Santa Fe Institute Events Wiki - User contributions [en]

Presentations 2012

2012-06-27T18:41:12Z

GHaslam:

{{Complex Systems Summer School 2012}}

9:00 - 9:15: Christa Brelsford and Xin Lu: Changes in Social Network Structure in Response to Crisis: Using Twitter data to Explore the Effect of the Tōhoku Earthquake.

9:15 - 9:30: Piotr Milanowski and Georg F. Weber: Enzyme kinetics and the outcome of chemical reactions.

9:30 - 9:45: Fabio, Elena, Tom and Friederike: Collaboration in times of stress: an Agent Based Modelling approach

9:45 - 10:00: Joanne, Vikram, Matteo, Sanith: Price-time Dynamics of Contracts Traded on Prediction Markets

10:15 - 10:45: BREAK

10:45 - 11:00: Katrien, Jasmeen, Sandro, Cameron, Vanessa

11:15 - 11:30: Xue, Χλοε, Xiaoli

12:00 - 1:15: LUNCH

1:15 - 1:30: Andres, Charlie, Gareth, and Nic G: We Got the Skills to Pay the Bills - Exploring the Link Between Occupation Diversity and Innovation

1:30 - 1:45: Xin and Abby

1:45 - 2:00: Sepehr

5:00: Final Remarks & Farewell Dinner

Presentations 2012

2012-06-27T18:36:10Z

GHaslam:

{{Complex Systems Summer School 2012}}

9:00 - 9:15: Christa Brelsford and Xin Lu: Changes in Social Network Structure in Response to Crisis: Using Twitter data to Explore the Effect of the Tōhoku Earthquake.

9:15 - 9:30: Piotr Milanowski and Georg F. Weber: Enzyme kinetics and the outcome of chemical reactions.

9:30 - 9:45: Fabio, Elena, Tom and Friederike: Collaboration in times of stress: an Agent Based Modelling approach

9:45 - 10:00: Joanne, Vikram, Matteo, Sanith: Price-time Dynamics of Contracts Traded on Prediction Markets

10:15 - 10:45: BREAK

10:45 - 11:00: Katrien, Jasmeen, Sandro, Cameron, Vanessa

11:15 - 11:30: Xue, Χλοε, Xiaoli

12:00 - 1:15: LUNCH

1:15 - 1:30: Andres, Charlie, Gareth, and Nic G

1:30 - 1:45: Xin and Abby

5:00: Final Remarks & Farewell Dinner

Complex Systems Summer School 2012-Project Presentations

2012-06-27T05:51:56Z

GHaslam:

{{Complex Systems Summer School 2012}}

Use this space to post project presentations and outlines. Include group members, a brief outline, and your slides.

=== Price-time Dynamics of Contracts Traded on Prediction Markets ===

Joanne, Vikram, Matteo, Sanith

Prediction markets have been shown to outperform traditional methods of polls and opinion surveys in forecasting future events. The futures contracts traded in these markets assess the expectation of occurrence of a variety of events spread across multiple domains (political, economic, entertainment, financial and weather). We explore the feasibility of 'predicting' the outcome of binary true/false prediction market contracts ahead of their expiry date using a neural-network based machine learning approach. In addition we focus on the characteristics of political-based contracts to establish whether they exhibit characteristic 'fundamental' properties.

=== How Complex Languages Replicate through Simple Brains ===

Katrien, Vanessa, Sandro, Cameron, Jasmeen

Through the use of an iterated learning experiment, we investigated the transmission of a "high entropy", randomised initial language through successive generations of participants. We want to see what features of the language replicated most easily, and what structure emerged by the end of the chain. Our hypothesis is that the language converges to a "low entropy" equilibrium state with a minimal number of words, morphemes, and form-meaning distinctions.

=== Collaboration in times of stress: an Agent Based Modelling approach ===

Fabio Cresto Aleina, Elena del Val, Tom Fennewald and Friederike Greb

We want to investigate the influence of exogenous stress on cooperative behaviour. We propose an agent based model in which the agents can be interpreted as farmers living in a water limited environment. With changes in precipitation patterns, the farmers undergo stress, and we observe how this impacts relationships among farmers and their properties.

=== Simple variation of the logistic map as a model to invoke questions on cellular protein trafficking ===
(Sepehr Ehsani, http://arxiv.org/abs/1206.5557)

Many open problems in biology, as in the physical sciences, display nonlinear and 'chaotic' dynamics, which, to the extent possible, cannot be reasonably understood. Moreover, mathematical models which aim to predict/estimate unknown aspects of a biological system cannot provide more information about the set of biologically meaningful (e.g., 'hidden') states of the system than could be understood by the designer of the model ab initio. Here, the case is made for the utilization of such models to shift from a 'predictive' to a 'questioning' nature, and a simple natural-logarithm variation of the logistic polynomial map is presented that can invoke questions about protein trafficking in eukaryotic cells.

===Changes in Social Network Structure in Response to Crisis: Using Twitter data to Explore the Effect of the Tōhoku Earthquake.===

Christa Brelsford and Xin Lu

Abstract:
We use twitter data from 7 days before and after the Tōhoku Earthquake to explore how cooperation rates, social network structure and connectivity, and social network vulnerability changed in Japan in response to the disaster. An English language data set is collected for the same time period to use as a control. Data is collected for a period of 96 hours starting from March 4th 2011 2:46pm JST and for 96 hours beginning March 11th 2011 2:46 pm JST. The rate of cooperative behavior, measured by the occurrence of helping words in tweets increases slightly in the English dataset and by an order of magnitude in the Japanese dataset. A network analysis is also performed. Network edges are retweets and direct messages. In future work, we hope to explore whether problem solving capacity in a social system changes in response to crises, based on changes in the rate of cooperation and information transfer in a network.

=== The CSSS Network ===

Tom & Riccardo (with JP and others)

We will investigate the questions you are dying to know: What interesting interactions are revealed from the first 3 weeks of the Complex System Summer School survey? Have barriers between academic disciplines been broken down? Do power laws fit the data!? ...

Let us know if you have specific questions or if you would like to be involved in data analysis!

=== Is there a method in the madness? the dynamic structures of human language use ===

Priya and Riccardo

Psychiatric anecdotal reports point to the monotony, lack of emotion and sometimes intelligibility in many clinical populations. Linear measures of fluency and prosody, however, present only controversial differences between patients and healthy controls and only in unnatural phonations (i.e. say "aaaaa" for 30 secs).
We therefore go complex and chaotic on a set of more ecological recordings and transcriptions from 4 clinical populations (Asperger's, Schizophrenics, Depressed and Right Hemisphere Damage patients) as well as from healthy controls.
We then set a classifier-driven race: will non-linear analyses outcompete linear analyses in discriminating between pathologies?

=== Escaping the Poverty Trap: Modeling the Interplay Between Economic Growth and the Ecology of Infectious Disease ===

Georg, Ben, Laurent, Oscar

The dynamics of economies and infectious disease are inexorably linked: economic well-being influences health (sanitation, nutrition, etc) and health influences economic well-being (labor productivity lost to sickness and disease). Often societies are locked into "poverty traps" of poor health and poor economy. Here, we demonstrate poverty traps formed in models of infection and endogenous growth, as well as ways to break out of poverty traps. We explore two mechanisms of escape: one, through an influx of capital, and another through changing the percentage of GDP spent on healthcare. We find large influxes of capital is successful, but increasing health spending does not. Our results have important policy implications in the distribution of aid and within-country healthcare spending.

=== The Targeting and Timing of Treatment Influences the Emergence of Influenza Resistance in Structured Populations ===

Ben, Laurent, Oscar, Georg

Evolution of antiviral resistance in influenza carries large societal impacts through morbidity and mortality caused by treatment failure. Several previous studies put forth theory regarding the optimal timing, targeting and absolute level of treatment in populations. Few of these studies, however, have considered populations with explicit structure. Here we present a model of antiviral resistance on networks and explore the timing, targeting and levels of treatment. Interestingly, we find bistability as a result of treatment leading to the existence of an unstable manifold, above which successful treatment (i.e.: no resistance) is impossible. We find, contrary to previous results, that degree-targeted treatment is not optimal, and leads to higher levels of resistance than random treatment. Additionally, in accordance with previous results, we find an optimum level of treatment which is less than 100%. These findings findings have important consequences in guiding policy behind influenza treatment. The bistability indicates that caution should be taken when treating populations when the absolute numbers of infections are unknown. Positively, our results indicate that putting resources into targeted treatment is not necessary, random treatment is preferable.

=== Learning in Random Boolean Networks ===

Nick A., Keegan, Matteo, Vikram, Sarah, Mark

Inspired by biochemical networks which adapt on evolutionary timescales, neural networks that adapt during development and learning, and universal computation in cellular automata, we have implemented several models of learning in Random Boolean Networks (RBNs) in order to better understand the relationships between network structure, node interaction rules, and network output.

=== Enzyme Catalysis and the Outcome of Chemical Reactions ===
Piotr and Georg W.
Enzymes are catalysts that accelerate chemical reactions but do not affect their outcome. This traditional paradism was developed under artificail test tube conditions. Our project investigates the possibility that the presence of an enzyme can alter the course of a reaction if it takes place under more physiologic conditions.

=== How Does a Stochastic Environment affect Community Assembly? ===

Xue, Χλοε, Xiaoli

We are interested in how exogenous temporal variability in resource availability affects the community structure of organisms with different resource-use strategies. Organisms induce additional resource stress on each other through competition. This is an abstraction of an arid environment with unreliable rainfall; the organisms themselves have been abstracted to four unitless parameters that allocate their resources to different parts of their lifecycles. The system has memory, as the previous presence of an organism affects the resource transport mechanism (an abstraction of soil).

=== How Does a Network’s Structure Influence its Traceability? ===

Xin and Abby

We are interested in systematically studying the problem of finding the source of a contamination spread through a network. We model a contamination spreading through the food distribution network, which we represent by interconnections between farmers, distributors, and retailers, and construct an estimator for the outbreak source given only this structure. We show how the ability of the estimator to narrow down the source identification problem changes with the connectivity and the number of observations. We propose a measure for traceability, or the overall ability to identify the source of spreading given any set of outbreak observations, based on entropy. We show how this measure appropriately reflects the range of uncertainty in identifying the source. We believe this measure may be useful in the design of networks that are conducive to accurate identification of the source of contamination.

=== We Got the Skills to Pay the Bills: Exploring the Link Between Occupation Diversity and Innovation===

Andrés, Charlie, Gareth, and Nick

Where does diversity in skills or occupations come from and why does it lead to more innovative cities? Previous work in this area has shown that there is a scaling behaviour which allows citizens of larger cities to earn an extra 15% in income whilst making use of 15% fewer gas stations, for example. Making use of occupation, patent, and population data of US Metropolitan Statistical Areas (MSA), we try to understand what factors make successful cities. Here we assume that successful cities are those cities which are most innovative as determined by the production of patents. In addition we use agent-based modelling to explore how and why people acquire new skills and whether this leads to more productive cities.

