Santa Fe Institute Events Wiki - User contributions [en]

Nathan Hodas

2009-11-23T02:45:56Z

Nhodas:

I am a graduate student in physics at Caltech in Pasadena, CA. I do both theoretical and experimental work in biophysics and optics. My work is only very roughly assigned to any particular discipline, as I have an advisor in chemistry and biology but attaining a degree in physics. I hope there are others who more specifically share my enjoyment of hiking, fishing, and playing squash on the St. John's courts.

'''1. What are your main interests? Feel free to include a "pie in the sky" big idea!''' I am curious about emergent behavior in networks. Cellular gene networks, traffic flow, and the economy are only a few examples of common phenomena where simple interactions properly coordinated give rise to wonderful results. To what extent does the underlying structure of the network determine the final dynamics? Is it possible to capture the overall behavior without knowing the microscopic details of individual interactions?

'''2. What sorts of expertise can you bring to the group?''' I hope my knowledge of non-equilibrium statistical mechanics, stochastic differential equations, and computer programming will help others answer questions they are curious about. In a past life, I studied highway traffic dynamics, and so I have experience connecting microscopic and macroscopic dynamics in the non-physical arena.

'''3. What do you hope to get out of the CSSS?''' I am excited about the multi-disciplinary atmosphere of the CSSS. I know that each discipline has its own perspective on how to interpret experimental data and how to how to critique hypotheses. I hope to absorb as much and as many of the diverse perspectives as I can.

'''4. Do you have any possible projects in mind for the CSSS? ''' I do not have any specific projects in mind, but I would like to do something in line with my interest in emergent behavior, as well as something outside of my primary field.

You can mail me, after removing the numbers, at: n3hodas 4at gmail do5t com

[[Media:LittleGuy.jpg]]

File:LittleGuy.jpg

2009-11-23T02:44:34Z

Nhodas:

CSSS 2009 Santa Fe-Final Papers

2009-09-02T06:23:16Z

Nhodas:

{{CSSS 2009 Santa Fe}}

===The Effect of Gossip on Social Networks===
'''Summary''': In this project we look at the effects of the spread of gossip (defined as information passed between two individuals A and B about an individual C who is not present) on social network structure.

'''By''' ([http://www.santafe.edu/events/workshops/index.php/Modeling_gossip_networks Group Page]): [[Allison Shaw]], [[Chang Yu]], [[Dave Brooks|David Brooks]], [[Milena Tsvetkova]], [[Roozbeh Daneshvar]]

* [[Media:Gossip.pdf|Final Paper]]

===Deconstructing CSSS09 Social Network===
'''Summary''': CSSS09 is a group of people interested in complex systems who are randomly chosen to attend the one month summer school. The international and interdisciplinary group spends four weeks together learning, discussing and working on projects related to complex systems. We are analyzing the social interaction of this network and the network change over time.

'''By''' ([[CSSS 09 Social Network|Group Page]]):
[[Margreth Keiler]], [[Murad Mithani]], [[Roozbeh Daneshvar]], [[Wendy Ham]]

* [[Media:Network_CSSS_2009.pdf|Final Paper]]

===The Effect of Disaggregation on Infection Spreading in a social network: 'More' may not be 'Merrier'===
'''Summary''': This project analyzes the dynamics of infection spreading in the disaggregated framework of a social network using prevalence data for different countries.

'''By''':
[[Varsha Kulkarni]]

* [[Media: Disaggregation_effect_paper.pdf|Final Paper]]

===Mom made me do it: Division of labor via maternal effects===
'''Summary''': We explore an alternative to cooperation for the evolution of division of labor (maternal manipulation) by means of an individual-based model.

'''By''':
[[Mauricio Gonzalez-Forero]], [[Mareen Hofmann]]

* [[Media: manipulation_19_08_2009.pdf|Final Paper]]

===Foraging on the Move===
'''Summary''':In this project we develop a model for organisms that forage in groups while migrating (e.g. caribou, wildebeest), to understand how individuals should balance foraging and flocking behaviors.

'''By''' ([http://www.santafe.edu/events/workshops/index.php/Foraging_on_the_move Group Page]): [[Allison Shaw]], [[Andrew Berdahl]], [[Kathrine Behrman|Kate Behrman]], [[Liliana Salvador]], [[Steven Lade]]

* [[Media:Foraging.pdf|Final Paper]]

===Approaches to Panarchy===
'''Summary''': Panarchy refers to a nested set of interacting dynamical systems, each one cycling over a hierarchy of scales in time and space. This concept provides a framework for thinking about complex systems in ecology, economics, sociology, etc. We consider novel examples and applications of panarchy.

'''By''': [[Barbara Bauer]], [[Andrew Noble]], [[Damian Winters]]

* [[Media:WaterWheel.pdf|Final Paper2]]

===Spiking Neural Networks on the Cusp of Chaos: Initial Report===
'''Summary''': Spiking neural networks, even of small order, show great variability in their behavioral patterns. This paper is an initial look at how structure of small spiking networks influence their measured behavior over time. Of particular interest are networks showing non-periodic behavior, one of the tell-tale indicators of chaos in the underlying dynamics.

'''By''': [[Watson|R. Watson]]

* [[Media:SpikingNetworksWatson2009.pdf|Final Paper]]

===Terrorism: Radicalization Mechanism and Spread Control===
'''Summary''': Preventing the spread of radical ideologies requires models capable of identifying areas and agents before occurrence of terrorist act. We develop a model that captures a radicalization mechanism through several intermediate stages of individuals. We show how our model can combat the development of terrorist networks even with limited information on a target terrorist network.

'''By'''([http://www.santafe.edu/events/workshops/index.php/Radicalization Group page]): [[ Alhaji Cherif]], [[ Hirotoshi Yoshioka]], [[Wei Ni]], [[Prasanta Bose]]

* [[Media:TerrorismWorkingPaper.pdf|Final Paper]]

===Acupuncture Points Complex Networks in Human Body===
'''Summary''': The therapeutic properties of the points of the fourteen meridians are generalized on the basis of the meridian is amenable to treatment. We constructed an acupuncture complex network of a portion of the body depending on different kinds of illness to figure out interactions between acupuncture points. We use 44 common illnesses such as common cold, headache as the portion to do the research. The constructed network contains a large component of 70 nodes out of 770 acupuncture points around the body.

'''By''' : [[Guimei Zhu]], [[Dave Brooks]], [[Brian Hollar]],

* [[Media:Acupuncture Points Complex Networks in Human Body.pdf|working Paper]]

===The Roundtable - An Agent-Based Model of Conversation Dynamics===
'''Abstract'''. We present and analyze an agent-based model of conversation dynamics. The model develops from intuitive assumptions derived from experimental evidence, it abstracts from conversation content and semantics while including topological and psychological information, and is driven by stochastic dynamics. The model exhibits rich behavior and can capture many aspects of real-life conversations. Its potential generalizations, including individual preferences, memory effects and more complex topologies, may find useful applications in other fields of research where dynamically-interacting and networked agents play a fundamental role.

'''By''' [[Lucas Lacasa]], [[Massimo Mastrangeli]] and [[Martin Schmidt]]

* [[Media:The Roundtable.pdf|Working Paper]]

===Exploring source-sink dynamics of pre-vaccination measles epidemics using a spectral formulation of Granger causality===
We present a method for determining spatial contact networks from time series data of disease transmission.

'''By''' [[Kathrine Behrman]], [[Alexander Mikheyev]] and [[Erin Taylor]]

* [[Media:SpatialContactNetwork.pdf|Final Paper]]

===Computational investigation of dynamic response of small networks: a research proposal===
'''Abstract''' We propose to determine the reaction of every network of
3-5 nodes to a standardised signal. We see the networks as cellular signalling
modules, but other interpretations may apply as well.
We argue that the results of this project may shed light on evolutionary properties
of small molecular networks. The computational
framework we propose to use is ''probabilistic model checking'', which guarantees
a mathematically sound and completely automatic classification of responses.
We invite the reader to undertake the research, with our assistance if required.

'''By''' [[Rosemary Braun]], [[Marek Kwiatkowski]] and [[Alexander Mikheyev]]

* [[Media:Small_graphs.pdf|The proposal]]

===Scalable Efficient Agent Based Models===
'''Abstract''' Agent based models (ABMs) are characterized by purposive intelligent agents which interact to yield complex emergent behaviors. While each agent operates with an autonomous localized set of rules, these rules typically involve interactions among agents and with the agents’ environment, presenting challenges when attempting to simulate very large populations of agents. At the same time, the scaling properties of complex adaptive systems are such that it is important to study the properties of an ABM over wide ranges of population size. This is because the emergent properties of a system typically change with scope and scale: mammalian basal metabolic rate scales sublinearly with animal mass, and incidence of crime in a city scales superlinearly with city population size. The attached Abstract of Work in Progress describes work conducted heretofore in deploying a spatial ABM to a distributed-memory computational cluster, along with planned next steps.

