00 departure, open to all combinations)
# Mikaela (13:07 departure)
# Pablo Franco (13:00 departure)
# Mark Chu (unspecified PM arrival to ABQ) ... I think the Railrunner sounds fun if anyone's got a flexi schedule!

==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 

==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 :)

==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 
...

Complex Systems Summer School 2019-Project Presentations

2019-07-02T17:13:15Z

Shruti:

{{Complex Systems Summer School 2019}}

'''TUESDAY'''

Morning

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

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

'''WEDNESDAY'''

Morning

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

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

Afternoon

*14.00: Computational Synesthesia (Doug, Bhargav, Aabir, Mark, Ruggerio, Ethan)
*14.10: HTG in Robotic Swarms (Kirtus/Levi/Jessica/Mackenzie)
*14.20: The Perfect Door: Design & Complexity (Jeongki, Kenzie, Luther)
*14.30: Scaling of water resources in US cities (Catherine, Jessica Brumley, Gen, Ian)
*14.40: Concave Utility as Efficient Encoding (Mikaela, Paula, Elissa)
*14.50: Sci-fi ABM (Jeongki, Harun, Andrew)
*15.00: Evolution of Cells' Decision to Divide (Kunaal, Kazuya)
*15.10: Models and Metaphors (Dries, Doug Reckamp, Ethan)
*15.20: Paradigmatic Relations! (Yuka, Mark)

*15.50: Perception of Aesthetic Information (Mikaela, Ethan, Mark)
*16.00: Chaotic Brain (Pablo, Paula, Keith, Laura, David, Mikaela, Levi)
*16.10: HebbWeb (Pasha, Chiara, Levi, Xin, Billy)
*16.20: River networks (Gen, Brennan, Alec, Dan)
*16.30: How does resource availability and usage affect cooperation? (Anshuman, Dries, Marjorie, Ruggiero, Kirtus, Ian, Billy, Kunaal)
*16.40: Modelling the spatial diffusion of human language (Henri, Harun, Kenzie, Pablo Flores, Ritu)
*16.50:
*17.00:
*17.10:
*17.20:

Complex Systems Summer School 2019-Project Presentations

2019-06-29T05:11:25Z

Shruti:

{{Complex Systems Summer School 2019}}

'''TUESDAY'''

Morning

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

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

'''WEDNESDAY'''

Morning

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

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

Afternoon

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

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

Complex Systems Summer School 2019-Project check-ins

2019-06-26T18:12:12Z

Shruti:

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, Anshuman, Jessica)
* 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)
* 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: 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)
* 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)
* 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)
* 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:
* 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:
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* 16.00 - 16.10:
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'''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:
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* 16.20 - 16.30:

Complex Systems Summer School 2019-Project check-ins

2019-06-26T15:16:48Z

Shruti:

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, Anshuman, Jessica)
* 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)
* 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: 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)
* 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)
* 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)
* 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:
* 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:
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* 14.50 - 15.00:
*
* 15.40 - 15.50:
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* 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

2019-06-20T20:50:04Z

Shruti:

{{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
* David (I can also drive a third car if necessary)
* Pablo Flores

* Kazu (If I can rent a sleeping bag and there are still seats left)
* Ruggiero (would love to join with similar problems as Kazu)
* Chiara (same as above)
* Shruti (same as above)

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 
...

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-18T05:47:31Z

Shruti: /* Emergence of cooperative strategies by means of game warping, using network science */

{{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]]
 
Citations: 
* https://poseidon01.ssrn.com/delivery.php?ID=325026118089093124102093068082080010034050058012070082112080112071106003085090090099038035127124020121002005065018075109121122105060069010052127002094098103004021064093039078084024001019025078027004029068023080086068066082022108116118112010021093014094&EXT=pdf 
* https://medium.com/@virgilgr/ethereum-is-game-changing-technology-literally-d67e01a01cf8

===Research questions===
* What gaming strategies emerge when NxN intelligent agents interact in a system that allows game warping? How does the system evolve over time?
* Agents are able to make Bayesian decisions whose vectors adapt as more historical information becomes available
* How are sequential games played on a NxN level? Consider contingency trail using Level-K solution concept
* Simulate an artificial economy with adaptive agents

===Interested participants===
* Shruti
* Aabir
* Mikaela

===Slack===
Join #gamewarping channel.