Complex Systems Summer School 2012-Project Presentations

2012-06-27T03:10:32Z

GHaslam: /* We Got the Skills to Pay the Bills */

{{Complex Systems Summer School 2012}}

Use this space to post project presentations and outlines. Include group members, a brief outline, and your slides.

=== Price-time Dynamics of Contracts Traded on Prediction Markets ===

Joanne, Vikram, Matteo, Sanith

Prediction markets have been shown to outperform traditional methods of polls and opinion surveys in forecasting future events. The futures contracts traded in these markets assess the expectation of occurrence of a variety of events spread across multiple domains (political, economic, entertainment, financial and weather). We explore the feasibility of 'predicting' the outcome of binary true/false prediction market contracts ahead of their expiry date using a neural-network based machine learning approach. In addition we focus on the characteristics of political-based contracts to establish whether they exhibit characteristic 'fundamental' properties.

=== How Complex Languages Replicate through Simple Brains ===

Katrien, Vanessa, Sandro, Cameron, Jasmeen

Through the use of an iterated learning experiment, we investigated the transmission of a "high entropy", randomised initial language through successive generations of participants. We want to see what features of the language replicated most easily, and what structure emerged by the end of the chain. Our hypothesis is that the language converges to a "low entropy" equilibrium state with a minimal number of words, morphemes, and form-meaning distinctions.

=== Collaboration in times of stress: an Agent Based Modelling approach ===

Fabio Cresto Aleina, Elena del Val, Tom Fennewald and Friederike Greb

We want to investigate the influence of exogenous stress on cooperative behaviour. We propose an agent based model in which the agents can be interpreted as farmers living in a water limited environment. With changes in precipitation patterns, the farmers undergo stress, and we observe how this impacts relationships among farmers and their properties.

=== Simple variation of the logistic map as a model to invoke questions on cellular protein trafficking ===
(Sepehr Ehsani, http://arxiv.org/abs/1206.5557)

Many open problems in biology, as in the physical sciences, display nonlinear and 'chaotic' dynamics, which, to the extent possible, cannot be reasonably understood. Moreover, mathematical models which aim to predict/estimate unknown aspects of a biological system cannot provide more information about the set of biologically meaningful (e.g., 'hidden') states of the system than could be understood by the designer of the model ab initio. Here, the case is made for the utilization of such models to shift from a 'predictive' to a 'questioning' nature, and a simple natural-logarithm variation of the logistic polynomial map is presented that can invoke questions about protein trafficking in eukaryotic cells.

===Changes in Social Network Structure in Response to Crisis: Using Twitter data to Explore the Effect of the Tōhoku Earthquake.===

Christa Brelsford and Xin Lu

Abstract:
We use twitter data from 7 days before and after the Tōhoku Earthquake to explore how cooperation rates, social network structure and connectivity, and social network vulnerability changed in Japan in response to the disaster. An English language data set is collected for the same time period to use as a control. Data is collected for a period of 96 hours starting from March 4th 2011 2:46pm JST and for 96 hours beginning March 11th 2011 2:46 pm JST. The rate of cooperative behavior, measured by the occurrence of helping words in tweets increases slightly in the English dataset and by an order of magnitude in the Japanese dataset. A network analysis is also performed. Network edges are retweets and direct messages. In future work, we hope to explore whether problem solving capacity in a social system changes in response to crises, based on changes in the rate of cooperation and information transfer in a network.

=== The CSSS Network ===

Tom & Riccardo (with JP and others)

We will investigate the questions you are dying to know: What interesting interactions are revealed from the first 3 weeks of the Complex System Summer School survey? Have barriers between academic disciplines been broken down? Do power laws fit the data!? ...

Let us know if you have specific questions or if you would like to be involved in data analysis!

=== Is there a method in the madness? the dynamic structures of human language use ===

Priya and Riccardo

Psychiatric anecdotal reports point to the monotony, lack of emotion and sometimes intelligibility in many clinical populations. Linear measures of fluency and prosody, however, present only controversial differences between patients and healthy controls and only in unnatural phonations (i.e. say "aaaaa" for 30 secs).
We therefore go complex and chaotic on a set of more ecological recordings and transcriptions from 4 clinical populations (Asperger's, Schizophrenics, Depressed and Right Hemisphere Damage patients) as well as from healthy controls.
We then set a classifier-driven race: will non-linear analyses outcompete linear analyses in discriminating between pathologies?

=== Escaping the Poverty Trap: Modeling the Interplay Between Economic Growth and the Ecology of Infectious Disease ===

Georg, Ben, Laurent, Oscar

The dynamics of economies and infectious disease are inexorably linked: economic well-being influences health (sanitation, nutrition, etc) and health influences economic well-being (labor productivity lost to sickness and disease). Often societies are locked into "poverty traps" of poor health and poor economy. Here, we demonstrate poverty traps formed in models of infection and endogenous growth, as well as ways to break out of poverty traps. We explore two mechanisms of escape: one, through an influx of capital, and another through changing the percentage of GDP spent on healthcare. We find large influxes of capital is successful, but increasing health spending does not. Our results have important policy implications in the distribution of aid and within-country healthcare spending.

=== The Targeting and Timing of Treatment Influences the Emergence of Influenza Resistance in Structured Populations ===

Ben, Laurent, Oscar, Georg

Evolution of antiviral resistance in influenza carries large societal impacts through morbidity and mortality caused by treatment failure. Several previous studies put forth theory regarding the optimal timing, targeting and absolute level of treatment in populations. Few of these studies, however, have considered populations with explicit structure. Here we present a model of antiviral resistance on networks and explore the timing, targeting and levels of treatment. Interestingly, we find bistability as a result of treatment leading to the existence of an unstable manifold, above which successful treatment (i.e.: no resistance) is impossible. We find, contrary to previous results, that degree-targeted treatment is not optimal, and leads to higher levels of resistance than random treatment. Additionally, in accordance with previous results, we find an optimum level of treatment which is less than 100%. These findings findings have important consequences in guiding policy behind influenza treatment. The bistability indicates that caution should be taken when treating populations when the absolute numbers of infections are unknown. Positively, our results indicate that putting resources into targeted treatment is not necessary, random treatment is preferable.

=== Learning in Random Boolean Networks ===

Nick A., Keegan, Matteo, Vikram, Sarah, Mark

Inspired by biochemical networks which adapt on evolutionary timescales, neural networks that adapt during development and learning, and universal computation in cellular automata, we have implemented several models of learning in Random Boolean Networks (RBNs) in order to better understand the relationships between network structure, node interaction rules, and network output.

=== Enzyme Catalysis and the Outcome of Chemical Reactions ===
Piotr and Georg W.
Enzymes are catalysts that accelerate chemical reactions but do not affect their outcome. This traditional paradism was developed under artificail test tube conditions. Our project investigates the possibility that the presence of an enzyme can alter the course of a reaction if it takes place under more physiologic conditions.

=== How Does a Stochastic Environment affect Community Assembly? ===

Xue, Χλοε, Xiaoli

We are interested in how exogenous temporal variability in resource availability affects the community structure of organisms with different resource-use strategies. Organisms induce additional resource stress on each other through competition. This is an abstraction of an arid environment with unreliable rainfall; the organisms themselves have been abstracted to four unitless parameters that allocate their resources to different parts of their lifecycles. The system has memory, as the previous presence of an organism affects the resource transport mechanism (an abstraction of soil).

=== How Does a Network’s Structure Influence its Traceability? ===

Xin and Abby

We are interested in systematically studying the problem of finding the source of a contamination spread through a network. We model a contamination spreading through the food distribution network, which we represent by interconnections between farmers, distributors, and retailers, and construct an estimator for the outbreak source given only this structure. We show how the ability of the estimator to narrow down the source identification problem changes with the connectivity and the number of observations. We propose a measure for traceability, or the overall ability to identify the source of spreading given any set of outbreak observations, based on entropy. We show how this measure appropriately reflects the range of uncertainty in identifying the source. We believe this measure may be useful in the design of networks that are conducive to accurate identification of the source of contamination.

=== We Got the Skills to Pay the Bills===

Andrés, Charlie, Gareth, and Nick

Where does diversity in skills or occupations come from and why does it lead to more innovative cities? Previous work in this area has shown that there is a scaling behaviour which allows citizens of larger cities to earn an extra 15% in income whilst making use of 15% fewer gas stations, for example. Making use of occupation, patent, and population data of US Metropolitan Statistical Areas (MSA), we try to understand what factors make successful cities. Here we assume that successful cities are those cities which are most innovative as determined by the production of patents. In addition we use agent-based modelling to explore how and why people acquire new skills and whether this leads to more productive cities.

Complex Systems Summer School 2012-Project Presentations

2012-06-27T02:27:44Z

GHaslam:

{{Complex Systems Summer School 2012}}

Use this space to post project presentations and outlines. Include group members, a brief outline, and your slides.

=== Price-time Dynamics of Contracts Traded on Prediction Markets ===

Joanne, Vikram, Matteo, Sanith

Prediction markets have been shown to outperform traditional methods of polls and opinion surveys in forecasting future events. The futures contracts traded in these markets assess the expectation of occurrence of a variety of events spread across multiple domains (political, economic, entertainment, financial and weather). We explore the feasibility of 'predicting' the outcome of binary true/false prediction market contracts ahead of their expiry date using a neural-network based machine learning approach. In addition we focus on the characteristics of political-based contracts to establish whether they exhibit characteristic 'fundamental' properties.

=== How Complex Languages Replicate through Simple Brains ===

Katrien, Vanessa, Sandro, Cameron, Jasmeen

Through the use of an iterated learning experiment, we investigated the transmission of a "high entropy", randomised initial language through successive generations of participants. We want to see what features of the language replicated most easily, and what structure emerged by the end of the chain. Our hypothesis is that the language converges to a "low entropy" equilibrium state with a minimal number of words, morphemes, and form-meaning distinctions.

=== Collaboration in times of stress: an Agent Based Modelling approach ===

Fabio Cresto Aleina, Elena del Val, Tom Fennewald and Friederike Greb

We want to investigate the influence of exogenous stress on cooperative behaviour. We propose an agent based model in which the agents can be interpreted as farmers living in a water limited environment. With changes in precipitation patterns, the farmers undergo stress, and we observe how this impacts relationships among farmers and their properties.

=== Simple variation of the logistic map as a model to invoke questions on cellular protein trafficking ===
(Sepehr Ehsani, http://arxiv.org/abs/1206.5557)

Many open problems in biology, as in the physical sciences, display nonlinear and 'chaotic' dynamics, which, to the extent possible, cannot be reasonably understood. Moreover, mathematical models which aim to predict/estimate unknown aspects of a biological system cannot provide more information about the set of biologically meaningful (e.g., 'hidden') states of the system than could be understood by the designer of the model ab initio. Here, the case is made for the utilization of such models to shift from a 'predictive' to a 'questioning' nature, and a simple natural-logarithm variation of the logistic polynomial map is presented that can invoke questions about protein trafficking in eukaryotic cells.