'''By''' [[Matt McMahon]]

*[[Media:ScalableEfficientAgentBasedModels.pdf|Abstract of Work in Progress]]

===1,2,3, language! Building the phylogenetric tree of languages with numbers===
'''Abstract''' In this paper we make use of bioinformatics tools to build up the phylogenetic tree of languages. We had access to a large dataset gathering the numbers one to ten in over five thousand languages. In a first step, for each language we have concatenated each of the ten numbers in a string. After defining a mapping between the 26-letter alphabet and the DNA-like codons, we make use of a global alignment method to calculate the distance between pairs of strings, i.e. between languages. We finally generate the distance matrix and its associated phylogenetic tree. Specifically, we have used this method to generate the phylogenetic tree of indoeuropean languages. Despite the small size of the dataset (only ten words per language), preliminary results perfectly match the state of the art. We finally discuss some potential applications and future work, on relation to culture-based concepts such as trading or spreading of culture.

'''By''' [[Andrew Berdahl]] and [[Lucas Lacasa]]

*[[Media:phylotree2.pdf|working paper]]

===Crossover phenomenon in the performance of an Internet search engine===
'''Abstract''' In this work we explore the ability of Google search engine to find results for random N-letter
strings. These random strings, dense over the set of possible N-letter words, address the existence of
typos, acronyms, and other words without semantic meaning. Interestingly, we find that the probability
of finding such strings sharply drops from one to zero at Nc = 6. The behavior of such order parameter
suggests the presence of a transition-like phenomenon in the geometry of the search space. Furthermore, we
define a susceptibility-like parameter which reaches a peaked maximum in the neighborhood, suggesting
the presence of criticality. We finally speculate on the possible connections to Ramsey theory.

'''By''' [[Lucas Lacasa]], [[Jacopo Tagliabue]] and [[Andrew Berdahl]]

*[[Media: report_google2.pdf|working paper]]

===Agent-Based Modeling of MEMS Fluidic Self-assembly===
'''Abstract'''. The dynamics of fluidic self-assembly (FSA) of Micro Electro-Mechanical Systems (MEMS), recently demonstrated by experimental data, is modeled using interacting software agents. This method enables realistic simulations of FSA dynamics and represents a significant step ahead in the state-of-the-art of this field.

'''By''' [[Massimo Mastrangeli]]

* [[Media:ABMxFSA|Working Paper]]

===The Individual and the Empire:The Effects of Agent-Based Emigration Behavior on the Emergence of Settlement Size Inequality in the Titicaca Basin, Bolivia and Peru===
'''Abstract'''. Approximately 1000 years ago, from a relatively homogeneous distribution of small, sedentary villages in the Andean highlands, emerged a state-level society with settlements ranging in size from small hamlets to a large urban center of some 50,000 individuals. This working paper explores a possible mechanism that caused vast inequalities in the distribution of settlement sizes in time and space in the Titicaca Basin. Using agent-based modeling, we show that the cumulative effects of simple, individual-based migration optimization behavior can create a settlement rank-size distribution with the same formal qualities as that observed during the Tiwanaku Empire's apex. The model's general applicability is explored, improvements are suggested, and future directions proposed.

'''By''' [[Randy Haas]], [[Jacopo Tagliabue]], and [[Jeremy Barofsky]]

* [[Media:Haas_etal_2009.pdf|Working Paper]]

===The Effect of Leverage on Financial Markets===
'''Abstract''' When people get excited about their prospects on the stock market, they borrow money from the bank to invest. This leverage effectively couples the bank to the stock market. Thus, interest rates determine demand for stock, and demand for stock can determine interest rates. Does this interplay cause traders to naturally find a stable balance of leverage and aggressiveness? How do the behavioral traits of traders influence the stability of these interactions? Are there regulatory behaviors, such as limiting leverage or slowing margin calls, that would contribute to the overall health of the market? We present an economy consisting of a banking sector and an equity market, with traders transferring money between the two. Using an agent-based model, we will be able to examine how leverage couples the bank to the equities market. Furthermore, we can explore how different leverage strategies effects the stability of these markets.

'''By''' [[Nathan Hodas]], [[Jacopo Tagliabue]], [[Martin Schmidt]], and [[Jeremy Barofsky]]
* [[Media:MarketSimulationWorkingPaper.pdf|Working Paper]]

===Creativity, Learning and Risk Orientation===
'''Abstract''' Haven't we all come up with great ideas and later found that others have already done that? Don't we feel excited and potentially more creative when we learn newer concepts relevant to our interests? We all see highly creative people having diverse interests and want to emulate their creative success, however, it is difficult to open up to newer experiences just like that . . . and why should we unless there is a strong link between willingness to talk to strangers and being able to come up with a grand theory in physics? The enclosed working paper attempts to show that a single congitive mechanism is responsible for individual's learning and creative capacities: Creativity is just an internally driven learning mechanism. The paper shows that creativity is directly related to individual's openness to new expeirences and therefore learning, creativity and openess to new situations all connect together to define an individuals capacity for original thinking. A basic model in Netlogo tries to emulate the framework proposed in the paper.

'''By''' [[Murad Mithani]]
* [[Media:Mithani - Creativity Learning and Risk.pdf|Working Paper]]
* [[Media:Mithani - Creativity Learning and Risk.nlogo|Netlogo based Model]]

=== Revolutionary Fervor as Contagion: A Network Model of Rebellion===
'''Abstract''' In this project, we model revolutionary activism on networks. Revolutionary fervor spreads through the network like a contagion, but depends on the revolutionary thresholds and grievance levels of each node. We provide for nodal removal and rewiring to better capture the underlying dynamics that drive revolutions and endogenize regime legitimacy. Our model deviates from existing work insofar as we arrange actors along a continuum, allowing for counter-revolutionaries and police to eventually be coopted into the revolution. This is a crucial condition for successful revolutions, which is too often gainsaid. Ultimately, we find that initial network structure has a negligible effect on long-run dynamics and that under many parameter settings the system equilibrates to a steady state after a transitory period and phase transition.

'''By''' [[Elliot Martin]], [[Andrew Berdahl]], [[Trevor Johnston]], [[Eric Kasper]], and [[Mahyar Malekpour]]
* [[Media:Revolutions.pdf|Final Paper]]

===Modeling the Impact of Gender Imbalance on Marriage Markets===
'''Abstract'''. This project examines the effects of gender imbalances in marriage markets. Examples of populations with gender imbalance are given, the effects of these demographics are explored, and recommendations for further research are given.

'''By''' [[Dave Brooks]], [[Wendy Ham]], [[Nathan Hodas]], [[Brian Hollar]], [[Liliana Salvador]], and [[Guimei Zhu]]

* [[Media:Bjh_marriage_market.pdf|Working Paper]]
* [[Media:Bjh_marriage_market.nlogo|NetLogo Model]]

===Guns, Germs, and Steel: Guns, Germs, and Steel not included===
'''Abstract''' Jared Diamond's (1997) argument that geographic topology played a central role in the timing and development of variable settlement and civilization sizes presents a testable hypothesis using agent based simulation. We examine early hunter-gatherer population dynamics on a global scale. Using recognized models for agricultural adoption, we can observed the transition from hunter-gatherer to agriculture dominated society. As of now, it certainly seems that geography played a major role in the spread of ideas and technology in pre-history. Maybe the best way to go from here is to have lunch with Jared Diamond.

'''By''' [[Nathan Hodas]],[[Randy Haas]], and [[Alexander Mikheyev]]
*[[Media:GGSWriteUp.pdf|Working Paper]]

File:GGSWriteUp.pdf

2009-09-02T06:17:32Z

Nhodas:

CSSS 2009 Santa Fe-Final Papers

2009-09-01T23:04:52Z

Nhodas: /* The Effect of Leverage on Financial Markets */

CSSS 2009 Santa Fe-Final Papers

2009-09-01T22:04:16Z

Nhodas:

{{CSSS 2009 Santa Fe}}

===The Effect of Gossip on Social Networks===
'''Summary''': In this project we look at the effects of the spread of gossip (defined as information passed between two individuals A and B about an individual C who is not present) on social network structure.

'''By''' ([http://www.santafe.edu/events/workshops/index.php/Modeling_gossip_networks Group Page]): [[Allison Shaw]], [[Chang Yu]], [[Dave Brooks|David Brooks]], [[Milena Tsvetkova]], [[Roozbeh Daneshvar]]

* [[Media:Gossip.pdf|Final Paper]]

===Deconstructing CSSS09 Social Network===
'''Summary''': CSSS09 is a group of people interested in complex systems who are randomly chosen to attend the one month summer school. The international and interdisciplinary group spends four weeks together learning, discussing and working on projects related to complex systems. We are analyzing the social interaction of this network and the network change over time.