==Mathematical formalization of cryptoeconomics==

(From Shruti)

Create the Maxwell's equations of cryptoeconomics. Cryptoeconomics is a very new field, alike economics but comes with special properties that traditional economic systems cannot achieve. It is fertile ground that requires a foundation (pun), developing fundamentals, and formalization. Non-exhaustive list of potential approaches: network science, adaptive agent-based simulations, systems modeling (bond graphs), hyperparametric optimization, control systems. I've been thinking about developing this foundation for a few months now, and I will express all these ideas in my SFI talk on Jun 21 - open invite to join the collective nerd out! This topic was also hotly discussed as being the next steps in moving the space of cryptonetworks forward, in today's CollectiveCrypto workshop @ SFI by Geoffrey West, Jessica Flack, David Krakauer, David Wolpert et al. We arrived at the conclusion that this type of research is highly valuable.

This is likely a mini-project, with an intent to publish a paper.

===Interested participants===
* Shruti
* Mikaela

==How might we quantify non-monetary value exchanges (like gift giving)?==

(From Shruti)

The current financial system doesn't incentivize corporations/individuals to take environmentally and socially positive actions (for the most part). There is no mechanism that accounts for non-monetary value exchanges in today's shareholder model of corporate governance. These exchanges range anywhere from simple gift-giving to a government agreeing to not dump their waste in Vietnam, India etc. Through this project, we want to explore generalizable means to account for currently unquantified (yet important) value exchanges. We're currently toying around with the idea of a human reputation market, we are aware it sounds dystopian and are open to ideas.

===Interested participants===
* Shruti
* Pavel
* Earnest

===Slack===
Join #moralmoniezzz

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

(From Kunaal)

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

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

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

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

==Modelling the spatial diffusion of human languages==

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

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

===Data===

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

===Papers to read===

* Let's add them here

===Interested participants===

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

===Future plans===

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

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

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

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

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

===Interested Participants===
Jessica Brumley

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

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

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

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

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

===Meeting Schedule & Notes===
TBA

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

==Science Policy & Communication==

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

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

===Communication Channels===

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

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

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

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

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

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

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

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

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

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

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

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

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

== Analyzing Collaboration Throughout CSSS History ==

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

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

== Multi-scale inequalities and cities ==

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

=== Participants ===

* Bhartendu Pandey
* Christopher Quarles
* Alec Kirkley
* Luther Seet

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

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

=== Participants ===

* Dakota Murray

==Artificial fossilization of animal interaction networks==

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

=== Participants ===

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

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

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

=== Participants ===

* Harun Siljak

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

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

=== Participants ===

* Harun Siljak

== CSSS Social Network Study ==

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

=== Participants ===

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

==Self organizing city==

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

How do do public spaces and active functions of the city influence the flow of people?

Slack Channel: '''#selforganizing-city'''

=== Participants ===

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

==Too Much Information and Segregation==

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

=== Participants ===

* Christopher Quarles
* Wenqian Yin
* Jordi Piñero

==Scrutinizing Early Warning Signals for Depression==
Historically, depression has been understood within a 'common cause' framework in which the associations between symptoms such as worry, sadness, and lack of sleep is due to an underlying latent variable. This is an extremely successful approach in medicine, where symptoms usually are due to some underlying biological disease. In psychology, this has been less successful. An emerging 'network perspective' instead abandons the assumption of an underlying common cause and views depression as arising out of symptoms that directly influence each other, that is, as a complex system. There has been some work on 'early warning signals' which indicate 'tipping points', i.e., transitions to an alternative stable state. This project aims to extend and critically evaluate how these approaches have been applied to predict the onset and termination of depression.

=== Participants ===

* Fabian
* Toni
* Andrea
* Arta

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-18T05:46:21Z

Shruti: /* Emergence of cooperative strategies by means of game warping, using network science */

{{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]]
 
Citations: 
* https://poseidon01.ssrn.com/delivery.php?ID=325026118089093124102093068082080010034050058012070082112080112071106003085090090099038035127124020121002005065018075109121122105060069010052127002094098103004021064093039078084024001019025078027004029068023080086068066082022108116118112010021093014094&EXT=pdf 
* https://medium.com/@virgilgr/ethereum-is-game-changing-technology-literally-d67e01a01cf8

===Interested participants===
* Shruti
* Aabir
* Mikaela

===Slack===
Join #gamewarping channel.