===Changes in Social Network Structure in Response to Crisis: Using Twitter data to Explore the Effect of the Tōhoku Earthquake.===

Christa Brelsford and Xin Lu

Abstract:
We use twitter data from 7 days before and after the Tōhoku Earthquake to explore how cooperation rates, social network structure and connectivity, and social network vulnerability changed in Japan in response to the disaster. An English language data set is collected for the same time period to use as a control. Data is collected for a period of 96 hours starting from March 4th 2011 2:46pm JST and for 96 hours beginning March 11th 2011 2:46 pm JST. The rate of cooperative behavior, measured by the occurrence of helping words in tweets increases slightly in the English dataset and by an order of magnitude in the Japanese dataset. A network analysis is also performed. Network edges are retweets and direct messages. In future work, we hope to explore whether problem solving capacity in a social system changes in response to crises, based on changes in the rate of cooperation and information transfer in a network.

=== The CSSS Network ===

Tom & Riccardo (with JP and others)

We will investigate the questions you are dying to know: What interesting interactions are revealed from the first 3 weeks of the Complex System Summer School survey? Have barriers between academic disciplines been broken down? Do power laws fit the data!? ...

Let us know if you have specific questions or if you would like to be involved in data analysis!

=== Is there a method in the madness? the dynamic structures of human language use ===

Priya and Riccardo

Psychiatric anecdotal reports point to the monotony, lack of emotion and sometimes intelligibility in many clinical populations. Linear measures of fluency and prosody, however, present only controversial differences between patients and healthy controls and only in unnatural phonations (i.e. say "aaaaa" for 30 secs).
We therefore go complex and chaotic on a set of more ecological recordings and transcriptions from 4 clinical populations (Asperger's, Schizophrenics, Depressed and Right Hemisphere Damage patients) as well as from healthy controls.
We then set a classifier-driven race: will non-linear analyses outcompete linear analyses in discriminating between pathologies?

=== Escaping the Poverty Trap: Modeling the Interplay Between Economic Growth and the Ecology of Infectious Disease ===

Georg, Ben, Laurent, Oscar

The dynamics of economies and infectious disease are inexorably linked: economic well-being influences health (sanitation, nutrition, etc) and health influences economic well-being (labor productivity lost to sickness and disease). Often societies are locked into "poverty traps" of poor health and poor economy. Here, we demonstrate poverty traps formed in models of infection and endogenous growth, as well as ways to break out of poverty traps. We explore two mechanisms of escape: one, through an influx of capital, and another through changing the percentage of GDP spent on healthcare. We find large influxes of capital is successful, but increasing health spending does not. Our results have important policy implications in the distribution of aid and within-country healthcare spending.

=== The Targeting and Timing of Treatment Influences the Emergence of Influenza Resistance in Structured Populations ===

Ben, Laurent, Oscar, Georg

Evolution of antiviral resistance in influenza carries large societal impacts through morbidity and mortality caused by treatment failure. Several previous studies put forth theory regarding the optimal timing, targeting and absolute level of treatment in populations. Few of these studies, however, have considered populations with explicit structure. Here we present a model of antiviral resistance on networks and explore the timing, targeting and levels of treatment. Interestingly, we find bistability as a result of treatment leading to the existence of an unstable manifold, above which successful treatment (i.e.: no resistance) is impossible. We find, contrary to previous results, that degree-targeted treatment is not optimal, and leads to higher levels of resistance than random treatment. Additionally, in accordance with previous results, we find an optimum level of treatment which is less than 100%. These findings findings have important consequences in guiding policy behind influenza treatment. The bistability indicates that caution should be taken when treating populations when the absolute numbers of infections are unknown. Positively, our results indicate that putting resources into targeted treatment is not necessary, random treatment is preferable.

=== Learning in Random Boolean Networks ===

Nick A., Keegan, Matteo, Vikram, Sarah, Mark

Inspired by biochemical networks which adapt on evolutionary timescales, neural networks that adapt during development and learning, and universal computation in cellular automata, we have implemented several models of learning in Random Boolean Networks (RBNs) in order to better understand the relationships between network structure, node interaction rules, and network output.

=== Enzyme Catalysis and the Outcome of Chemical Reactions ===
Piotr and Georg W.
Enzymes are catalysts that accelerate chemical reactions but do not affect their outcome. This traditional paradism was developed under artificail test tube conditions. Our project investigates the possibility that the presence of an enzyme can alter the course of a reaction if it takes place under more physiologic conditions.

=== How Does a Network’s Structure Influence its Traceability? ===

Xin and Abby

We are interested in systematically studying the problem of finding the source of a contamination spread through a network. We model a contamination spreading through the food distribution network, which we represent by interconnections between farmers, distributors, and retailers, and construct an estimator for the outbreak source given only this structure. We show how the ability of the estimator to narrow down the source identification problem changes with the connectivity and the number of observations. We propose a measure for traceability, or the overall ability to identify the source of spreading given any set of outbreak observations, based on entropy. We show how this measure appropriately reflects the range of uncertainty in identifying the source. We believe this measure may be useful in the design of networks that are conducive to accurate identification of the source of contamination.

=== We Got the Skills to Pay the Bills===

Andrés, Charlie, Gareth, and Nick

Where does diversity in skills or occupations come from and why does it lead to more innovative cities?

Complex Systems Summer School 2012-After Hours

2012-06-19T23:06:25Z

GHaslam:

{{Complex Systems Summer School 2012}}

Use this space to organize your own after hours activities.

==Road Trip to Boulder, Colorado==

From Gareth: Hi all, I'm planning on making a trip up to Boulder, CO for the weekend. It's about a 6 1/2 hr drive from Santa Fe and I'll be renting a car. My main reason for the trip is to see a friend of mine so you might have to sort your own accommodation (camping/youth hostel/hotel). We're planning on a bit of hiking nearby. The plan is to leave straight from class on Friday evening and arrive back in Santa Fe on Sunday eve. If you're interested in splitting petrol and rental fee and joining me for some Springsteen singalongs then sign up:

1.

2.

3.

4.

==Some Banjo fun out on the town==

My brother will be having a concert this Saturday June 16 at the Second Street Brewery (original location) from 6-9p.m. I will be at the parking circle at 6p.m. For those who do not sign up for a car don't forget Friday and Saturday $5 cabs.

[http://www.secondstreetbrewery.com/2012/05/todd-the-fox-9/ Todd and the Fox Venue Details]

[http://www.toddandthefox.com/fr_home.cfm To hear their music]

If anyone would like to join:

Car 1: Juniper's Car 

1. Katrien (not sure if we'll be back from the lake trip by 6pm. Somebody can take my place if they want.) back up: Georg Weber

2. Marque

3. Sarah

4. Marco

==Dancing==

Swing dancing on Monday 18th June.
Lessons from 7 to 8 P.M.
Dancing from 8 on wards.
The cost is $8 including the lesson and dancing (or $3 for the dancing). Venue: Odd Fellows Hall, 1125 Cerrillos Road. We have not yet decided on transportation. We could either take a cab or walk -- Let's try to decide during dinner.
Sign up below if you are interested:

1.Vikram -- Slightly biased towards taking the lesson. 
2. Xue -- dancing, though not a strong preference. 
3. Mark - I could use a lesson, or twelve. Do we have transportation? 
4. Chloe -- would rather walk down with everyone than skip the lesson. 
5. Aleksandra -- would try lesson, may be stay for dancing. 

PS: If you are on the edge because you want to attend the session on "Introduction to Python". I would be happy to walk you through the basics of Python at a later time. -- Vikram

PPS: how about a Dancing Python lunch tomorrow? I can do intro tutoring too. --Chloe

Other varieties --

There's a contra on the 23rd; swing dancing most Mondays; this is supposed to be a great tango town, and the drop-in-friendly beginner class on Thursday PM was good ($20, though).

[http://www.santafenewmexican.com/Sidebar/Dance_fever_in_Santa_Fe swing, salsa, tango]

[http://www.folkmads.org/may_jun_calendar12.html contras, here and ABQ]

We've heard great Appalachian-style folk musicians here already, but I haven't found a ceili or hoedown locally.

--Chloe

==Trip to Taos==

JP and Tom are going to go to Taos on Saturday 6/16. Sights to see will include the High Road to Taos, Taos Pueblo, the Taos Gorge, Taos Earthships, and the plenty of Taos Hippies.
 
Car 1: JP's Camry 

1.[[JP]] 
2. 
3.Piotr 
4.Matteo 
5.Vikram 
 

Car 2: Tom's Car 
1 
2 Miguel 
3 Riccardo 
4 Priya 

From Andres: I'm sorry... I decided to stay tomorrow at St. John's. I'm very sorry to letting you know so late...! I want to rest, and there is some work I'd like to do...
From Nick: Same for me guys. I feel exhausted. Sorry for telling you so late. Enjoy!

==Trip to Abiquiu==

We are organizing a trip to lake Abiquiu this weekend. ATTENTION ATTENTION! Drivers (Christa, Fabio, John, Tom and David) will meet at 8:30 tomorrow morning (Saturday), and will go with Christa to town to rent 4 cars. We'll pick the others up at 9:30. Those in Christas car, meet at 9am. See you tomorrow!

Fabio

Friederike

Elena

Mikkel

John

Nona

Abby

Marco

Aleksandra

Jasmeen

Dan

Sandro

Ian (if there is any room)

Vanessa (ditto)

Christa (my car seats 5 including me, but I want to stop by Los Alamos to pick up my dog on the way. That adds ~30 min to the drive. "Christa's Honda has manual transmission. do we need a second driver on the car who can drive a stick shift car?" "Yes"- Christa

1. Christa

2. Xue (though I'm also willing to be a driver if necessary)

3. Katrien

4. Jianfeng Xu

5. Xin

Tom

Nick A

==Bandelier Field Trip==

Bandelier Field Trip
Trip to Bandelier National Monument on Sat. June 9.
We might string a visit to the Valles Caldera and Bradbury Science Museum/Los Alamos in as well. If another group would like to stay around Bandelier, we can split up.

Head over to the [[Bandelier Trip 2012 | Bandelier Trip]] Page to sign up!

==Mafia==

[[JP]] is a huge fan of Mafia/Werewolf. Let's play a game sometime in the lower commons.

Let's meet Saturday evening at 8:00 in the lower commons for our first game.

- [[Ryan_James|Ryan]] is down for this.

- Jasmeen is also a big fan of Mafia.

- Ian has never played, but is interested

- Vikram is interested in learning the game.

- Tom F. would like to join and can also teach "The Resistance" a very similar game

- Katrien wants to play too

==FOOTBALL!==

Anyone up for a friendly game of soccer? We can check out equipment from the gym.