'''By''' ([[CSSS 09 Social Network|Group Page]]):
[[Margreth Keiler]], [[Murad Mithani]], [[Roozbeh Daneshvar]], [[Wendy Ham]]

* [[Media:Network_CSSS_2009.pdf|Final Paper]]

===The Effect of Disaggregation on Infection Spreading in a social network: 'More' may not be 'Merrier'===
'''Summary''': This project analyzes the dynamics of infection spreading in the disaggregated framework of a social network using prevalence data for different countries.

'''By''':
[[Varsha Kulkarni]]

* [[Media: Disaggregation_effect_paper.pdf|Final Paper]]

===Mom made me do it: Division of labor via maternal effects===
'''Summary''': We explore an alternative to cooperation for the evolution of division of labor (maternal manipulation) by means of an individual-based model.

'''By''':
[[Mauricio Gonzalez-Forero]], [[Mareen Hofmann]]

* [[Media: manipulation_19_08_2009.pdf|Final Paper]]

===Foraging on the Move===
'''Summary''':In this project we develop a model for organisms that forage in groups while migrating (e.g. caribou, wildebeest), to understand how individuals should balance foraging and flocking behaviors.

'''By''' ([http://www.santafe.edu/events/workshops/index.php/Foraging_on_the_move Group Page]): [[Allison Shaw]], [[Andrew Berdahl]], [[Kathrine Behrman|Kate Behrman]], [[Liliana Salvador]], [[Steven Lade]]

* [[Media:Foraging.pdf|Final Paper]]

===Approaches to Panarchy===
'''Summary''': Panarchy refers to a nested set of interacting dynamical systems, each one cycling over a hierarchy of scales in time and space. This concept provides a framework for thinking about complex systems in ecology, economics, sociology, etc. We consider novel examples and applications of panarchy.

'''By''': [[Barbara Bauer]], [[Andrew Noble]], [[Damian Winters]]

* [[Media:WaterWheel.pdf|Final Paper2]]

===Spiking Neural Networks on the Cusp of Chaos: Initial Report===
'''Summary''': Spiking neural networks, even of small order, show great variability in their behavioral patterns. This paper is an initial look at how structure of small spiking networks influence their measured behavior over time. Of particular interest are networks showing non-periodic behavior, one of the tell-tale indicators of chaos in the underlying dynamics.

'''By''': [[Watson|R. Watson]]

* [[Media:SpikingNetworksWatson2009.pdf|Final Paper]]

===Terrorism: Radicalization Mechanism and Spread Control===
'''Summary''': Preventing the spread of radical ideologies requires models capable of identifying areas and agents before occurrence of terrorist act. We develop a model that captures a radicalization mechanism through several intermediate stages of individuals. We show how our model can combat the development of terrorist networks even with limited information on a target terrorist network.

'''By'''([http://www.santafe.edu/events/workshops/index.php/Radicalization Group page]): [[ Alhaji Cherif]], [[ Hirotoshi Yoshioka]], [[Wei Ni]], [[Prasanta Bose]]

* [[Media:TerrorismWorkingPaper.pdf|Final Paper]]

===Acupuncture Points Complex Networks in Human Body===
'''Summary''': The therapeutic properties of the points of the fourteen meridians are generalized on the basis of the meridian is amenable to treatment. We constructed an acupuncture complex network of a portion of the body depending on different kinds of illness to figure out interactions between acupuncture points. We use 44 common illnesses such as common cold, headache as the portion to do the research. The constructed network contains a large component of 70 nodes out of 770 acupuncture points around the body.

'''By''' : [[Guimei Zhu]], [[Dave Brooks]], [[Brian Hollar]],

* [[Media:Acupuncture Points Complex Networks in Human Body.pdf|working Paper]]

===The Roundtable - An Agent-Based Model of Conversation Dynamics===
'''Abstract'''. We present and analyze an agent-based model of conversation dynamics. The model develops from intuitive assumptions derived from experimental evidence, it abstracts from conversation content and semantics while including topological and psychological information, and is driven by stochastic dynamics. The model exhibits rich behavior and can capture many aspects of real-life conversations. Its potential generalizations, including individual preferences, memory effects and more complex topologies, may find useful applications in other fields of research where dynamically-interacting and networked agents play a fundamental role.

'''By''' [[Lucas Lacasa]], [[Massimo Mastrangeli]] and [[Martin Schmidt]]

* [[Media:The Roundtable.pdf|Working Paper]]

===Exploring source-sink dynamics of pre-vaccination measles epidemics using a spectral formulation of Granger causality===
We present a method for determining spatial contact networks from time series data of disease transmission.

'''By''' [[Kathrine Behrman]], [[Alexander Mikheyev]] and [[Erin Taylor]]

* [[Media:SpatialContactNetwork.pdf|Final Paper]]

===Computational investigation of dynamic response of small networks: a research proposal===
'''Abstract''' We propose to determine the reaction of every network of
3-5 nodes to a standardised signal. We see the networks as cellular signalling
modules, but other interpretations may apply as well.
We argue that the results of this project may shed light on evolutionary properties
of small molecular networks. The computational
framework we propose to use is ''probabilistic model checking'', which guarantees
a mathematically sound and completely automatic classification of responses.
We invite the reader to undertake the research, with our assistance if required.

'''By''' [[Rosemary Braun]], [[Marek Kwiatkowski]] and [[Alexander Mikheyev]]

* [[Media:Small_graphs.pdf|The proposal]]

===Scalable Efficient Agent Based Models===
'''Abstract''' Agent based models (ABMs) are characterized by purposive intelligent agents which interact to yield complex emergent behaviors. While each agent operates with an autonomous localized set of rules, these rules typically involve interactions among agents and with the agents’ environment, presenting challenges when attempting to simulate very large populations of agents. At the same time, the scaling properties of complex adaptive systems are such that it is important to study the properties of an ABM over wide ranges of population size. This is because the emergent properties of a system typically change with scope and scale: mammalian basal metabolic rate scales sublinearly with animal mass, and incidence of crime in a city scales superlinearly with city population size. The attached Abstract of Work in Progress describes work conducted heretofore in deploying a spatial ABM to a distributed-memory computational cluster, along with planned next steps.

'''By''' [[Matt McMahon]]

*[[Media:ScalableEfficientAgentBasedModels.pdf|Abstract of Work in Progress]]

===1,2,3, language! Building the phylogenetric tree of languages with numbers===
'''Abstract''' In this paper we make use of bioinformatics tools to build up the phylogenetic tree of languages. We had access to a large dataset gathering the numbers one to ten in over five thousand languages. In a first step, for each language we have concatenated each of the ten numbers in a string. After defining a mapping between the 26-letter alphabet and the DNA-like codons, we make use of a global alignment method to calculate the distance between pairs of strings, i.e. between languages. We finally generate the distance matrix and its associated phylogenetic tree. Specifically, we have used this method to generate the phylogenetic tree of indoeuropean languages. Despite the small size of the dataset (only ten words per language), preliminary results perfectly match the state of the art. We finally discuss some potential applications and future work, on relation to culture-based concepts such as trading or spreading of culture.

'''By''' [[Andrew Berdahl]] and [[Lucas Lacasa]]

*[[Media:phylotree2.pdf|working paper]]

===Crossover phenomenon in the performance of an Internet search engine===
'''Abstract''' In this work we explore the ability of Google search engine to find results for random N-letter
strings. These random strings, dense over the set of possible N-letter words, address the existence of
typos, acronyms, and other words without semantic meaning. Interestingly, we find that the probability
of finding such strings sharply drops from one to zero at Nc = 6. The behavior of such order parameter
suggests the presence of a transition-like phenomenon in the geometry of the search space. Furthermore, we
define a susceptibility-like parameter which reaches a peaked maximum in the neighborhood, suggesting
the presence of criticality. We finally speculate on the possible connections to Ramsey theory.

'''By''' [[Lucas Lacasa]], [[Jacopo Tagliabue]] and [[Andrew Berdahl]]

*[[Media: report_google2.pdf|working paper]]

===Agent-Based Modeling of MEMS Fluidic Self-assembly===
'''Abstract'''. The dynamics of fluidic self-assembly (FSA) of Micro Electro-Mechanical Systems (MEMS), recently demonstrated by experimental data, is modeled using interacting software agents. This method enables realistic simulations of FSA dynamics and represents a significant step ahead in the state-of-the-art of this field.

'''By''' [[Massimo Mastrangeli]]

* [[Media:ABMxFSA|Working Paper]]

===The Individual and the Empire:The Effects of Agent-Based Emigration Behavior on the Emergence of Settlement Size Inequality in the Titicaca Basin, Bolivia and Peru===
'''Abstract'''. Approximately 1000 years ago, from a relatively homogeneous distribution of small, sedentary villages in the Andean highlands, emerged a state-level society with settlements ranging in size from small hamlets to a large urban center of some 50,000 individuals. This working paper explores a possible mechanism that caused vast inequalities in the distribution of settlement sizes in time and space in the Titicaca Basin. Using agent-based modeling, we show that the cumulative effects of simple, individual-based migration optimization behavior can create a settlement rank-size distribution with the same formal qualities as that observed during the Tiwanaku Empire's apex. The model's general applicability is explored, improvements are suggested, and future directions proposed.