==Mathematical formalization of cryptoeconomics==

(From Shruti)

Create the Maxwell's equations of cryptoeconomics. Cryptoeconomics is a very new field, alike economics but comes with special properties that traditional economic systems cannot achieve. It is fertile ground that requires a foundation (pun), developing fundamentals, and formalization. Non-exhaustive list of potential approaches: network science, adaptive agent-based simulations, systems modeling (bond graphs), hyperparametric optimization, control systems. I've been thinking about developing this foundation for a few months now, and I will express all these ideas in my SFI talk on Jun 21 - open invite to join the collective nerd out! This topic was also hotly discussed as being the next steps in moving the space of cryptonetworks forward, in today's CollectiveCrypto workshop @ SFI by Geoffrey West, Jessica Flack, David Krakauer, David Wolpert et al. We arrived at the conclusion that this type of research is highly valuable.

This is likely a mini-project, with an intent to publish a paper.

===Interested participants===
* Shruti
* Mikaela

==How might we quantify non-monetary value exchanges (like gift giving)?==

(From Shruti)

The current financial system doesn't incentivize corporations/individuals to take environmentally and socially positive actions (for the most part). There is no mechanism that accounts for non-monetary value exchanges in today's shareholder model of corporate governance. These exchanges range anywhere from simple gift-giving to a government agreeing to not dump their waste in Vietnam, India etc. Through this project, we want to explore generalizable means to account for currently unquantified (yet important) value exchanges. We're currently toying around with the idea of a human reputation market, we are aware it sounds dystopian and are open to ideas.

===Interested participants===
* Shruti
* Pavel
* Earnest

===Slack===
Join #moralmoniezzz

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

(From Kunaal)

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

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

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

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

==Modelling the spatial diffusion of human languages==

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

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

===Data===

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

===Papers to read===

* Let's add them here

===Interested participants===

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

===Future plans===

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

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

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

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

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

===Interested Participants===
Jessica Brumley

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

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

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

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

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

===Meeting Schedule & Notes===
TBA

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

==Science Policy & Communication==

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

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

===Communication Channels===

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

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

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

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

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

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

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

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

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

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

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

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

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

== Analyzing Collaboration Throughout CSSS History ==

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

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

== Multi-scale inequalities and cities ==

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

=== Participants ===

* Bhartendu Pandey
* Christopher Quarles
* Alec Kirkley
* Luther Seet

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

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

=== Participants ===

* Dakota Murray

==Artificial fossilization of animal interaction networks==

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

=== Participants ===

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

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

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

=== Participants ===

* Harun Siljak

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

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

=== Participants ===

* Harun Siljak

== CSSS Social Network Study ==

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

=== Participants ===

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

==Self organizing city==

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

How do do public spaces and active functions of the city influence the flow of people?

Slack Channel: '''#selforganizing-city'''

=== Participants ===

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

==Too Much Information and Segregation==

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

=== Participants ===

* Christopher Quarles
* Wenqian Yin
* Jordi Piñero

==Scrutinizing Early Warning Signals for Depression==
Historically, depression has been understood within a 'common cause' framework in which the associations between symptoms such as worry, sadness, and lack of sleep is due to an underlying latent variable. This is an extremely successful approach in medicine, where symptoms usually are due to some underlying biological disease. In psychology, this has been less successful. An emerging 'network perspective' instead abandons the assumption of an underlying common cause and views depression as arising out of symptoms that directly influence each other, that is, as a complex system. There has been some work on 'early warning signals' which indicate 'tipping points', i.e., transitions to an alternative stable state. This project aims to extend and critically evaluate how these approaches have been applied to predict the onset and termination of depression.

=== Participants ===

* Fabian
* Toni
* Andrea
* Arta

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-18T05:43:45Z

Shruti: /* Mathematical formalization of cryptoeconomics */

{{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

===Interested participants===
* Shruti
* Aabir
* Mikaela

===Slack===
Join #gamewarping channel.