[Team: Continuous!] 
1. [[Piotr Milanowski|Piotr]] 
2. [[Marco Duenas|Marco]] 
3.[[Oleksandr Ivanov|Alex]] 
4. 
5. 
[Team: Discrete!] 
1. [[Fabio Cresto Aleina|Fabio]] 
2. [[Matteo Chinazzi|Matteo]] 
3.[[JP]] 
4. 
5.

Complex Systems Summer School 2012-After Hours

2012-06-19T23:05:59Z

GHaslam:

{{Complex Systems Summer School 2012}}

Use this space to organize your own after hours activities.

==Road Trip to Boulder, Colorado==

Hi all, I'm planning on making a trip up to Boulder, CO for the weekend. It's about a 6 1/2 hr drive from Santa Fe and I'll be renting a car. My main reason for the trip is to see a friend of mine so you might have to sort your own accommodation (camping/youth hostel/hotel). We're planning on a bit of hiking nearby. The plan is to leave straight from class on Friday evening and arrive back in Santa Fe on Sunday eve. If you're interested in splitting petrol and rental fee and joining me for some Springsteen singalongs then sign up:

1.

2.

3.

4.

==Some Banjo fun out on the town==

My brother will be having a concert this Saturday June 16 at the Second Street Brewery (original location) from 6-9p.m. I will be at the parking circle at 6p.m. For those who do not sign up for a car don't forget Friday and Saturday $5 cabs.

[http://www.secondstreetbrewery.com/2012/05/todd-the-fox-9/ Todd and the Fox Venue Details]

[http://www.toddandthefox.com/fr_home.cfm To hear their music]

If anyone would like to join:

Car 1: Juniper's Car 

1. Katrien (not sure if we'll be back from the lake trip by 6pm. Somebody can take my place if they want.) back up: Georg Weber

2. Marque

3. Sarah

4. Marco

==Dancing==

Swing dancing on Monday 18th June.
Lessons from 7 to 8 P.M.
Dancing from 8 on wards.
The cost is $8 including the lesson and dancing (or $3 for the dancing). Venue: Odd Fellows Hall, 1125 Cerrillos Road. We have not yet decided on transportation. We could either take a cab or walk -- Let's try to decide during dinner.
Sign up below if you are interested:

1.Vikram -- Slightly biased towards taking the lesson. 
2. Xue -- dancing, though not a strong preference. 
3. Mark - I could use a lesson, or twelve. Do we have transportation? 
4. Chloe -- would rather walk down with everyone than skip the lesson. 
5. Aleksandra -- would try lesson, may be stay for dancing. 

PS: If you are on the edge because you want to attend the session on "Introduction to Python". I would be happy to walk you through the basics of Python at a later time. -- Vikram

PPS: how about a Dancing Python lunch tomorrow? I can do intro tutoring too. --Chloe

Other varieties --

There's a contra on the 23rd; swing dancing most Mondays; this is supposed to be a great tango town, and the drop-in-friendly beginner class on Thursday PM was good ($20, though).

[http://www.santafenewmexican.com/Sidebar/Dance_fever_in_Santa_Fe swing, salsa, tango]

[http://www.folkmads.org/may_jun_calendar12.html contras, here and ABQ]

We've heard great Appalachian-style folk musicians here already, but I haven't found a ceili or hoedown locally.

--Chloe

==Trip to Taos==

JP and Tom are going to go to Taos on Saturday 6/16. Sights to see will include the High Road to Taos, Taos Pueblo, the Taos Gorge, Taos Earthships, and the plenty of Taos Hippies.
 
Car 1: JP's Camry 

1.[[JP]] 
2. 
3.Piotr 
4.Matteo 
5.Vikram 
 

Car 2: Tom's Car 
1 
2 Miguel 
3 Riccardo 
4 Priya 

From Andres: I'm sorry... I decided to stay tomorrow at St. John's. I'm very sorry to letting you know so late...! I want to rest, and there is some work I'd like to do...
From Nick: Same for me guys. I feel exhausted. Sorry for telling you so late. Enjoy!

==Trip to Abiquiu==

We are organizing a trip to lake Abiquiu this weekend. ATTENTION ATTENTION! Drivers (Christa, Fabio, John, Tom and David) will meet at 8:30 tomorrow morning (Saturday), and will go with Christa to town to rent 4 cars. We'll pick the others up at 9:30. Those in Christas car, meet at 9am. See you tomorrow!

Fabio

Friederike

Elena

Mikkel

John

Nona

Abby

Marco

Aleksandra

Jasmeen

Dan

Sandro

Ian (if there is any room)

Vanessa (ditto)

Christa (my car seats 5 including me, but I want to stop by Los Alamos to pick up my dog on the way. That adds ~30 min to the drive. "Christa's Honda has manual transmission. do we need a second driver on the car who can drive a stick shift car?" "Yes"- Christa

1. Christa

2. Xue (though I'm also willing to be a driver if necessary)

3. Katrien

4. Jianfeng Xu

5. Xin

Tom

Nick A

==Bandelier Field Trip==

Bandelier Field Trip
Trip to Bandelier National Monument on Sat. June 9.
We might string a visit to the Valles Caldera and Bradbury Science Museum/Los Alamos in as well. If another group would like to stay around Bandelier, we can split up.

Head over to the [[Bandelier Trip 2012 | Bandelier Trip]] Page to sign up!

==Mafia==

[[JP]] is a huge fan of Mafia/Werewolf. Let's play a game sometime in the lower commons.

Let's meet Saturday evening at 8:00 in the lower commons for our first game.

- [[Ryan_James|Ryan]] is down for this.

- Jasmeen is also a big fan of Mafia.

- Ian has never played, but is interested

- Vikram is interested in learning the game.

- Tom F. would like to join and can also teach "The Resistance" a very similar game

- Katrien wants to play too

==FOOTBALL!==

Anyone up for a friendly game of soccer? We can check out equipment from the gym.

[Team: Continuous!] 
1. [[Piotr Milanowski|Piotr]] 
2. [[Marco Duenas|Marco]] 
3.[[Oleksandr Ivanov|Alex]] 
4. 
5. 
[Team: Discrete!] 
1. [[Fabio Cresto Aleina|Fabio]] 
2. [[Matteo Chinazzi|Matteo]] 
3.[[JP]] 
4. 
5.

Experiment sign-up

2012-06-18T19:20:41Z

GHaslam:

Hi! Please sign up for one slot below. The experiment will be held in the JUNIOR Common Room next to the great hall (NOT JP's office - the other side of the great hall). Please arrive on time! Ta.

- Katrien, Vanessa, Sandro, Cameron & Jasmeen

==Monday June 18==

Morning Break:

10.20-10.30: Piotr X

10.30-10.40: Laurent X

Lunch Break:

12.05-12.15: Chloe X

'''12.15-12.25: Andres ???'''

12.25-12.35: Xue X

12.35-12.45: Oleksandr X

12.55-1.05: Nick A X

1.05-1.15: Fabio X

1.15-1.25: Priya X

Afternoon:

4.30-4.40: Matteo

4.40-4.50:Ben

4.50-5.00: Sarah

5.00-5.10: Charlie

5.10-5.20: Abby

5.20-5.30: Elena

5.30-5.40: Riccardo

5.40-5.50: [[JP]]!

5.50-6.00: Ryan

6.00-6.10: [[Xin]]

6.10-6.20: Ian

6.20-6.30: Keegan

6.30-6.40: Aleksandra

6.40-6.50: Tom

6.50-7.00:Christa

==Tuesday June 19==

Morning Break:

10.20-10.30: Vikram

10.30-10.40: Oscar

Lunch Break:

12.30-12.40: Gareth

12.40-12.50:

12.50-1.00:

1.00-1.10:

1.10-1.20:

1.20-1.30:

Afternoon:

4.30-4.40:

4.40-4.50:

4.50-5.00:

5.00-5.10:

5.10-5.20:

5.20-5.30:

5.30-5.40:

5.40-5.50:

5.50-6.00:

6.00-6.10:

6.10-6.20:

6.20-6.30:

6.30-6.40:

6.40-6.50:

6.50-7.00:

Experiment sign-up

2012-06-18T19:19:18Z

GHaslam:

Hi! Please sign up for one slot below. The experiment will be held in the JUNIOR Common Room next to the great hall (NOT JP's office - the other side of the great hall). Please arrive on time! Ta.

- Katrien, Vanessa, Sandro, Cameron & Jasmeen

==Monday June 18==

Morning Break:

10.20-10.30: Piotr X

10.30-10.40: Laurent X

Lunch Break:

12.05-12.15: Chloe X

'''12.15-12.25: Andres ???'''

12.25-12.35: Xue X

12.35-12.45: Oleksandr X

12.55-1.05: Nick A X

1.05-1.15: Fabio X

1.15-1.25: Priya X

Afternoon:

4.30-4.40: Matteo

4.40-4.50:Ben

4.50-5.00: Sarah

5.00-5.10: Charlie

5.10-5.20: Abby

5.20-5.30: Elena

5.30-5.40: Riccardo

5.40-5.50: [[JP]]!

5.50-6.00: Ryan

6.00-6.10: [[Xin]]

6.10-6.20: Ian

6.20-6.30: Keegan

6.30-6.40: Aleksandra

6.40-6.50: Tom

6.50-7.00:Christa

==Tuesday June 19==

Morning Break:

10.20-10.30: Vikram

10.30-10.40: Oscar

Lunch Break:

12.30-12.40: [[Gareth]]

12.40-12.50:

12.50-1.00:

1.00-1.10:

1.10-1.20:

1.20-1.30:

Afternoon:

4.30-4.40:

4.40-4.50:

4.50-5.00:

5.00-5.10:

5.10-5.20:

5.20-5.30:

5.30-5.40:

5.40-5.50:

5.50-6.00:

6.00-6.10:

6.10-6.20:

6.20-6.30:

6.30-6.40:

6.40-6.50:

6.50-7.00:

Complex Systems Summer School 2012-After Hours

2012-06-10T03:52:13Z

GHaslam:

{{Complex Systems Summer School 2012}}

Use this space to organize your own after hours activities.

==Prometheus==
While I have a rental car that seats up to 5, I've been thinking of going to see Prometheus in theaters. It shows tonight the 9th at 10:40 so we need to meet at the visitor's circle at 10:25, if anyone's interested. more than 5 people will require another car or taxis.

1. Ian

2. John

3. Fabio

==Saturday Football==

Who is up for a quick match tomorrow at 8am before the Bandelier trip? Let's make it a relaxed, just-for-fun match. Sign up below:

- [[Miguel Lurgi|Miguel]]

==Bandelier Field Trip==

Bandelier Field Trip
Trip to Bandelier National Monument on Sat. June 9.
We might string a visit to the Valles Caldera and Bradbury Science Museum/Los Alamos in as well. If another group would like to stay around Bandelier, we can split up.

Head over to the [[Bandelier Trip 2012 | Bandelier Trip]] Page to sign up!

==Mafia==

[[JP]] is a huge fan of Mafia/Werewolf. Let's play a game sometime in the lower commons.

Let's meet Saturday evening at 8:00 in the lower commons for our first game.