'''By''' [[Randy Haas]], [[Jacopo Tagliabue]], and [[Jeremy Barofsky]]

* [[Media:Haas_etal_2009.pdf|Working Paper]]

===The Effect of Leverage on Financial Markets===
'''Abstract''' When people get excited about their prospects on the stock market, they borrow money from the bank to invest. This leverage effectively couples the bank to the stock market. Thus, interest rates determine demand for stock, and demand for stock can determine interest rates. Does this interplay cause traders to naturally find a stable balance of leverage and aggressiveness? How do the behavioral traits of traders influence the stability of these interactions? Are there regulatory behaviors, such as limiting leverage or slowing margin calls, that would contribute to the overall health of the market? We present an economy consisting of a banking sector and an equity market, with traders transferring money between the two. Using an agent-based model, we will be able to examine how leverage couples the bank to the equities market. Furthermore, we can explore how different leverage strategies effects the stability of these markets.
'''By''' [[Nathan Hodas]], [[Jacopo Tagliabue]], [[Martin Schmidt]], and [[Jeremy Barofsky]]
* [[Media:MarketSimulationWorkingPaper.pdf|Working Paper]]

CSSS 2009 Santa Fe-Final Papers

2009-09-01T22:02:29Z

Nhodas:

{{CSSS 2009 Santa Fe}}

===The Effect of Gossip on Social Networks===
'''Summary''': In this project we look at the effects of the spread of gossip (defined as information passed between two individuals A and B about an individual C who is not present) on social network structure.

'''By''' ([http://www.santafe.edu/events/workshops/index.php/Modeling_gossip_networks Group Page]): [[Allison Shaw]], [[Chang Yu]], [[Dave Brooks|David Brooks]], [[Milena Tsvetkova]], [[Roozbeh Daneshvar]]

* [[Media:Gossip.pdf|Final Paper]]

===Deconstructing CSSS09 Social Network===
'''Summary''': CSSS09 is a group of people interested in complex systems who are randomly chosen to attend the one month summer school. The international and interdisciplinary group spends four weeks together learning, discussing and working on projects related to complex systems. We are analyzing the social interaction of this network and the network change over time.

'''By''' ([[CSSS 09 Social Network|Group Page]]):
[[Margreth Keiler]], [[Murad Mithani]], [[Roozbeh Daneshvar]], [[Wendy Ham]]

* [[Media:Network_CSSS_2009.pdf|Final Paper]]

===The Effect of Disaggregation on Infection Spreading in a social network: 'More' may not be 'Merrier'===
'''Summary''': This project analyzes the dynamics of infection spreading in the disaggregated framework of a social network using prevalence data for different countries.

'''By''':
[[Varsha Kulkarni]]

* [[Media: Disaggregation_effect_paper.pdf|Final Paper]]

===Mom made me do it: Division of labor via maternal effects===
'''Summary''': We explore an alternative to cooperation for the evolution of division of labor (maternal manipulation) by means of an individual-based model.

'''By''':
[[Mauricio Gonzalez-Forero]], [[Mareen Hofmann]]

* [[Media: manipulation_19_08_2009.pdf|Final Paper]]

===Foraging on the Move===
'''Summary''':In this project we develop a model for organisms that forage in groups while migrating (e.g. caribou, wildebeest), to understand how individuals should balance foraging and flocking behaviors.

'''By''' ([http://www.santafe.edu/events/workshops/index.php/Foraging_on_the_move Group Page]): [[Allison Shaw]], [[Andrew Berdahl]], [[Kathrine Behrman|Kate Behrman]], [[Liliana Salvador]], [[Steven Lade]]

* [[Media:Foraging.pdf|Final Paper]]

===Approaches to Panarchy===
'''Summary''': Panarchy refers to a nested set of interacting dynamical systems, each one cycling over a hierarchy of scales in time and space. This concept provides a framework for thinking about complex systems in ecology, economics, sociology, etc. We consider novel examples and applications of panarchy.

'''By''': [[Barbara Bauer]], [[Andrew Noble]], [[Damian Winters]]

* [[Media:WaterWheel.pdf|Final Paper2]]

===Spiking Neural Networks on the Cusp of Chaos: Initial Report===
'''Summary''': Spiking neural networks, even of small order, show great variability in their behavioral patterns. This paper is an initial look at how structure of small spiking networks influence their measured behavior over time. Of particular interest are networks showing non-periodic behavior, one of the tell-tale indicators of chaos in the underlying dynamics.

'''By''': [[Watson|R. Watson]]

* [[Media:SpikingNetworksWatson2009.pdf|Final Paper]]

===Terrorism: Radicalization Mechanism and Spread Control===
'''Summary''': Preventing the spread of radical ideologies requires models capable of identifying areas and agents before occurrence of terrorist act. We develop a model that captures a radicalization mechanism through several intermediate stages of individuals. We show how our model can combat the development of terrorist networks even with limited information on a target terrorist network.

'''By'''([http://www.santafe.edu/events/workshops/index.php/Radicalization Group page]): [[ Alhaji Cherif]], [[ Hirotoshi Yoshioka]], [[Wei Ni]], [[Prasanta Bose]]

* [[Media:TerrorismWorkingPaper.pdf|Final Paper]]

===Acupuncture Points Complex Networks in Human Body===
'''Summary''': The therapeutic properties of the points of the fourteen meridians are generalized on the basis of the meridian is amenable to treatment. We constructed an acupuncture complex network of a portion of the body depending on different kinds of illness to figure out interactions between acupuncture points. We use 44 common illnesses such as common cold, headache as the portion to do the research. The constructed network contains a large component of 70 nodes out of 770 acupuncture points around the body.

'''By''' : [[Guimei Zhu]], [[Dave Brooks]], [[Brian Hollar]],

* [[Media:Acupuncture Points Complex Networks in Human Body.pdf|working Paper]]

===The Roundtable - An Agent-Based Model of Conversation Dynamics===
'''Abstract'''. We present and analyze an agent-based model of conversation dynamics. The model develops from intuitive assumptions derived from experimental evidence, it abstracts from conversation content and semantics while including topological and psychological information, and is driven by stochastic dynamics. The model exhibits rich behavior and can capture many aspects of real-life conversations. Its potential generalizations, including individual preferences, memory effects and more complex topologies, may find useful applications in other fields of research where dynamically-interacting and networked agents play a fundamental role.

'''By''' [[Lucas Lacasa]], [[Massimo Mastrangeli]] and [[Martin Schmidt]]

* [[Media:The Roundtable.pdf|Working Paper]]

===Exploring source-sink dynamics of pre-vaccination measles epidemics using a spectral formulation of Granger causality===
We present a method for determining spatial contact networks from time series data of disease transmission.

'''By''' [[Kathrine Behrman]], [[Alexander Mikheyev]] and [[Erin Taylor]]

* [[Media:SpatialContactNetwork.pdf|Final Paper]]

===Computational investigation of dynamic response of small networks: a research proposal===
'''Abstract''' We propose to determine the reaction of every network of
3-5 nodes to a standardised signal. We see the networks as cellular signalling
modules, but other interpretations may apply as well.
We argue that the results of this project may shed light on evolutionary properties
of small molecular networks. The computational
framework we propose to use is ''probabilistic model checking'', which guarantees
a mathematically sound and completely automatic classification of responses.
We invite the reader to undertake the research, with our assistance if required.

'''By''' [[Rosemary Braun]], [[Marek Kwiatkowski]] and [[Alexander Mikheyev]]

* [[Media:Small_graphs.pdf|The proposal]]

===Scalable Efficient Agent Based Models===
'''Abstract''' Agent based models (ABMs) are characterized by purposive intelligent agents which interact to yield complex emergent behaviors. While each agent operates with an autonomous localized set of rules, these rules typically involve interactions among agents and with the agents’ environment, presenting challenges when attempting to simulate very large populations of agents. At the same time, the scaling properties of complex adaptive systems are such that it is important to study the properties of an ABM over wide ranges of population size. This is because the emergent properties of a system typically change with scope and scale: mammalian basal metabolic rate scales sublinearly with animal mass, and incidence of crime in a city scales superlinearly with city population size. The attached Abstract of Work in Progress describes work conducted heretofore in deploying a spatial ABM to a distributed-memory computational cluster, along with planned next steps.

'''By''' [[Matt McMahon]]

*[[Media:ScalableEfficientAgentBasedModels.pdf|Abstract of Work in Progress]]

===1,2,3, language! Building the phylogenetric tree of languages with numbers===
'''Abstract''' In this paper we make use of bioinformatics tools to build up the phylogenetic tree of languages. We had access to a large dataset gathering the numbers one to ten in over five thousand languages. In a first step, for each language we have concatenated each of the ten numbers in a string. After defining a mapping between the 26-letter alphabet and the DNA-like codons, we make use of a global alignment method to calculate the distance between pairs of strings, i.e. between languages. We finally generate the distance matrix and its associated phylogenetic tree. Specifically, we have used this method to generate the phylogenetic tree of indoeuropean languages. Despite the small size of the dataset (only ten words per language), preliminary results perfectly match the state of the art. We finally discuss some potential applications and future work, on relation to culture-based concepts such as trading or spreading of culture.