==Mathematical formalization of cryptoeconomics==

(From Shruti)

Create the Maxwell's equations of cryptoeconomics. Cryptoeconomics is a very new field, alike economics but comes with special properties that traditional economic systems cannot achieve. It is fertile ground that requires a foundation (pun), developing fundamentals, and formalization. Non-exhaustive list of potential approaches: network science, adaptive agent-based simulations, systems modeling (bond graphs), hyperparametric optimization, control systems. I've been thinking about developing this foundation for a few months now, and I will express all these ideas in my SFI talk on Jun 21 - open invite to join the collective nerd out! This topic was also hotly discussed as being the next steps in moving the space of cryptonetworks forward, in today's CollectiveCrypto workshop @ SFI by Geoffrey West, Jessica Flack, David Krakauer, David Wolpert et al. We arrived at the conclusion that this type of research is highly valuable.

This is likely a mini-project, with an intent to publish a paper.

===Interested participants===
* Shruti
* Mikaela

==How might we quantify non-monetary value exchanges (like gift giving)?==

(From Shruti)

The current financial system doesn't incentivize corporations/individuals to take environmentally and socially positive actions (for the most part). There is no mechanism that accounts for non-monetary value exchanges in today's shareholder model of corporate governance. These exchanges range anywhere from simple gift-giving to a government agreeing to not dump their waste in Vietnam, India etc. Through this project, we want to explore generalizable means to account for currently unquantified (yet important) value exchanges. We're currently toying around with the idea of a human reputation market, we are aware it sounds dystopian and are open to ideas.

===Interested participants===
* Shruti
* Pavel
* Earnest

===Slack===
Join #moralmoniezzz

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

(From Kunaal)

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

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

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

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

==Modelling the spatial diffusion of human languages==

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

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

===Data===

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

===Papers to read===

* Let's add them here

===Interested participants===

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

===Future plans===

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

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

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

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

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

===Interested Participants===
Jessica Brumley

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

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

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

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

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

===Meeting Schedule & Notes===
TBA

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

==Science Policy & Communication==

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

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

===Communication Channels===

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

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

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

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

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

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

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

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

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

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

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

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

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

== Analyzing Collaboration Throughout CSSS History ==

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

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

== Multi-scale inequalities and cities ==

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

=== Participants ===

* Bhartendu Pandey
* Christopher Quarles
* Alec Kirkley
* Luther Seet

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

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

=== Participants ===

* Dakota Murray

==Artificial fossilization of animal interaction networks==

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

=== Participants ===

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

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

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

=== Participants ===

* Harun Siljak

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

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

=== Participants ===

* Harun Siljak

== CSSS Social Network Study ==

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

=== Participants ===

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

==Self organizing city==

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

How do do public spaces and active functions of the city influence the flow of people?

Slack Channel: '''#selforganizing-city'''

=== Participants ===

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

==Too Much Information and Segregation==

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

=== Participants ===

* Christopher Quarles
* Wenqian Yin
* Jordi Piñero

==Scrutinizing Early Warning Signals for Depression==
Historically, depression has been understood within a 'common cause' framework in which the associations between symptoms such as worry, sadness, and lack of sleep is due to an underlying latent variable. This is an extremely successful approach in medicine, where symptoms usually are due to some underlying biological disease. In psychology, this has been less successful. An emerging 'network perspective' instead abandons the assumption of an underlying common cause and views depression as arising out of symptoms that directly influence each other, that is, as a complex system. There has been some work on 'early warning signals' which indicate 'tipping points', i.e., transitions to an alternative stable state. This project aims to extend and critically evaluate how these approaches have been applied to predict the onset and termination of depression.

=== Participants ===

* Fabian
* Toni
* Andrea
* Arta

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-18T05:40:58Z

Shruti:

{{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

===Interested participants===
* Shruti
* Aabir
* Mikaela

===Slack===
Join #gamewarping channel.

==Mathematical formalization of cryptoeconomics==

(From Shruti)

Create the Maxwell's equations of cryptoeconomics. Cryptoeconomics is a very new field, alike economics but comes with special properties that traditional economic systems cannot achieve. It is fertile ground that requires a foundation (pun), developing fundamentals, and formalization. Non-exhaustive list of potential approaches: network science, adaptive agent-based simulations, systems modeling, hyperparametric optimization, control systems. I've been thinking about developing this foundation for a few months now, and I will express all these ideas in my SFI talk on Jun 21 - open invite to join the collective nerd out! This topic was also hotly discussed as being the next steps in moving the space of cryptonetworks forward, in today's CollectiveCrypto workshop @ SFI by Geoffrey West, Jessica Flack, David Krakauer, David Wolpert et al. We arrived at the conclusion that this type of research is highly valuable.