- [[Ryan_James|Ryan]] is down for this.

- Jasmeen is also a big fan of Mafia.

- Ian has never played, but is interested

- Vikram is interested in learning the game.

- Tom F. would like to join and can also teach "The Resistance" a very similar game

==FOOTBALL!==

Anyone up for a friendly game of soccer? We can check out equipment from the gym.

[Team: Continuous!] 
1. [[Piotr Milanowski|Piotr]] 
2. [[Marco Duenas|Marco]] 
3.[[Oleksandr Ivanov|Alex]] 
4. 
5. 
[Team: Discrete!] 
1. [[Fabio Cresto Aleina|Fabio]] 
2. [[Matteo Chinazzi|Matteo]] 
3.[[JP]] 
4. 
5.

Complex Systems Summer School 2012-After Hours

2012-06-10T03:40:27Z

GHaslam:

{{Complex Systems Summer School 2012}}

Use this space to organize your own after hours activities.

==Prometheus==
While I have a rental car that seats up to 5, I've been thinking of going to see Prometheus in theaters. It shows tonight the 9th at 10:40 so we need to meet at the visitor's circle at 10:25, if anyone's interested. more than 5 people will require another car or taxis.

1. Ian

2. John

3. Fabio

4. Abby

5. Gareth

==Saturday Football==

Who is up for a quick match tomorrow at 8am before the Bandelier trip? Let's make it a relaxed, just-for-fun match. Sign up below:

- [[Miguel Lurgi|Miguel]]

==Bandelier Field Trip==

Bandelier Field Trip
Trip to Bandelier National Monument on Sat. June 9.
We might string a visit to the Valles Caldera and Bradbury Science Museum/Los Alamos in as well. If another group would like to stay around Bandelier, we can split up.

Head over to the [[Bandelier Trip 2012 | Bandelier Trip]] Page to sign up!

==Mafia==

[[JP]] is a huge fan of Mafia/Werewolf. Let's play a game sometime in the lower commons.

Let's meet Saturday evening at 8:00 in the lower commons for our first game.

- [[Ryan_James|Ryan]] is down for this.

- Jasmeen is also a big fan of Mafia.

- Ian has never played, but is interested

- Vikram is interested in learning the game.

- Tom F. would like to join and can also teach "The Resistance" a very similar game

==FOOTBALL!==

Anyone up for a friendly game of soccer? We can check out equipment from the gym.

[Team: Continuous!] 
1. [[Piotr Milanowski|Piotr]] 
2. [[Marco Duenas|Marco]] 
3.[[Oleksandr Ivanov|Alex]] 
4. 
5. 
[Team: Discrete!] 
1. [[Fabio Cresto Aleina|Fabio]] 
2. [[Matteo Chinazzi|Matteo]] 
3.[[JP]] 
4. 
5.

Bandelier Trip 2012

2012-06-09T15:51:03Z

GHaslam:

{{Complex Systems Summer School 2012}}

Please sign up here so we know who's going. 
Also: If you have a car, please let us know. The more cars, the more people. 

We'll meet Saturday at 10:00am in the parking circle.

Please remember to bring a hat, sunscreen, water, hiking shoes, and anything else you'll need for a day out in the field.

If you would like to rent a car please visit the [http://santafe.edu/about/contact/ground/ SFI website] for more info

==Cars:==

===Tom's Sedan: 5 seats===
1. [[Nicholas Allgaier]] 

2. Vikram Vijayaraghavan 

3. Katrien Beuls 

4. Riccardo Fusaroli 

5. Priya Subramanian

===John Paul's Camry: 4 (maybe 5) seats===

1. [[John Paul]] 

2. [[Matteo Chinazzi]] 

3. [[Chloe Lewis]] 

4. [[Xue Feng]] 

5. [[Joanne Rodrigues]] 

===Juniper's Car: 4 seats===

1. Jasmeen Kanwal

2. Sarah Tweedt

3. Mark Longo

4. Hide Inamine

===[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa]'s Car: 4 (maybe 5) seats===

1. Christa

2. Nicolas Goudemand

3. Marco

4. [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin]

5(middle seat in a 2 door civic). [[Miguel Lurgi]]

===Charlie's car: 2 seats ===
My car's not that useful for this trip because I took out so many of the seats. I have only two seats besides the driver seat. Better than nothing.

1. Andres Gomez-Lievano

2. Vanessa Ferdinand

===Ian's Rental Compact from Enterprise (5 seats, at $75 / # of passengers)===

1. Ian Wood

2. Abby Horn

3.

4. Jianfeng Xu

5. Oleksandr Ivanov

===STILL NEEDS A SEAT!===
1.

2. [[Piotr Milanowski | Piotr]]

3. Georg M Goerg

4. Ben Althouse

5. Georg Weber

6. Oscar Patterson

7. David Pugh

8. Friederike Greb

9. Tom Fennewald

10. Mikkel Vestergaard

11. Nona Karalashvili

12. Elena del Val

13. Cameron Smith

14. Enrico Sandro Colizzi
15. Gareth Haslam

Bandelier Trip 2012

2012-06-09T02:38:03Z

GHaslam:

{{Complex Systems Summer School 2012}}

Please sign up here so we know who's going. 
Also: If you have a car, please let us know. The more cars, the more people. 

We'll meet Saturday at 10:00am in the parking circle.

Please remember to bring a hat, sunscreen, water, hiking shoes, and anything else you'll need for a day out in the field.

If you would like to rent a car please visit the [http://santafe.edu/about/contact/ground/ SFI website] for more info

==Cars:==

===Tom's Sedan: 5 seats===
1. [[Nicholas Allgaier]] 

2. Vikram Vijayaraghavan 

3. Katrien Beuls 

4. Riccardo Fusaroli 

5. Priya Subramanian

===John Paul's Camry: 4 (maybe 5) seats===

1. [[John Paul]] 

2. [[Matteo Chinazzi]] 

3. [[Chloe Lewis]] 

4. [[Xue Feng]] 

5. [[Joanne Rodrigues]] 

===Juniper's Car: 4 seats===

1. Jasmeen Kanwal

2. Sarah Tweedt

3. Mark Longo

4. Hide Inamine

===[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa]'s Car: 4 (maybe 5) seats===

1. Christa

2. Nicolas Goudemand

3. Marco

4. [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin]

5(middle seat in a 2 door civic). [[Miguel Lurgi]]

===Charlie's car: 2 seats ===
My car's not that useful for this trip because I took out so many of the seats. I have only two seats besides the driver seat. Better than nothing.

1. Andres Gomez-Lievano

2. Vanessa Ferdinand

===Ian's Rental Compact from Enterprise (5 seats, at $75 / # of passengers)===

1. Ian Wood

2. Abby Horn

3. Gareth Haslam

===STILL NEEDS A SEAT!===
1. Benji zusman

2. [[Piotr Milanowski | Piotr]]

3. Georg M Goerg

4. Oleksandr Ivanov

5. Ben Althouse

6. Georg Weber

7. Oscar Patterson

8. David Pugh

9. Friederike Greb

10. Jianfeng Xu

11. Tom Fennewald

12. Mikkel Vestergaard

13. Nona Karalashvili

14. Elena del Val

15. Cameron Smith

Bandelier Trip 2012

2012-06-08T02:11:05Z

GHaslam:

{{Complex Systems Summer School 2012}}

Please sign up here so we know who's going. 
Also: If you have a car, please let us know. The more cars, the more people. 

We'll meet Saturday at 10:00am in the parking circle.

Please remember to bring a hat, sunscreen, water, hiking shoes, and anything else you'll need for a day out in the field.

If you would like to rent a car please visit the [http://santafe.edu/about/contact/ground/ SFI website] for more info

==Cars:==

===Tom's Sedan: 5 seats===
1. [[Nicholas Allgaier]] 

2. Vikram Vijayaraghavan 

3. Katrien Beuls 

4. Riccardo Fusaroli 

5. Priya Subramanian

===John Paul's Camry: 4 (maybe 5) seats===

1. [[John Paul]] 

2. [[Matteo Chinazzi]] 

3. [[Chloe Lewis]] 

4. [[Xue Feng]] 

5. [[Joanne Rodrigues]] 

===Juniper's Car: 4 seats===

1. Jasmeen Kanwal

2. Sarah Tweedt

3. Mark Longo

4. Hide Inamine

===[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa]'s Car: 4 (maybe 5) seats===

1. Christa

2. Nicolas Goudemand

3. Marco

4. [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin]

5(middle seat in a 2 door civic). [[Miguel Lurgi]]

===Charlie's car: 2 seats ===
My car's not that useful for this trip because I took out so many of the seats. I have only two seats besides the driver seat. Better than nothing.

1. Andres Gomez-Lievano

2. Vanessa Ferdinand

===STILL NEEDS A SEAT!===
1.

2. [[Piotr Milanowski | Piotr]]

3. Georg M Goerg

4. Oleksandr Ivanov

5. Ben Althouse

6. Georg Weber

7. Oscar Patterson

8. David Pugh

9. Abby Horn

10. Ian Wood

11. Friederike Greb

12. Jianfeng Xu

13. Tom Fennewald

14. Mikkel Vestergaard

15. Nona Karalashvili

16. Elena del Val

17. Gareth Haslam

Innovation Group Project

2012-06-06T23:47:28Z

GHaslam:

== Ideas for Innovation Project ==

Please list here your individual ideas for what we might like to work on. At the moment Charlie Brummitt, Gareth Haslam, Daniel Wu, Nicolas Goudemand, Andres Gomez-Lievano, and Jienfang Xu have all expressed an interest. If there is anyone I have missed please let me (GH) know. Everything is still open at the moment so I think we agreed that each person should try and form a research question and then a few details about idea, methods etc. (keep it short). Please use this page to post comments, papers, and other thoughts. Also, please list any particular skills/assets you may have (existing data, software familiarity, programming, game design, graphic design, network construction etc.).

=== Idea 1 ===

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-06T23:34:04Z

GHaslam: /* Innovation and Technological Progress */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday. Also, here's a [http://posterhall.org/igert2012/posters/218 fun 3-minute video] of the effect I'm personally the most interested in, with a special appearance from The Princess Bride.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the [[Georg_M_Goerg#SFI_Project:_Traffic_pattern_analysis_-_Can_we_estimate_car_velocity_by_only_observing_car_counts.3F_.3D|SFI Project]] subsection on [[Georg_M_Goerg|Georg M. Goerg]] or email me to my_3_initials_in_lowercase@stat.cmu.edu. Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam [at] ias.unu.edu or find me in class. [[Innovation Group Project]]

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

The [[Spatial-Stochastic]] group is writing up their ideas to share here and look for overlap and coupling.