'''By''' [[Andrew Berdahl]] and [[Lucas Lacasa]]

*[[Media:phylotree2.pdf|working paper]]

===Crossover phenomenon in the performance of an Internet search engine===
'''Abstract''' In this work we explore the ability of Google search engine to find results for random N-letter
strings. These random strings, dense over the set of possible N-letter words, address the existence of
typos, acronyms, and other words without semantic meaning. Interestingly, we find that the probability
of finding such strings sharply drops from one to zero at Nc = 6. The behavior of such order parameter
suggests the presence of a transition-like phenomenon in the geometry of the search space. Furthermore, we
define a susceptibility-like parameter which reaches a peaked maximum in the neighborhood, suggesting
the presence of criticality. We finally speculate on the possible connections to Ramsey theory.

'''By''' [[Lucas Lacasa]], [[Jacopo Tagliabue]] and [[Andrew Berdahl]]

*[[Media: report_google2.pdf|working paper]]

===Agent-Based Modeling of MEMS Fluidic Self-assembly===
'''Abstract'''. The dynamics of fluidic self-assembly (FSA) of Micro Electro-Mechanical Systems (MEMS), recently demonstrated by experimental data, is modeled using interacting software agents. This method enables realistic simulations of FSA dynamics and represents a significant step ahead in the state-of-the-art of this field.

'''By''' [[Massimo Mastrangeli]]

* [[Media:ABMxFSA|Working Paper]]

===The Individual and the Empire:The Effects of Agent-Based Emigration Behavior on the Emergence of Settlement Size Inequality in the Titicaca Basin, Bolivia and Peru===
'''Abstract'''. Approximately 1000 years ago, from a relatively homogeneous distribution of small, sedentary villages in the Andean highlands, emerged a state-level society with settlements ranging in size from small hamlets to a large urban center of some 50,000 individuals. This working paper explores a possible mechanism that caused vast inequalities in the distribution of settlement sizes in time and space in the Titicaca Basin. Using agent-based modeling, we show that the cumulative effects of simple, individual-based migration optimization behavior can create a settlement rank-size distribution with the same formal qualities as that observed during the Tiwanaku Empire's apex. The model's general applicability is explored, improvements are suggested, and future directions proposed.

'''By''' [[Randy Haas]], [[Jacopo Tagliabue]], and [[Jeremy Barofsky]]

* [[Media:Haas_etal_2009.pdf|Working Paper]]

===The Effect of Leverage on Financial Markets===
'''Abstract''' When people get excited about their prospects on the stock market, they borrow money from the bank to invest. This leverage effectively couples the bank to the stock market. Thus, interest rates determine demand for stock, and demand for stock can determine interest rates. Does this interplay cause traders to naturally find a stable balance of leverage and aggressiveness? How do the behavioral traits of traders influence the stability of these interactions? Are there regulatory behaviors, such as limiting leverage or slowing margin calls, that would contribute to the overall health of the market? We present an economy consisting of a banking sector and an equity market, with traders transferring money between the two. Using an agent-based model, we will be able to examine how leverage couples the bank to the equities market. Furthermore, we can explore how different leverage strategies effects the stability of these markets.
'''By''' [[Nathan Hodas]], [[Jacopo Tagliabue]], [[Martin Schmidt]], and [[Jeremy Barofsky]]
* [[Media:ModelSimulationWorkingPaper.pdf|Working Paper]]

File:MarketSimulationWorkingPaper.pdf

2009-09-01T21:59:51Z

Nhodas:

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-29T05:37:30Z

Nhodas: /* Guns, Germs and Steel: A Simulation */

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

===Problem solving and mating - are they similar?===

The discussions on this project have been moved to a separate page: [[Problem solving]]

=== Interacting distribution networks ===
Moved to its own page: [[Interacting distribution networks]]

===The Effect of Gossip on Social Networks===
Moved to a separate page: [[Modeling gossip networks]]

===Radicalization of Islamic Diasporas and Reactive Control Theoretical Approach===
See [[Radicalization]]

===From Topology to Response===
[[From_Topology_to_Response]]

===[[Spiking Networks on the Cusp of Chaos]]===

Please click the title to be transported to the project page.
===Modeling behaviors between students and teachers===
Update and Details about this project, please click here !
[http://www.santafe.edu/events/workshops/index.php/Modeling_behaviors_student&teacher Modeling behaviors between students and teachers]

===Determining spatial contact networks for pathogen transmission===
Project Page: [[Spatial contact networks]]

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

====Acupuncture/Chinese Alternative Medicine====

Here are some more papers regarding research that has been done on acupuncture. Some network analysis has been done. Very interesting stuff!

[[Media: AcupunctureOverview.pdf|Acupuncture Overview]]: Here is an overview of acupuncture from a journal entitled "Alternative Therapies" in 1998.

[[Media: AcupunctureGraphTheory.pdf| Acupuncture and Graph Theory]]: This paper was written in "Progress in Natural Science" in 2009 which implements the use of graph theory to make a model to understand the effects of acupunture on brain function.

[[Media: AcupunctureFibroblasts.pdf|Body-Wide Cellular Network of Fibroblast Cells]]: A paper relating the study of a body-wide network of fibroblasts to acupuncture. Written in "Histochemistry and Cell Biology" in 2004.

[[Media: AcupunctureNeedleAdmin.pdf|Acupuncture-Psychosocial Context]] And another which studies the effects of the procedure. Written in "Advanced Access Publication" in 2008.

Enjoy! [[Karen Simpson]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

[[Mahyar Malekpour]]: Update June 16, 2009 - Someone asked if there is an application for this. The answer is yes, categorically, any self-organizing system needs synchronization. However, my interest here are visualization and exploration using agent-based tools. I don not intend to develop a solution to this problem, rather build on an existing agent-based model (if there is any) and enhance its capabilities.

[[Massimo Mastrangeli]]: as someone said, there is vast literature on synchronization available; you can for example get a taste in [http://www.amazon.com/SYNC-Emerging-Science-Spontaneous-Order/dp/0786868449 Sync] by Steven Strogatz (also, check out his talk [http://www.ted.com/talks/steven_strogatz_on_sync.html at TED]). I am quite interested in the idea.

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski | Marek]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response | project page]] [[Marek Kwiatkowski | Marek]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

* [[Mauricio Gonzalez-Forero]]: I would like to hear more about this project. Can we meet sometime?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

*[[watson]]: hmmm ... yes. this is exactly what I am talking about. very cool to hear you have some experience with this Massimo. right now i am leaning on going ahead with this project. i think we have serious potential to make impact, elucidate new relationships and phenomena and educate in the process. and i think it could be a ton of fun. here are a couple of links i have found which could be of use:
**[http://fusionanomaly.net/polyrhythms.html this] page talks about history, theory and even mentions chaos.
**[http://web.mit.edu/cjoye/www/music/tabla/ this] is a good source for tabla samples. tabla is one of the simpler devices that has some melodic structure as well as rhythmic structure to it. we could work with others as well... one thought is even just a drum kit of different sounds (rock style).

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and has looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects, too. His papers might be a good starting point.

[[Nathan Hodas]] I'd like to be in on this. I've pondered a good deal about this since reading the book. Maybe we should contact Jared Diamond?

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

[[Margreth Keiler]]: Murad and I had the same idea yesterday, but we thought to make each week a surveys to see how the network change over time and to add also after CSSS surveys. Should we discuss our draft tomorrow at SFI?

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to
‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity
in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to
allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

===Economic Geography in the Lake Titicaca Basin===

Moved to [http://www.santafe.edu/events/workshops/index.php/Economic_Geography_and_State_Emergence Economic Geography and State Emergence]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

[[Image:Theoretical_framework.jpg|480px|thumb|Theoretical_framework]]

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Looking forward to exchanging ideas!

[[Steven Lade]] Hi Almut, As I've said, I think this is well suited to modelling with differential equations. Particularly if, as I assume to be the case, the GIS data comes already in a rectangular grid. Having said that, there are some complementary aspects for which ABM would be well-suited. For instance, following agents as they form streams, or if you were to have a localised thunderstorm. We could possibly do this in parallel and see if they match and/or use each method's particular advantages.

You may be interested in this paper, which I found through the SFI library database: [http://pubs.usgs.gov/sir/2007/5009/pdf/sir_2007-5009.pdf]. I think this one is more complicated though, because they need to consider a three-dimensional water table. More generally, what sort of modelling (if any) do people usually do in these sorts of topics?