This is likely a mini-project, with an intent to publish a paper.

===Interested participants===
* Shruti
* Mikaela

==How might we quantify non-monetary value exchanges (like gift giving)?==

(From Shruti)

The current financial system doesn't incentivize corporations/individuals to take environmentally and socially positive actions (for the most part). There is no mechanism that accounts for non-monetary value exchanges in today's shareholder model of corporate governance. These exchanges range anywhere from simple gift-giving to a government agreeing to not dump their waste in Vietnam, India etc. Through this project, we want to explore generalizable means to account for currently unquantified (yet important) value exchanges. We're currently toying around with the idea of a human reputation market, we are aware it sounds dystopian and are open to ideas.

===Interested participants===
* Shruti
* Pavel
* Earnest

===Slack===
Join #moralmoniezzz

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

(From Kunaal)

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

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

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

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

==Modelling the spatial diffusion of human languages==

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

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

===Data===

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

===Papers to read===

* Let's add them here

===Interested participants===

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

===Future plans===

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

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

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

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

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

===Interested Participants===
Jessica Brumley

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

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

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

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

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

===Meeting Schedule & Notes===
TBA

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

==Science Policy & Communication==

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

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

===Communication Channels===

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

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

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

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

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

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

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

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

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

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

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

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

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

== Analyzing Collaboration Throughout CSSS History ==

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

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

== Multi-scale inequalities and cities ==

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

=== Participants ===

* Bhartendu Pandey
* Christopher Quarles
* Alec Kirkley
* Luther Seet

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

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

=== Participants ===

* Dakota Murray

==Artificial fossilization of animal interaction networks==

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

=== Participants ===

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

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

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

=== Participants ===

* Harun Siljak

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

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

=== Participants ===

* Harun Siljak

== CSSS Social Network Study ==

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

=== Participants ===

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

==Self organizing city==

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

How do do public spaces and active functions of the city influence the flow of people?

Slack Channel: '''#selforganizing-city'''

=== Participants ===

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

==Too Much Information and Segregation==

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

=== Participants ===

* Christopher Quarles
* Wenqian Yin
* Jordi Piñero

==Scrutinizing Early Warning Signals for Depression==
Historically, depression has been understood within a 'common cause' framework in which the associations between symptoms such as worry, sadness, and lack of sleep is due to an underlying latent variable. This is an extremely successful approach in medicine, where symptoms usually are due to some underlying biological disease. In psychology, this has been less successful. An emerging 'network perspective' instead abandons the assumption of an underlying common cause and views depression as arising out of symptoms that directly influence each other, that is, as a complex system. There has been some work on 'early warning signals' which indicate 'tipping points', i.e., transitions to an alternative stable state. This project aims to extend and critically evaluate how these approaches have been applied to predict the onset and termination of depression.

=== Participants ===

* Fabian
* Toni
* Andrea
* Arta

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-18T05:39:36Z

Shruti:

{{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

'''Interested participants'''
* Shruti
* Aabir
* Mikaela

'''Slack'''
Join #gamewarping channel.

==Mathematical formalization of cryptoeconomics==

(From Shruti)

Create the Maxwell's equations of cryptoeconomics. Cryptoeconomics is a very new field, alike economics but comes with special properties that traditional economic systems cannot achieve. It is fertile ground that requires a foundation (pun), developing fundamentals, and formalization. Non-exhaustive list of potential approaches: network science, adaptive agent-based simulations, systems modeling, hyperparametric optimization, control systems. I've been thinking about developing this foundation for a few months now, and I will express all these ideas in my SFI talk on Jun 21 - open invite to join the collective nerd out! This topic was also hotly discussed as being the next steps in moving the space of cryptonetworks forward, in today's CollectiveCrypto workshop @ SFI by Geoffrey West, Jessica Flack, David Krakauer, David Wolpert et al. We arrived at the conclusion that this type of research is highly valuable.