==== Entrenchment and rhythms ====

One idea that I had was to look at the entrenchment properties of various systems. This is an universal phenomena that arises from nonlinear mechanisms interacting with a fluctuation environment and appears most often in animal and plant physiological rhythms (e.g. circadian rhythms, sleep cycles) and result in predictable oscillations that can also sometimes be forced into stable/unstable states by noise (in the case of humans, this can result in disease). I would like to see if there are any mechanisms that produce similar behavior at the ecosystem level based on structural or species/functional diversity, especially in climates where the energy/water input is non-uniform. The "noise" in this case could be natural or anthropogenic disturbances. I think this can be generalized into many different types of systems. If you have an idea on this, please shoot me an email at xue.feng@duke.edu

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|300px|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

==== Relevant literature ====
* [http://www.barabasilab.com/pubs/CCNR-ALB_Publications/199910-15_Science-Emergence/199910-15_Science-Emergence.pdf BA Scale-free network]
* [http://people.maths.ox.ac.uk/maini/PKM%20publications/195.pdf How to generate Scale-free modular network using preferential attachment]
* [http://www.barabasilab.com/pubs/CCNR-ALB_Publications/200007-27_Nature-ErrorAttack/200007-27_Nature-ErrorAttack.pdf Error and attack tollerance of complex networks]
* [http://www.cis.upenn.edu/~mkearns/teaching/NetworkedLife/hierarchical.pdf Hierarchical organization in complex networks]
* [http://arxiv.org/pdf/cond-mat/0402009v1.pdf Scale-free networks with tunable degree distribution exponents]
* [http://arxiv.org/pdf/cond-mat/0110452v1.pdf Scale free networks with tunable clustering]
* [http://vw.indiana.edu/netsci06/conference/Ng_Structural.pdf Structural Robustness of Complex networks]
* [http://arxiv.org/pdf/cond-mat/0205405.pdf Assortative mixing in networks]
* [http://www.cis.upenn.edu/~mkearns/teaching/NetworkedLife/prefatt.pdf Mean field theory to study scale-free networks]
* [http://www.graphanalysis.org/SIAM-PP08/Leskovic.pdf Kroneker Graphs]
* [http://www.lem.sssup.it/WPLem/files/2011-07.pdf Exact maximum-likelihood method to detect patterns in real networks]
* [http://www-users.york.ac.uk/~lsdc1/SysBiol/kitano.robustness.naturegenetics.2004.pdf Biological robustness]
* [http://arxiv.org/ftp/cond-mat/papers/0202/0202410.pdf Attack vulnerability of complex networks]
* [http://jmvidal.cse.sc.edu/papers/nair11a.pdf Supply Network Topology and Robustness against Disruptions – an investigation using multi agent model]

* Add a relevant paper...

=== Systemic Risk in Financial Networks and/or an ABM of money/liquidity===

'''Systemic Risk in Financial Networks:''' Hypothesis: the motive to diversify risk at the level of the individual agent (i.e., for an agent to increase its connectivity) will increase systemic risk (by systemic risk I mean vulnerability of the system to widespread collapse). Point of departure is the [http://en.wikipedia.org/wiki/Forest-fire_model Forest Fire] model from statistical physics.

Key difference(s) between the physics version of the Forest Fire model, and the "economics" version of the Forest Fire model I have in mind are:
* Tree growth probability (which determines network structure) must be endogenous. Agents must be able to choose which other agents to link with.
* Probability of lightning strikes (i.e., defaults on specific loans) must also be endogenous.

I think that financial networks might exhibit self-organizing criticality in the sense that diversification will reduce the probability of lightning strikes (i.e., defaults), however over time systemic risk builds up as a result of the diversification which means that eventually a small number of lightning strikes might be enough to bring the entire system down.

'''ABM of the emergence of Money:'''
Basically, I would be interested in building an ABM of the emergence of money based around the following economic models of money developed by Nobu Kiyotaki and John Moore:
* [[File:Kiyotaki_and_Moore_(2001).pdf | Evil is the Root of all Money]]
* [[File:Financial-Deepening.pdf | Financial Deepening]]

These models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange. In these models agents manage projects which require capital investment now in order to generate a return at some point in the future and agents must trade financial promises (think debt contracts) in order to obtain the needed investment. Two key parameters of is these models (which are assumed COMMON to all agents in the above models in order to maintain analytical tractibility) are 0 < theta < 1 and 0 < phi < 1. Theta is the fraction of the future return from the project that an agent can promise to repay in the future in exchange for investment now. Phi is the fraction of the face value of a debt contract (which by construction is a contract between two agents) that can be re-sold to a third agent.

Hypothesis: In an ABM where agents differ in terms of both theta and phi, the promises of only a small number of agents will be widely traded (i.e., will serve as money).

If anyone might be interested in working on these projects, send me and email: drobert.pugh at gmail dot com

=== Price-time dynamics of contracts traded on prediction markets===
Prediction markets have been shown to outperform traditional methods of polls and opinion surveys in forecasting future events. I am interested in exploring the price-time dynamics of contracts traded on prediction markets to better understand how they are able to aggregate individual opinion to establish collective insight.

Several questions that I’m curious to probe further:
* How do ‘information shocks’ generated by news sources influence price-time trajectories?
* Can features of the underlying dynamics be characterized using a simple model?
* What is the minimum number of traders required for an accurate prediction?
On a separate note, I invite you to share your opinion regards whether “China will win the most medals at the 2012 London Olympics”, by logging into the following [https://csss12.inklingmarkets.com/user/login site] (please send me your email address and I will send you the login details).

If you haven't used a prediction market before don't worry -just follow the instructions provided in the site to 'buy' and 'sell' contracts according to your expectation.

If you are interested in the discussing any of the above questions or have other ideas related to prediction markets please get in touch with me at: sanith at mitre dot org

=== Internal models: what do they do and how are they built?===
In the past decade(s) Bayesian statistics has come to dominate empirical science. Consequently, the significance of prior beliefs for guiding inference has become widely accepted. But how do we map the concept of prior beliefs onto natural systems? I argue that the composition of organisms realize internal models of their environment. These internal models manifest as structured behavior, which we scientists describe as reflecting prior beliefs or bias. In humans you have reflexes at one extreme and the influence of memories upon behavior at the other. It is an open question how these internal models are instantiated in biological systems. Are they structural motifs in neural networks? Protein networks within cells? Concepts such as memory, storage, and recall provide relevant bridges between the statistical formulation of these ideas and the physics of computation, but these are jumping off points at best.

My suspicion is that part of the challenge is we don’t have a clear understanding of what benefits internals models impart to organisms beyond some general statement about resolving uncertainty. This is compounded by the fact that we probably wouldn’t recognize an internal model if we saw one. This is why I find work over the past decade upon self-localizing and mapping (SLAM) systems to be so interesting. To my knowledge, these are the first man-made systems designed with the objective of imparting complex internal models to artificial systems. The Mars rover is a SLAM system. The driverless car depends critically upon a SLAM system. The successes, and failures, of these systems have exposed the complexity of functionalities we use so naturally that they evade our notice. These include differentiating between static and dynamic elements of our environment as well as ascribing our sensations to external or internal causes. In the least, the design of these systems offer first order models for what an internal model of non-trivial complexity might look like.

I’m interested in exploring the role of internal models as well as how they are embedded in natural systems. I welcome you to join to me to discuss these ideas in the seminar room after 3 on Wednesday and after 4 on Thursday. Maybe we'll go to the coffee shop if that makes more sense. Feel free to email me at: jlong29@gmail.com (John Long)

=== Rain-Cimate-Agriculture Interactions ===

We are trying to think about the influence of different precipitation/climate regimes on farming strategies and crop prices. It is still a very vague idea, and we're meeting at 4:15 in front of the cafeteria to brainstorm a bit (possibly outside). If you're interested in it, you're very welcome to join us! fabio.cresto-aleina@zmaw.de and fgreb@uni-goettingen.de

Innovation Group Project

2012-06-06T23:31:19Z

GHaslam: Created page with '== Ideas for Innovation Project == Please list here your individual ideas for what we might like to work on. At the moment Charlie Brummitt, Gareth Haslam, Daniel Wu, Nicolas Go…'

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-06T23:30:31Z

GHaslam: /* Innovation and Technological Progress */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday. Also, here's a [http://posterhall.org/igert2012/posters/218 fun 3-minute video] of the effect I'm personally the most interested in, with a special appearance from The Princess Bride.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the [[Georg_M_Goerg#SFI_Project:_Traffic_pattern_analysis_-_Can_we_estimate_car_velocity_by_only_observing_car_counts.3F_.3D|SFI Project]] subsection on [[Georg_M_Goerg|Georg M. Goerg]] or email me to my_3_initials_in_lowercase@stat.cmu.edu. Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class. [[Innovation Group Project]]

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

The [[Spatial-Stochastic]] group is writing up their ideas to share here and look for overlap and coupling.

==== Entrenchment and rhythms ====

One idea that I had was to look at the entrenchment properties of various systems. This is an universal phenomena that arises from nonlinear mechanisms interacting with a fluctuation environment and appears most often in animal and plant physiological rhythms (e.g. circadian rhythms, sleep cycles) and result in predictable oscillations that can also sometimes be forced into stable/unstable states by noise (in the case of humans, this can result in disease). I would like to see if there are any mechanisms that produce similar behavior at the ecosystem level based on structural or species/functional diversity, especially in climates where the energy/water input is non-uniform. The "noise" in this case could be natural or anthropogenic disturbances. I think this can be generalized into many different types of systems. If you have an idea on this, please shoot me an email at xue.feng@duke.edu

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|300px|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

==== Relevant literature ====
* [http://www.barabasilab.com/pubs/CCNR-ALB_Publications/199910-15_Science-Emergence/199910-15_Science-Emergence.pdf BA Scale-free network]
* [http://people.maths.ox.ac.uk/maini/PKM%20publications/195.pdf How to generate Scale-free modular network using preferential attachment]
* [http://www.barabasilab.com/pubs/CCNR-ALB_Publications/200007-27_Nature-ErrorAttack/200007-27_Nature-ErrorAttack.pdf Error and attack tollerance of complex networks]
* [http://www.cis.upenn.edu/~mkearns/teaching/NetworkedLife/hierarchical.pdf Hierarchical organization in complex networks]
* [http://arxiv.org/pdf/cond-mat/0402009v1.pdf Scale-free networks with tunable degree distribution exponents]
* [http://arxiv.org/pdf/cond-mat/0110452v1.pdf Scale free networks with tunable clustering]
* [http://vw.indiana.edu/netsci06/conference/Ng_Structural.pdf Structural Robustness of Complex networks]
* [http://arxiv.org/pdf/cond-mat/0205405.pdf Assortative mixing in networks]
* [http://www.cis.upenn.edu/~mkearns/teaching/NetworkedLife/prefatt.pdf Mean field theory to study scale-free networks]
* [http://www.graphanalysis.org/SIAM-PP08/Leskovic.pdf Kroneker Graphs]
* [http://www.lem.sssup.it/WPLem/files/2011-07.pdf Exact maximum-likelihood method to detect patterns in real networks]
* [http://www-users.york.ac.uk/~lsdc1/SysBiol/kitano.robustness.naturegenetics.2004.pdf Biological robustness]
* [http://arxiv.org/ftp/cond-mat/papers/0202/0202410.pdf Attack vulnerability of complex networks]
* [http://jmvidal.cse.sc.edu/papers/nair11a.pdf Supply Network Topology and Robustness against Disruptions – an investigation using multi agent model]

* Add a relevant paper...