[[Karen Simpson]] I am interested in this project! I have studied these concepts in many of my classes. Through past research, I've looked at storm/rain events, and how a large runoff from stormwater causes high contaminant concentrations in streams and rivers. This research was done for urban, forest, and agricultural landuse types.(I will try to find the results of this research soon). Another thing to think about is the time between rain events. A long timespan between rainfall events will cause the soil to become unsaturated, and the next rainfall may have little effect on the stream. I also will not be around much this weekend, so would it be possible to meet sometime tomorrow (Thursday 6/18)?

===Scalable (parallel) Spatial Agent-Based Models===

This project idea is an exploration of what happens to agent-based models “in the large?” For example,
* As the number of interacting agents in a model increases, what happens to the dynamics of the model?
* What happens as the size of the agents’ domain increases (e.g. simulating a neighborhood versus simulating a city or country)
* How do the properties of the model change? Are there scaling laws in effect ?

In order to investigate these issues, we need a scalable simulation, i.e. a parallel implementation of the model that allows us to introduce arbitrarily large numbers of agents. There are many approaches to doing this [lit review needed here!], but for this project, I would like to focus on spatial agent-based models: models where there are N agents who exist in a geographical domain and possess “vision,” where vision can be optical/eye-based, local communications (audible or electromagnetic line of site).
A couple such models which can serve as starting points include the flocking model (aka “boids”) and Epstein’s model of civil violence (or its derivative “Rebellion” model).

The idea is to decompose the spatial domain into independent subdomains, distribute those subdomains to nodes on a compute cluster, amalgamate the results, wash-rinse-repeat. One possible approach is to use an adaptive mesh refinement (AMR) such as those used by engineers for finite element analysis or by physicists in hydrodynamics simulations. One concrete example, using a quad-tree decomposition to keep agent density constant on each processor (and thereby keeping computational load balanced), is as follows:

[[Image:Particle.PNG|thumb|left|An example showing decomposition of a particle system using a quad-tree. Each resulting square has (roughly) the same number of particles in it. Can this approach be used for parallelizing spatial agent-based models ?]]
I have a cluster available for implementation, along with the MPI libraries for parallel programming. Other suggested areas of expertise that would greatly benefit the project include:
Someone interested in evaluating simulation results, who can help ensure that we don’t break the model by decomposing it.
Someone interested in analysis, for exploring the effects of scaling on the model.
Someone interested in high-performance computing, to help with programming (probably c/c++ with MPI)

From talking to folks in our class, some other benefits of the approach include
* improving running time for very-long-running simulations
* aerospace applications—decomposing the National Air Space into computationally tractable subdomains for modeling or real-world purposes.
* Applying the decomposition technique to other model domains. For example, can a similar technique be used to decompose a social network, especially if a single model has both geographic spatial domains and also network domains?

Other approaches suggested by classmates have included implementation on GPUs (graphics processors used for general purpose computation) and sticking to an SMP implementation (multicore workstations with shared memory--simpler implementation/perhaps not as scalable), versus a distributed-memory cluster. I welcome further ideas that might help kick-start this zany scheme.

[[Steven Lade]] What I'm about to say seems kind of obvious, and I'm not sure it helps you at all, but I can't help but say that if your 'averaged behaviour' converges for very large numbers of agents, you'd in effect be modelling some partial differential equation.

[[Matt McMahon]] Thanks, Steven. Not obvious to me though ... Can you elucidate?

[[Steven Lade]] It seems that as you reach a large number of agents, and your grid becomes small, you'd most likely reach some partial differential equation in the density of agents. Say a diffusion equation. Or a Navier-Stokes (fluid flow) equation. Not sure how easy it would be to derive, but this would be my intuition. It would be easiest for local interactions only (i.e. some radius which you could let approach zero) but non-local interactions might be possible too. It would of course all depend on the agent rules you use. If you're lucky one might even be able to derive some analytical results for special cases. If you want to chat more, find me in person. (Anyone: does this allnmake sense?)

=== Resilience to invaders in social systems ===

A piece of anecdata from my organizing days: the effect of an external organizer coming to help on a local campaign had one of two -- very different -- effects: either further coalescing the local campaign, or fragmenting it.

I'm curious how well social structures tolerate interlopers and what drives their resilience.

Possible metaphors/methods which could be useful:
* An agent-based models of the connectivity of the underlying social structure & reaction to interloper?
* Analogizing to food-web/ecology with the interloper as an invasive species?
* Analogizing the interloper to a crystal defect?

BUT I have no idea 1) how to parameterize this and 2) whether there are data (of any sort -- eg resilience to colonists/prophets/carpetbaggers) to which the model could be compared for sanity-checking.

I know '''nothing''' about sociology & related fields, so maybe this is a well-studied problem. Or an ill-posed problem. Or maybe it's not a problem at all. In any event, I'd be curious to hear other's thoughts.

==Final Projects==

Please place your final project ideas here: details should include clear and objective outlines.

===The Effect of Gossip on Social Networks===
In this project we look at the effects of gossip spread on social network structure. We define gossip as information passed between two individuals A and B about an individual C who is not present, which has the potential to affect the strengths of all three relationships A-B, B-C, and A-C. This work is novel in two respects: first, there is no theoretical work on how network structure changes when information passing through a network has the potential to affect edges not in the direct path, and second while past studies have looked at how network structure affects gossip spread, there is no work done on how gossip spread affects network structure.

Page: [[Modeling gossip networks]]

Members:
* [[Allison Shaw]]
* [[Chang Yu]]
* [[Dave Brooks]]
* [[Milena Tsvetkova]]
* [[Roozbeh Daneshvar ]]

===1,2,3, language!===

In a nutshell:
In this project we will make use of information theoretic measures of similarity between data sets, such as mutual information
or more specifically some global allignment methods coming from evolutionary biology to build up a distance matrix between languages.
The data under study are simply the numbers 1,2,3...,10, for which we have access to a massive dataset that enumerates the spelling of the first ten numbers in more than 4,000 languages. We will finally derive the phylogenetic tree of languages, and compare it with the state of the art.

Members:
* [[Andrew Berdahl]]
* [[Lucas Lacasa]]

===Social mitosis in group conversations: a cooperative phenomenon approach===

In a nutshell:
When you participate in a conversation, you typically expect to (i) actively participate and (ii) be confortable in it. These arguments somewhat put some constraints in the number of persons attending the same conversation. In other words, when people are forced to stay in the same confined space, they tend to undertake a conversation, however if too many people are present, the conversation rapidly splits in two, three... some nucleation phenomenon takes place. In this project we approach this subject from a complex systems point of view and want to understand if the 'conversation mitosis' is a collective phenomenon, much in the vein of a symmetry-breaking phenomenon in statistical physics. We will develop an agent based model that captures the essential mechanisms of conversation dynamics and will characterize such behaviors. Analytical developments will also be addressed. Finally, we will compare our analytical/numerical results with empirical data gathered through e-mail surveys.

Members:
* [[Massimo Mastrangeli]]
* [[Martin Schmidt]]
* [[Lucas Lacasa]]

===Modeling mesoscopic sequential self-assembly===

One of the reasons for the huge success of microelectronics is the ability to produce very large amounts of devices at very small price. Anyway, a large part of the final price of electronic devices is due to assembly and packaging issues. The standard procedure to package microdevices is by robotic or even manual manipulation, which while satisfactory for large sizes becomes inefficient and even practically incontrollable below the millimeter scale. Moreover, when dealing with very large amounts of components the task becomes time-consuming and this expensive.

Self-assembly techniques have the potential to boost electronic assembly by their intrinsic massive parallelism and advantageous scaling properties. Particularly, self-assembly performed in liquid environment has gained momentum by showing interesting performance. Anyway, the analytic modeling of the dynamics of this process is still limited and not capable of capturing the details of the stochastic dynamics of self-assembly. In this project, I want to simulate the dynamics 2D and 3D sequential self-assembly with agent-based models. This framework, never so far applied to this task, may help sheding light on the role of important parameters of the process such as dimensions of the assembly space, redundance of components, viscosity of the fluid carrier.

[[Massimo Mastrangeli]]

===Percolation-like phenomenon in the Google search engine===

In a nutshell:
Type a (short) random string of letters in Google. This mimics the effect of mispelling words, 'typos'. Surprisingly, you will find a non-null amount of results: the probability of finding such a word, even if it's a random string without a semantic meaning, is non-null, since (i) someone could have already 'invented it' (acronym or so), (ii) someone could have mispelled a word (committed a typographic error) in his/her website/blog etc. But repeat the procedure with larger strings, and look how the number of results rapidly drops to zero... Is this a phase transition? Can we characterize such phenomenon? What are the relations between language-like properties and this behavior? What information can we extract? In this project we will endeavor such questions, programming automatic queries to google of randomly-generated strings and relating the system's behavior to some collective phenomena such as Percolation.