This is likely a mini-project, with an intent to publish a paper.

'''Interested participants'''
* Shruti
* Mikaela

==How might we quantify non-monetary value exchanges (like gift giving)?==

(From Shruti)

The current financial system doesn't incentivize corporations/individuals to take environmentally and socially positive actions (for the most part). There is no mechanism that accounts for non-monetary value exchanges in today's shareholder model of corporate governance. These exchanges range anywhere from simple gift-giving to a government agreeing to not dump their waste in Vietnam, India etc. Through this project, we want to explore generalizable means to account for currently unquantified (yet important) value exchanges. We're currently toying around with the idea of a human reputation market, we are aware it sounds dystopian and are open to ideas.

'''Interested participants'''
* Shruti
* Pavel
* Earnest

'''Slack'''
Join #moralmoniezzz

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

(From Kunaal)

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

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

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

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

==Modelling the spatial diffusion of human languages==

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

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

===Data===

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

===Papers to read===

* Let's add them here

===Interested participants===

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

===Future plans===

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

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

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

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

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

===Interested Participants===
Jessica Brumley

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

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

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

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

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

===Meeting Schedule & Notes===
TBA

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

==Science Policy & Communication==

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

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

===Communication Channels===

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

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

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

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

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

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

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

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

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

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

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

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

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

== Analyzing Collaboration Throughout CSSS History ==

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

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

== Multi-scale inequalities and cities ==

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

=== Participants ===

* Bhartendu Pandey
* Christopher Quarles
* Alec Kirkley
* Luther Seet

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

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

=== Participants ===

* Dakota Murray

==Artificial fossilization of animal interaction networks==

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

=== Participants ===

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

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

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

=== Participants ===

* Harun Siljak

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

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

=== Participants ===

* Harun Siljak

== CSSS Social Network Study ==

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

=== Participants ===

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

==Self organizing city==

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

How do do public spaces and active functions of the city influence the flow of people?

Slack Channel: '''#selforganizing-city'''

=== Participants ===

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

==Too Much Information and Segregation==

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

=== Participants ===

* Christopher Quarles
* Wenqian Yin
* Jordi Piñero

==Scrutinizing Early Warning Signals for Depression==
Historically, depression has been understood within a 'common cause' framework in which the associations between symptoms such as worry, sadness, and lack of sleep is due to an underlying latent variable. This is an extremely successful approach in medicine, where symptoms usually are due to some underlying biological disease. In psychology, this has been less successful. An emerging 'network perspective' instead abandons the assumption of an underlying common cause and views depression as arising out of symptoms that directly influence each other, that is, as a complex system. There has been some work on 'early warning signals' which indicate 'tipping points', i.e., transitions to an alternative stable state. This project aims to extend and critically evaluate how these approaches have been applied to predict the onset and termination of depression.

=== Participants ===

* Fabian
* Toni
* Andrea
* Arta

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-18T05:37:23Z

Shruti:

{{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

===Interested participants===
1. Shruti
2. Aabir
3. Mikaela

===Slack===
Join #gamewarping channel.

==Mathematical formalization of cryptoeconomics==

(From Shruti)

Create the Maxwell's equations of cryptoeconomics. Cryptoeconomics is a very new field, alike economics but comes with special properties that traditional economic systems cannot achieve. It is fertile ground that requires a foundation (pun), developing fundamentals, and formalization. Non-exhaustive list of potential approaches: network science, adaptive agent-based simulations, systems modeling, hyperparametric optimization, control systems. I've been thinking about developing this foundation for a few months now, and I will express all these ideas in my SFI talk on Jun 21 - open invite to join the collective nerd out! This topic was also hotly discussed as being the next steps in moving the space of cryptonetworks forward, in today's CollectiveCrypto workshop @ SFI by Geoffrey West, Jessica Flack, David Krakauer, David Wolpert et al. We arrived at the conclusion that this type of research is highly valuable.

This is likely a mini-project, with an intent to publish a paper.