=== Systemic Risk in Financial Networks and/or an ABM of money/liquidity===

'''Systemic Risk in Financial Networks:''' Hypothesis: the motive to diversify risk at the level of the individual agent (i.e., for an agent to increase its connectivity) will increase systemic risk (by systemic risk I mean vulnerability of the system to widespread collapse). Point of departure is the [http://en.wikipedia.org/wiki/Forest-fire_model Forest Fire] model from statistical physics.

Key difference(s) between the physics version of the Forest Fire model, and the "economics" version of the Forest Fire model I have in mind are:
* Tree growth probability (which determines network structure) must be endogenous. Agents must be able to choose which other agents to link with.
* Probability of lightning strikes (i.e., defaults on specific loans) must also be endogenous.

I think that financial networks might exhibit self-organizing criticality in the sense that diversification will reduce the probability of lightning strikes (i.e., defaults), however over time systemic risk builds up as a result of the diversification which means that eventually a small number of lightning strikes might be enough to bring the entire system down.

'''ABM of the emergence of Money:'''
Basically, I would be interested in building an ABM of the emergence of money based around the following economic models of money developed by Nobu Kiyotaki and John Moore:
* [[File:Kiyotaki_and_Moore_(2001).pdf | Evil is the Root of all Money]]
* [[File:Financial-Deepening.pdf | Financial Deepening]]

These models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange. In these models agents manage projects which require capital investment now in order to generate a return at some point in the future and agents must trade financial promises (think debt contracts) in order to obtain the needed investment. Two key parameters of is these models (which are assumed COMMON to all agents in the above models in order to maintain analytical tractibility) are 0 < theta < 1 and 0 < phi < 1. Theta is the fraction of the future return from the project that an agent can promise to repay in the future in exchange for investment now. Phi is the fraction of the face value of a debt contract (which by construction is a contract between two agents) that can be re-sold to a third agent.

Hypothesis: In an ABM where agents differ in terms of both theta and phi, the promises of only a small number of agents will be widely traded (i.e., will serve as money).

If anyone might be interested in working on these projects, send me and email: drobert.pugh at gmail dot com

=== Price-time dynamics of contracts traded on prediction markets===
Prediction markets have been shown to outperform traditional methods of polls and opinion surveys in forecasting future events. I am interested in exploring the price-time dynamics of contracts traded on prediction markets to better understand how they are able to aggregate individual opinion to establish collective insight.

Several questions that I’m curious to probe further:
* How do ‘information shocks’ generated by news sources influence price-time trajectories?
* Can features of the underlying dynamics be characterized using a simple model?
* What is the minimum number of traders required for an accurate prediction?
On a separate note, I invite you to share your opinion regards whether “China will win the most medals at the 2012 London Olympics”, by logging into the following [https://csss12.inklingmarkets.com/user/login site] (please send me your email address and I will send you the login details).

If you haven't used a prediction market before don't worry -just follow the instructions provided in the site to 'buy' and 'sell' contracts according to your expectation.

If you are interested in the discussing any of the above questions or have other ideas related to prediction markets please get in touch with me at: sanith at mitre dot org

=== Internal models: what do they do and how are they built?===
In the past decade(s) Bayesian statistics has come to dominate empirical science. Consequently, the significance of prior beliefs for guiding inference has become widely accepted. But how do we map the concept of prior beliefs onto natural systems? I argue that the composition of organisms realize internal models of their environment. These internal models manifest as structured behavior, which we scientists describe as reflecting prior beliefs or bias. In humans you have reflexes at one extreme and the influence of memories upon behavior at the other. It is an open question how these internal models are instantiated in biological systems. Are they structural motifs in neural networks? Protein networks within cells? Concepts such as memory, storage, and recall provide relevant bridges between the statistical formulation of these ideas and the physics of computation, but these are jumping off points at best.

My suspicion is that part of the challenge is we don’t have a clear understanding of what benefits internals models impart to organisms beyond some general statement about resolving uncertainty. This is compounded by the fact that we probably wouldn’t recognize an internal model if we saw one. This is why I find work over the past decade upon self-localizing and mapping (SLAM) systems to be so interesting. To my knowledge, these are the first man-made systems designed with the objective of imparting complex internal models to artificial systems. The Mars rover is a SLAM system. The driverless car depends critically upon a SLAM system. The successes, and failures, of these systems have exposed the complexity of functionalities we use so naturally that they evade our notice. These include differentiating between static and dynamic elements of our environment as well as ascribing our sensations to external or internal causes. In the least, the design of these systems offer first order models for what an internal model of non-trivial complexity might look like.

I’m interested in exploring the role of internal models as well as how they are embedded in natural systems. I welcome you to join to me to discuss these ideas in the seminar room after 3 on Wednesday and after 4 on Thursday. Maybe we'll go to the coffee shop if that makes more sense. Feel free to email me at: jlong29@gmail.com (John Long)

=== Rain-Cimate-Agriculture Interactions ===

We are trying to think about the influence of different precipitation/climate regimes on farming strategies and crop prices. It is still a very vague idea, and we're meeting at 4:15 in front of the cafeteria to brainstorm a bit (possibly outside). If you're interested in it, you're very welcome to join us! fabio.cresto-aleina@zmaw.de and fgreb@uni-goettingen.de

New Wiki Page

2012-06-06T23:26:08Z

GHaslam: Created page with '== Innovation Group Project == Please list here your individual ideas for what we might like to work on. At the moment Charlie Brummitt, Gareth Haslam, Daniel Wu, Nicolas Goudem…'

== Innovation Group Project ==

Please list here your individual ideas for what we might like to work on. At the moment Charlie Brummitt, Gareth Haslam, Daniel Wu, Nicolas Goudemand, Andres Gomez-Lievano, and Jienfang Xu have all expressed an interest. If there is anyone I have missed please let me (GH) know. Everything is still open at the moment so I think we agreed that each person should try and form a research question and then a few details about idea, methods etc. (keep it short). Please use this page to post comments, papers, and other thoughts.

== Idea 1 ==

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-06T23:14:39Z

GHaslam:

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday. Also, here's a [http://posterhall.org/igert2012/posters/218 fun 3-minute video] of the effect I'm personally the most interested in, with a special appearance from The Princess Bride.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the [[Georg_M_Goerg#SFI_Project:_Traffic_pattern_analysis_-_Can_we_estimate_car_velocity_by_only_observing_car_counts.3F_.3D|SFI Project]] subsection on [[Georg_M_Goerg|Georg M. Goerg]] or email me to my_3_initials_in_lowercase@stat.cmu.edu. Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class. [[New Wiki Page]]

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

The [[Spatial-Stochastic]] group is writing up their ideas to share here and look for overlap and coupling.

==== Entrenchment and rhythms ====

One idea that I had was to look at the entrenchment properties of various systems. This is an universal phenomena that arises from nonlinear mechanisms interacting with a fluctuation environment and appears most often in animal and plant physiological rhythms (e.g. circadian rhythms, sleep cycles) and result in predictable oscillations that can also sometimes be forced into stable/unstable states by noise (in the case of humans, this can result in disease). I would like to see if there are any mechanisms that produce similar behavior at the ecosystem level based on structural or species/functional diversity, especially in climates where the energy/water input is non-uniform. The "noise" in this case could be natural or anthropogenic disturbances. I think this can be generalized into many different types of systems. If you have an idea on this, please shoot me an email at xue.feng@duke.edu

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|300px|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

==== Relevant literature ====
* [http://www.barabasilab.com/pubs/CCNR-ALB_Publications/199910-15_Science-Emergence/199910-15_Science-Emergence.pdf BA Scale-free network]
* [http://people.maths.ox.ac.uk/maini/PKM%20publications/195.pdf How to generate Scale-free modular network using preferential attachment]
* [http://www.barabasilab.com/pubs/CCNR-ALB_Publications/200007-27_Nature-ErrorAttack/200007-27_Nature-ErrorAttack.pdf Error and attack tollerance of complex networks]
* [http://www.cis.upenn.edu/~mkearns/teaching/NetworkedLife/hierarchical.pdf Hierarchical organization in complex networks]
* [http://arxiv.org/pdf/cond-mat/0402009v1.pdf Scale-free networks with tunable degree distribution exponents]
* [http://arxiv.org/pdf/cond-mat/0110452v1.pdf Scale free networks with tunable clustering]
* [http://vw.indiana.edu/netsci06/conference/Ng_Structural.pdf Structural Robustness of Complex networks]
* [http://arxiv.org/pdf/cond-mat/0205405.pdf Assortative mixing in networks]
* [http://www.cis.upenn.edu/~mkearns/teaching/NetworkedLife/prefatt.pdf Mean field theory to study scale-free networks]
* [http://www.graphanalysis.org/SIAM-PP08/Leskovic.pdf Kroneker Graphs]
* [http://www.lem.sssup.it/WPLem/files/2011-07.pdf Exact maximum-likelihood method to detect patterns in real networks]
* [http://www-users.york.ac.uk/~lsdc1/SysBiol/kitano.robustness.naturegenetics.2004.pdf Biological robustness]
* [http://arxiv.org/ftp/cond-mat/papers/0202/0202410.pdf Attack vulnerability of complex networks]
* [http://jmvidal.cse.sc.edu/papers/nair11a.pdf Supply Network Topology and Robustness against Disruptions – an investigation using multi agent model]

* Add a relevant paper...

=== Systemic Risk in Financial Networks and/or an ABM of money/liquidity===

'''Systemic Risk in Financial Networks:''' Hypothesis: the motive to diversify risk at the level of the individual agent (i.e., for an agent to increase its connectivity) will increase systemic risk (by systemic risk I mean vulnerability of the system to widespread collapse). Point of departure is the [http://en.wikipedia.org/wiki/Forest-fire_model Forest Fire] model from statistical physics.

Key difference(s) between the physics version of the Forest Fire model, and the "economics" version of the Forest Fire model I have in mind are:
* Tree growth probability (which determines network structure) must be endogenous. Agents must be able to choose which other agents to link with.
* Probability of lightning strikes (i.e., defaults on specific loans) must also be endogenous.

I think that financial networks might exhibit self-organizing criticality in the sense that diversification will reduce the probability of lightning strikes (i.e., defaults), however over time systemic risk builds up as a result of the diversification which means that eventually a small number of lightning strikes might be enough to bring the entire system down.