Members:
* [[Jacopo Tagliabue]]
* [[Andrew Berdahl]]
* [[Lucas Lacasa]]

===Competitive spatial network growth===
Many large-scale aggregate networks are actually composed of several essentially independent subnetworks, each of which takes into account the other agents' actions. While traditional optimization methods yield insight into the most efficient network structures to satisfy a fixed objective, the presence of several overlapping and evolving networks may change the optimal strategy or create niches for otherwise suboptimal strategies. In this project we develop an agent-based network growth model to simulate competitive airline network growth, studying the effects of the demand distribution, entry time, and number of agents on the success and network structure of the agents.

[[Interacting distribution networks]]

Members:
* [[Brendan Colloran]]
* [[Caroline Farrior]]
* [[Daniel Wuellner]]
* [[Michael Schultz]]

===Spectral clustering of gene expression===

1. Can we differentiate between genes involved in separate biological functions (ie pathways) using spectral clustering?

2. If so, can we use this method to detect the genes activated in cancer?

Members:
* [[Rosemary Braun]]
* [[Corinne Teeter]]
* [[Elliot Martin]]
* [[Eric Kasper]]

===From Topology to Response===
[[From_Topology_to_Response]]

Members:
* [[Marek Kwiatkowski]]
* [[Rosemary Braun]]
* [[Alexander Mikheyev | Sasha Mikheyev]]

===Foraging on the move===
Many animals (e.g. caribou, wildebeest) forage in groups while moving from one location to another. This means individuals have to simultaneously balance several demands: finding the best resources, maintaining the cohesion of the group, and migration in a certain direction. While there is a vast literature on both flocking and optimal foraging, there has been no work done to understand how animals should trade off the decision to flock or forage (since it is difficult to do both simultaneously) during migration. We develop an individual-based model to address this, and implement a genetic algorithm to find the best decision-rule for switching between foraging and flocking, under a variety of conditions.

Page: [[Foraging on the move]]

Members:
* [[Allison Shaw]]
* [[Andrew Berdahl]]
* [[Kathrine Behrman|Kate Behrman]]
* [[Liliana Salvador]]
* [[Steven Lade]]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Members:
* [[Steven Lade]]
* [[Karen Simpson]]
* [[Almut Brunner]]

Update and own page following soon!

===Creative Process===
The project attempts to model the generation of ideas in the subconscious as a random combination of existing concepts (reflected as words) and their selection (reflected as variance). The selection filter determines the quality and quantity of ideas that rise to the conscious. Although the complete model may not be in place by the end of the week, the presentation will display a basic version of the final (and hopefully publishable) paper.

* [[Murad Mithani]]

===A Markov Model of Elite Factionalization===

Authoritarian regimes fracture when elites within the ruling coalition, which buttresses the dictator, defect. Consequently, regime change crucially depends on elite competition and coordination. Previous work has explored this topic through conventional formal models that make exacting informational and cognitive demands on agents. In contrast, this model will attempt to replicate these findings, while exploring additional dynamics and emergent behavior, by embedding boundedly rational agents in a Markovian system. Rather than assume hyper rational actors, capable of solving difficult dynamic programming problems, I assume that elites use relatively simple heuristics to navigate a stochastic environment.

* [[Trevor Johnston]]

===Radicalization Network (RadNet): Spread Mechanisms and Control===
Spread of radical ideologies is a key source of terrorist activities. Coordination and management of resources for preemptive control of such activities require network models that enable predictive identification of areas and agents before occurrence of terrorist action. Our RadNet project develops a layered network model where the dynamics of radicalization are used to define an evolving radical network layer that exploit an underlying social network layer. We develop quantitative models for change of state of nodes (representative of individuals in the social network) that capture progress of radicalization through intermediate stages of general, susceptible, indoctrinated, and radical. We analyze the model and its robustness to control actions by modeling in netlogo. The models developed and analyzed in RadNet, builds on insights from the contagion model, and introduces novel modular layered network approach to capture exploitation of social network for spread of ideology and control networks for monitoring and preemptive actions on the evolving radicalization network.

Members:
* [[Alhaji Cherif]]
* [[Prasanta Bose]]
* [[Hirotoshi Yoshioka]]
* [[Wei Ni]]
See [[Radicalization]]

===Guns, Germs and Steel: A Simulation===
[[Image:GGSscreencapture.jpg|400px|thumb|hunter-gatherer behavior of early humans]]
We intend to model the spread of early human populations. In his popular work, Guns Germs, and Steel, Jared Diamond argues that the differential concentration of wealth and people is a result of geography and historical contingencies. Using net logo we will model the dynamics of hunter-gatherer spread and the emergence of early agriculture. Global bioproductivity maps will be used to quantitatively represent resource habitats.

Members:
* [[Randy Haas]]
* [[Nathan Hodas]]
* [[Alexander Mikheyev]]

===Spiking Networks on the Cusp of Chaos===

The objective of this project is to investigate what small networks of idealized spiking neurons look and behave like at the edge of chaos. Specifically "edge of chaos" here means that special boundary area between the regions where we observe orderly periodic behavior on one side and chaotic non-periodic behavior on the other. (both have been observed previously in this dynamical system)

More details here: [[Spiking Networks on the Cusp of Chaos]]

===Game theoretic strategies in bike racing ===

Cooperation is often a paradoxical component of human groups. Seemingly altruistic behavior can often be explained by selfish strategies. The cultivation of cooperation in humans is crucial for solving the world's pressing problems such as climate change and international disputes. Apparent cooperation often shows up naturally in sports and is often attributed to social norms. Here we intend to set up the basic elements of a bike race where each individual is motivated to win, but can benefit through cooperation with other riders. Each rider will be given a set of skills and a cooperation strategy. The analysis will include competition among different strategies and an understanding of the emergent level of cooperation from only race environment (no social norms). We hope that the differences in qualitative properties between the modeled race and real races might teach us about the presence of social norms even in these competitive environments.

Members:
* [[Rhonda Hoenigman]]
* [[Joslyn Barnhart]]
* [[Caroline Farrior]]

=======Study of Sustainability of the Companies, Cities and Societies: Towards the Theory of Social Organizations.=======
=======BOEING AS A CASE STUDY===========
The first phase of this quantitative study will commence by analyzing Boeing as a complex adaptive system but with a view to start building a broad fundamental, quantitative, predictive theory of social organizations, with the help of the successful body of work that has already begun at SFI. A major component is to understand the role of innovation and adaptability in shaping the growth and sustainability of cities to corporations.
Why Boeing? Boeing itself is largely a structure facilitating and integrating the flow of information through the complex organization network consisting from its many divisions and its multi-tiered multi-national suppliers chains. Boeing’s outstanding longevity (has thrived for 100 years while others failed), outstanding complexity, high propensity for innovation and the availability of the data recommend the company as the perfect case study, from which in the future a bridge can be build towards developing some universal laws of sustainability.
The study will attempt to identify the reasons for the system’s longevity, to quantifiably measure its robustness to perturbations and to define the strategy for its optimization and sustainability as a complex network.
The study will attempt to look at this complex network’s topology and at the dynamics of the information flow across the network, studying the impact this dynamics has on technologies and speed of innovation within the company. The study will look at the efficiency of the company’s internal communication and especially try to determine the ways of the optimization of the information flow from the bottom up, with the goal to increase the speed of innovation and economic productivity.
The study will attempt to answer some specific questions, such as for example: how the number of internal patents has been growing with the growth of the company’s size? Can out of this data some dynamic laws be derived and what are the strategic implications and innovation expectations for a company of a certain size?
The study will especially focus on those areas of the system’s landscape where the knowledge spillover occur and will especially try to determine in quantifiable terms the rates of technological improvement due to the knowledge transform.
The analyses of the data on learning curves, design structure matrices and patents (preferably all of the above for several different models) could be very useful in further investigating questions surrounding how technology characteristics (such as modularity) affect rates of technological improvement.

Members:
* [[Michael Richey]]
* [[Lara Danilova-Burdess]]

===Leverage in Equities Markets===

When people get excited about their prospects on the stock market, they borrow money from the bank to invest. This leverage effectively couples the bank to the stock market. Thus, interest rates determine demand for stock, and demand for stock can determine interest rates. Does this interplay cause traders to naturally find a stable balance of leverage and aggressiveness? How do the behavioral traits of traders influence the stability of these interactions? Are there regulatory behaviors, such as limiting leverage or slowing margin calls, that would contribute to the overall health of the market?

Members:
*[[Nathan Hodas]]
*[[Jacopo Tagliabue]]
*[[Martin Schmidt]]
*[[Jeremy Barofsky]]
*[[John Paul Gonzales]]
(provisionally [[Corinne Teeter]])

===Deconstructing CSSS09 Social Network===
CSSS09 is a group of people interested in complex systems who are randomly chosen to attend the one month summer school. The international and interdisciplinary group spends four weeks together learning, discussing and working on projects related to complex systems. We are interested in understanding the following questions:
- What is the social interaction network?
- How does the network change over time?
- What other factors influence the evolution of the network?
- How frequently was there discussion between disciplines and did that lead to productive final projects?
- Is there a link between the social and final product networks?