===Interested participants===
1. Shruti
3. Mikaela

==How might we quantify non-monetary value exchanges (like gift giving)?==

(From Shruti)

The current financial system doesn't incentivize corporations/individuals to take environmentally and socially positive actions (for the most part). There is no mechanism that accounts for non-monetary value exchanges in today's shareholder model of corporate governance. These exchanges range anywhere from simple gift-giving to a government agreeing to not dump their waste in Vietnam, India etc. Through this project, we want to explore generalizable means to account for currently unquantified (yet important) value exhanges. We're currently toying around with the idea of a human reputation market, we are aware it sounds dystopian and are open to ideas.

===Interested participants===
1. Shruti
2. Pavel
3. Earnest

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

(From Kunaal)

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

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

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

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

==Modelling the spatial diffusion of human languages==

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

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

===Data===

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

===Papers to read===

* Let's add them here

===Interested participants===

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

===Future plans===

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

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

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

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

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

===Interested Participants===
Jessica Brumley

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

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

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

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

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

===Meeting Schedule & Notes===
TBA

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

==Science Policy & Communication==

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

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

===Communication Channels===

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

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

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

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

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

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

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

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

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

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

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

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

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

== Analyzing Collaboration Throughout CSSS History ==

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

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

== Multi-scale inequalities and cities ==

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

=== Participants ===

* Bhartendu Pandey
* Christopher Quarles
* Alec Kirkley
* Luther Seet

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

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

=== Participants ===

* Dakota Murray

==Artificial fossilization of animal interaction networks==

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

=== Participants ===

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

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

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

=== Participants ===

* Harun Siljak

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

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

=== Participants ===

* Harun Siljak

== CSSS Social Network Study ==

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

=== Participants ===

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

==Self organizing city==

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

How do do public spaces and active functions of the city influence the flow of people?

Slack Channel: '''#selforganizing-city'''

=== Participants ===

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

==Too Much Information and Segregation==

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

=== Participants ===

* Christopher Quarles
* Wenqian Yin
* Jordi Piñero

==Scrutinizing Early Warning Signals for Depression==
Historically, depression has been understood within a 'common cause' framework in which the associations between symptoms such as worry, sadness, and lack of sleep is due to an underlying latent variable. This is an extremely successful approach in medicine, where symptoms usually are due to some underlying biological disease. In psychology, this has been less successful. An emerging 'network perspective' instead abandons the assumption of an underlying common cause and views depression as arising out of symptoms that directly influence each other, that is, as a complex system. There has been some work on 'early warning signals' which indicate 'tipping points', i.e., transitions to an alternative stable state. This project aims to extend and critically evaluate how these approaches have been applied to predict the onset and termination of depression.

=== Participants ===

* Fabian
* Toni
* Andrea
* Arta

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-13T07:00:20Z

Shruti:

{{Complex Systems Summer School 2019}}

Project and working group ideas go here.

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.

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.

From Shruti:
'''Emergence of cooperative strategies by means of ''game warping'', using network science'''

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

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-13T06:57:36Z

Shruti:

{{Complex Systems Summer School 2019}}

Project and working group ideas go here.

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.

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.

From Shruti:
'''Emergence of cooperative strategies by means of ''game warping'', using network science'''

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. David Wolpert's (SFI) work on "game mining" is 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

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-13T06:56:49Z

Shruti:

{{Complex Systems Summer School 2019}}

Project and working group ideas go here.

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.

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.

From Shruti:
'''Emergence of cooperative strategies by means of ''game warping'', using network science'''

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. David Wolpert's (SFI) work on "game mining" is relevant. <ref>https://www.santafe.edu/news-center/news/wolpert-aaec-game-mining</ref>
[[File:Game warping .png]]

Complex Systems Summer School 2019-Projects & Working Groups

2019-06-13T06:56:01Z

Shruti:

{{Complex Systems Summer School 2019}}

Project and working group ideas go here.

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.

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.

From Shruti:
'''Emergence of cooperative strategies by means of ''game warping'', using network science'''

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. David Wolpert's (SFI) work on "game mining" is relevant. <ref>https://www.santafe.edu/news-center/news/wolpert-aaec-game-mining</ref>
[[File:Game warping .png|thumb]]

File:Game warping .png

2019-06-13T06:54:28Z

Shruti:

Citation: https://medium.com/@virgilgr/ethereum-is-game-changing-technology-literally-d67e01a01cf8

Complex Systems Summer School 2019-After Hours

2019-06-11T00:11:21Z

Shruti:

{{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. Shruti 
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