'''ABM of the emergence of Money:'''
Basically, I would be interested in building an ABM of the emergence of money based around the following economic models of money developed by Nobu Kiyotaki and John Moore:
* [[File:Kiyotaki_and_Moore_(2001).pdf | Evil is the Root of all Money]]
* [[File:Financial-Deepening.pdf | Financial Deepening]]

These models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange. In these models agents manage projects which require capital investment now in order to generate a return at some point in the future and agents must trade financial promises (think debt contracts) in order to obtain the needed investment. Two key parameters of is these models (which are assumed COMMON to all agents in the above models in order to maintain analytical tractibility) are 0 < theta < 1 and 0 < phi < 1. Theta is the fraction of the future return from the project that an agent can promise to repay in the future in exchange for investment now. Phi is the fraction of the face value of a debt contract (which by construction is a contract between two agents) that can be re-sold to a third agent.

Hypothesis: In an ABM where agents differ in terms of both theta and phi, the promises of only a small number of agents will be widely traded (i.e., will serve as money).

If anyone might be interested in working on these projects, send me and email: drobert.pugh at gmail dot com

=== Price-time dynamics of contracts traded on prediction markets===
Prediction markets have been shown to outperform traditional methods of polls and opinion surveys in forecasting future events. I am interested in exploring the price-time dynamics of contracts traded on prediction markets to better understand how they are able to aggregate individual opinion to establish collective insight.

Several questions that I’m curious to probe further:
* How do ‘information shocks’ generated by news sources influence price-time trajectories?
* Can features of the underlying dynamics be characterized using a simple model?
* What is the minimum number of traders required for an accurate prediction?
On a separate note, I invite you to share your opinion regards whether “China will win the most medals at the 2012 London Olympics”, by logging into the following [https://csss12.inklingmarkets.com/user/login site] (please send me your email address and I will send you the login details).

If you haven't used a prediction market before don't worry -just follow the instructions provided in the site to 'buy' and 'sell' contracts according to your expectation.

If you are interested in the discussing any of the above questions or have other ideas related to prediction markets please get in touch with me at: sanith at mitre dot org

=== Internal models: what do they do and how are they built?===
In the past decade(s) Bayesian statistics has come to dominate empirical science. Consequently, the significance of prior beliefs for guiding inference has become widely accepted. But how do we map the concept of prior beliefs onto natural systems? I argue that the composition of organisms realize internal models of their environment. These internal models manifest as structured behavior, which we scientists describe as reflecting prior beliefs or bias. In humans you have reflexes at one extreme and the influence of memories upon behavior at the other. It is an open question how these internal models are instantiated in biological systems. Are they structural motifs in neural networks? Protein networks within cells? Concepts such as memory, storage, and recall provide relevant bridges between the statistical formulation of these ideas and the physics of computation, but these are jumping off points at best.

My suspicion is that part of the challenge is we don’t have a clear understanding of what benefits internals models impart to organisms beyond some general statement about resolving uncertainty. This is compounded by the fact that we probably wouldn’t recognize an internal model if we saw one. This is why I find work over the past decade upon self-localizing and mapping (SLAM) systems to be so interesting. To my knowledge, these are the first man-made systems designed with the objective of imparting complex internal models to artificial systems. The Mars rover is a SLAM system. The driverless car depends critically upon a SLAM system. The successes, and failures, of these systems have exposed the complexity of functionalities we use so naturally that they evade our notice. These include differentiating between static and dynamic elements of our environment as well as ascribing our sensations to external or internal causes. In the least, the design of these systems offer first order models for what an internal model of non-trivial complexity might look like.

I’m interested in exploring the role of internal models as well as how they are embedded in natural systems. I welcome you to join to me to discuss these ideas in the seminar room after 3 on Wednesday and after 4 on Thursday. Maybe we'll go to the coffee shop if that makes more sense. Feel free to email me at: jlong29@gmail.com (John Long)

=== Rain-Cimate-Agriculture Interactions ===

We are trying to think about the influence of different precipitation/climate regimes on farming strategies and crop prices. It is still a very vague idea, and we're meeting at 4:15 in front of the cafeteria to brainstorm a bit (possibly outside). If you're interested in it, you're very welcome to join us! fabio.cresto-aleina@zmaw.de and fgreb@uni-goettingen.de

Alfred Hubler's Nonlinear Dynamics Lab 2012

2012-06-06T18:19:30Z

GHaslam:

{{Complex Systems Summer School 2012}}

==Thursday, June 7, 6:00pm==

1. Sarah Tweedt 
2. Georg F. Weber 
3. Georg M. Goerg 
4. Cameron Smith 
5. Mikkel Vestergaard 
6. Friederike Greb 
7. Fabio Cresto Aleina 
8. Benji zusman 
9. Elena del Val 
10.Riccardo Fusaroli 
11. Nick Allgaier 
12.John Long 
13. Sepehr Ehsani 
14. David Pugh 
15.Keegan Hines 
16.Sanith 

==Friday, June 8 7:00am==
PLEASE NOTE THAT THIS IS AN EARLY MORNING CLASS! 
1. Matteo Chinazzi 
2. Nona Karalashvili 
3. Xin Lu 
4. Joanne Rodrigues 
5. Chloe Lewis 
6. Seth Frey 
7. Graham Sack 
8. Piotr Milanowski 
9. Andres Gomez-Lievano 
10.Ian Wood 
11.Gareth Haslam 
12. 
13. 
14. 
15. 

==Monday, June 11, 6:00pm==
1. Hidetoshi Inamine 
2. Dan Wu 
3. Xiaoli Dong 
4. Si Tang 
5. Abby Horn 
6. Xue Feng 
7. Priya Subramanian 
8. Oscar Patterson 
9. Vanessa Ferdinand 
10.Tom Fennewald 
11.Jianfeng Xu 
12.Nicolas Goudemand 
13.Katrien Beuls 
14.Charlie Brummitt 
15.Ben Althouse 

==Tuesday, June 12, 6:00pm==
1. Marco Duenas

2. Jasmeen Kanwal 
3. shawana Wilson 
4. Miguel Lurgi 
5.Vikram Vijayaraghavan 
6.Mark Longo 
7.Oleksandr Ivanov 
8.Jon Stoffel 
9. Daniel Strombom 
10. 
11. 
12. 
13. 
14. 
15.

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T04:33:14Z

GHaslam: /* next idea here */

Complex Systems Summer School 2012-Tutorials

2012-06-05T04:15:33Z

GHaslam:

Complex Systems Summer School 2012-Tutorials

2012-06-05T01:18:10Z

GHaslam:

{{Complex Systems Summer School 2012}}

CSSS participants come from a wide range of disciplines. Participants are encouraged to share their knowledge by organizing their own tutorials.

Also, please post requests for tutorials here.

'''Monday 4th June''' Hi all: just wondering if anyone else had problems running the TISEAN program from today's lab? I'm using a Mac with OS X 10.5.8 (Leopard) and the solutions to get it running only extended to Lion and Snow Leopard users. I tried downloading and installing Xcode 3.1 but still got the error message:

Macintosh-2:tisean garethhaslam$ ./henon -h
dyld: Library not loaded: /usr/local/gfortran/lib/libgfortran.3.dylib
Referenced from: /Users/garethhaslam/Downloads/tisean/./henon
Reason: no suitable image found. Did find:
/usr/local/lib/libgfortran.3.dylib: unknown required load command 0x80000022
Trace/BPT trap

If anyone knows how to fix this can they send a link or catch me tomorrow morning: haslam@ias.unu.edu. Thanks, Gareth

Gareth Haslam

2012-05-29T01:55:24Z

GHaslam:

{{Complex Systems Summer School 2012}}

[[File:CIMG0124.jpg|300px]] 

Classic Van Der Graaf Generator Shot at the Toshiba Science Museum 

 

I finished my PhD last June which was focused on developing new electrocatalysts for use in hydrogen fuel cell vehicles (FCV). FCV are one possible technology that could be very useful in tackling CO2 emissions and air pollution in the transport sector as they convert hydrogen fuel to electricity directly rather than combusting fossil fuels, emitting only water. Whilst they have a number of challenges to overcome, I was looking at how to replace the expensive platinum electrocatalyst which is currently an essential component. This work was carried out at the Dept. of Materials Science at the University of Cambridge, UK. I have previously received a BSc Physics from the University of Durham, UK, and an MPhil in Engineering for Sustainable Development, also at the University of Cambridge. Whilst my background is more experimentally-based, I am keen to learn more about how complexity approaches may be used in materials discovery, at the micro-level, and in promoting technology innovation and alternative energy systems, at the macro-level.

After finishing my PhD, I spent two months at the University of Tokyo at the Dept. of Complexity Science, with the 2011 JSPS Summer Program (known as EAPSI in the US). I am now working as a postdoc at the United Nations University - Institute of Advanced Studies in Yokohama, Japan where I am trying to bring a technical perspective to science and innovation policy. I have done some preliminary research looking at how patent and publication data can be used as an indicator for fuel cell innovation and would like to expand that to look at social interactions between scientists, companies, institutes etc. Another interesting area might involve agent-based modelling to see the effect of different policies on improving the rate of technical progress/innovation. I am hoping that some of the ideas and experiences at the Santa Fe CSSS will provide new jumping-off points for further research once this position finishes in September 2012. I am excited and enthusiastic to meet everyone on the course, whether your interests are in energy, climate change, international development, science policy or anything else!

Gareth Haslam

2012-05-29T01:50:10Z

GHaslam: Created page with '{{Complex Systems Summer School 2012}} 600px Classic Van Der Graaf Generator Shot at the Toshiba Science Museum I finished my PhD last Ju…'

{{Complex Systems Summer School 2012}}

[[File:CIMG0124.jpg|600px]] 

Classic Van Der Graaf Generator Shot at the Toshiba Science Museum 

 

I finished my PhD last June which was focused on developing new electrocatalysts for use in hydrogen fuel cell vehicles (FCV). FCV are one possible technology that could be very useful in tackling CO2 emissions and air pollution in the transport sector as they convert hydrogen fuel to electricity directly rather than combusting fossil fuels, emitting only water. Whilst they have a number of challenges to overcome, I was looking at how to replace the expensive platinum electrocatalyst which is currently an essential component. This work was carried out at the Dept. of Materials Science at the University of Cambridge, UK. I have previously received a BSc Physics from the University of Durham, UK, and an MPhil in Engineering for Sustainable Development, also at the University of Cambridge. Whilst my background is more experimentally-based, I am keen to learn more about how complexity approaches may be used in materials discovery, at the micro-level, and in promoting technology innovation and alternative energy systems, at the macro-level.

After finishing my PhD, I spent two months at the University of Tokyo at the Dept. of Complexity Science, with the 2011 JSPS Summer Program (known as EAPSI in the US). I am now working as a postdoc at the United Nations University - Institute of Advanced Studies in Yokohama, Japan where I am trying to bring a technical perspective to science and innovation policy. I am hoping that some of the ideas and experiences at the Santa Fe CSSS will provide new jumping-off points for further research once this position finishes in September 2012. I am excited and enthusiastic to meet everyone on the course, whether your interests are in energy, climate change, international development, science policy or anything else!

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2012-05-29T01:48:44Z

GHaslam: Gareth Haslam

Gareth Haslam