Status

Stage 1: First survey (profile, social contacts, future collaboration) – after 2nd weeks

Stage 2: Second survey (language, social activities, social contacts, future collaboration) – end of 3rd week

Stage 3: Network visualisation in NetLogo (proto type) & discussion on approaches for further analysis of data

Stage 4: Survey at the end of 4th week and one month after summer school

Members
*[[Margreth Keiler]]
*[[Murad Mithani]]
*[[Roozbeh Daneshvar]]
*[[Wendy Ham]]

====Analyzing Contagion in Heterogeneous Networks===========
*[[Roozbeh Daneshvar]]
*[[Lara Danilova-Burdess]]
*[[Karen Simpson]]
*[[Jeremy Barofsky]]

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-29T05:35:14Z

Nhodas: /* Guns, Germs and Steel: A Simulation */

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

===Problem solving and mating - are they similar?===

The discussions on this project have been moved to a separate page: [[Problem solving]]

=== Interacting distribution networks ===
Moved to its own page: [[Interacting distribution networks]]

===The Effect of Gossip on Social Networks===
Moved to a separate page: [[Modeling gossip networks]]

===Radicalization of Islamic Diasporas and Reactive Control Theoretical Approach===
See [[Radicalization]]

===From Topology to Response===
[[From_Topology_to_Response]]

===[[Spiking Networks on the Cusp of Chaos]]===

Please click the title to be transported to the project page.
===Modeling behaviors between students and teachers===
Update and Details about this project, please click here !
[http://www.santafe.edu/events/workshops/index.php/Modeling_behaviors_student&teacher Modeling behaviors between students and teachers]

===Determining spatial contact networks for pathogen transmission===
Project Page: [[Spatial contact networks]]

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

====Acupuncture/Chinese Alternative Medicine====

Here are some more papers regarding research that has been done on acupuncture. Some network analysis has been done. Very interesting stuff!

[[Media: AcupunctureOverview.pdf|Acupuncture Overview]]: Here is an overview of acupuncture from a journal entitled "Alternative Therapies" in 1998.

[[Media: AcupunctureGraphTheory.pdf| Acupuncture and Graph Theory]]: This paper was written in "Progress in Natural Science" in 2009 which implements the use of graph theory to make a model to understand the effects of acupunture on brain function.

[[Media: AcupunctureFibroblasts.pdf|Body-Wide Cellular Network of Fibroblast Cells]]: A paper relating the study of a body-wide network of fibroblasts to acupuncture. Written in "Histochemistry and Cell Biology" in 2004.

[[Media: AcupunctureNeedleAdmin.pdf|Acupuncture-Psychosocial Context]] And another which studies the effects of the procedure. Written in "Advanced Access Publication" in 2008.

Enjoy! [[Karen Simpson]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

[[Mahyar Malekpour]]: Update June 16, 2009 - Someone asked if there is an application for this. The answer is yes, categorically, any self-organizing system needs synchronization. However, my interest here are visualization and exploration using agent-based tools. I don not intend to develop a solution to this problem, rather build on an existing agent-based model (if there is any) and enhance its capabilities.

[[Massimo Mastrangeli]]: as someone said, there is vast literature on synchronization available; you can for example get a taste in [http://www.amazon.com/SYNC-Emerging-Science-Spontaneous-Order/dp/0786868449 Sync] by Steven Strogatz (also, check out his talk [http://www.ted.com/talks/steven_strogatz_on_sync.html at TED]). I am quite interested in the idea.

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski | Marek]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response | project page]] [[Marek Kwiatkowski | Marek]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

* [[Mauricio Gonzalez-Forero]]: I would like to hear more about this project. Can we meet sometime?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

*[[watson]]: hmmm ... yes. this is exactly what I am talking about. very cool to hear you have some experience with this Massimo. right now i am leaning on going ahead with this project. i think we have serious potential to make impact, elucidate new relationships and phenomena and educate in the process. and i think it could be a ton of fun. here are a couple of links i have found which could be of use:
**[http://fusionanomaly.net/polyrhythms.html this] page talks about history, theory and even mentions chaos.
**[http://web.mit.edu/cjoye/www/music/tabla/ this] is a good source for tabla samples. tabla is one of the simpler devices that has some melodic structure as well as rhythmic structure to it. we could work with others as well... one thought is even just a drum kit of different sounds (rock style).

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and has looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects, too. His papers might be a good starting point.

[[Nathan Hodas]] I'd like to be in on this. I've pondered a good deal about this since reading the book. Maybe we should contact Jared Diamond?

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

[[Margreth Keiler]]: Murad and I had the same idea yesterday, but we thought to make each week a surveys to see how the network change over time and to add also after CSSS surveys. Should we discuss our draft tomorrow at SFI?

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to
‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity
in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to
allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

===Economic Geography in the Lake Titicaca Basin===

Moved to [http://www.santafe.edu/events/workshops/index.php/Economic_Geography_and_State_Emergence Economic Geography and State Emergence]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

[[Image:Theoretical_framework.jpg|480px|thumb|Theoretical_framework]]

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Looking forward to exchanging ideas!

[[Steven Lade]] Hi Almut, As I've said, I think this is well suited to modelling with differential equations. Particularly if, as I assume to be the case, the GIS data comes already in a rectangular grid. Having said that, there are some complementary aspects for which ABM would be well-suited. For instance, following agents as they form streams, or if you were to have a localised thunderstorm. We could possibly do this in parallel and see if they match and/or use each method's particular advantages.

You may be interested in this paper, which I found through the SFI library database: [http://pubs.usgs.gov/sir/2007/5009/pdf/sir_2007-5009.pdf]. I think this one is more complicated though, because they need to consider a three-dimensional water table. More generally, what sort of modelling (if any) do people usually do in these sorts of topics?

[[Karen Simpson]] I am interested in this project! I have studied these concepts in many of my classes. Through past research, I've looked at storm/rain events, and how a large runoff from stormwater causes high contaminant concentrations in streams and rivers. This research was done for urban, forest, and agricultural landuse types.(I will try to find the results of this research soon). Another thing to think about is the time between rain events. A long timespan between rainfall events will cause the soil to become unsaturated, and the next rainfall may have little effect on the stream. I also will not be around much this weekend, so would it be possible to meet sometime tomorrow (Thursday 6/18)?

===Scalable (parallel) Spatial Agent-Based Models===

This project idea is an exploration of what happens to agent-based models “in the large?” For example,
* As the number of interacting agents in a model increases, what happens to the dynamics of the model?
* What happens as the size of the agents’ domain increases (e.g. simulating a neighborhood versus simulating a city or country)
* How do the properties of the model change? Are there scaling laws in effect ?

In order to investigate these issues, we need a scalable simulation, i.e. a parallel implementation of the model that allows us to introduce arbitrarily large numbers of agents. There are many approaches to doing this [lit review needed here!], but for this project, I would like to focus on spatial agent-based models: models where there are N agents who exist in a geographical domain and possess “vision,” where vision can be optical/eye-based, local communications (audible or electromagnetic line of site).
A couple such models which can serve as starting points include the flocking model (aka “boids”) and Epstein’s model of civil violence (or its derivative “Rebellion” model).

The idea is to decompose the spatial domain into independent subdomains, distribute those subdomains to nodes on a compute cluster, amalgamate the results, wash-rinse-repeat. One possible approach is to use an adaptive mesh refinement (AMR) such as those used by engineers for finite element analysis or by physicists in hydrodynamics simulations. One concrete example, using a quad-tree decomposition to keep agent density constant on each processor (and thereby keeping computational load balanced), is as follows:

[[Image:Particle.PNG|thumb|left|An example showing decomposition of a particle system using a quad-tree. Each resulting square has (roughly) the same number of particles in it. Can this approach be used for parallelizing spatial agent-based models ?]]
I have a cluster available for implementation, along with the MPI libraries for parallel programming. Other suggested areas of expertise that would greatly benefit the project include:
Someone interested in evaluating simulation results, who can help ensure that we don’t break the model by decomposing it.
Someone interested in analysis, for exploring the effects of scaling on the model.
Someone interested in high-performance computing, to help with programming (probably c/c++ with MPI)

From talking to folks in our class, some other benefits of the approach include
* improving running time for very-long-running simulations
* aerospace applications—decomposing the National Air Space into computationally tractable subdomains for modeling or real-world purposes.
* Applying the decomposition technique to other model domains. For example, can a similar technique be used to decompose a social network, especially if a single model has both geographic spatial domains and also network domains?

Other approaches suggested by classmates have included implementation on GPUs (graphics processors used for general purpose computation) and sticking to an SMP implementation (multicore workstations with shared memory--simpler implementation/perhaps not as scalable), versus a distributed-memory cluster. I welcome further ideas that might help kick-start this zany scheme.

[[Steven Lade]] What I'm about to say seems kind of obvious, and I'm not sure it helps you at all, but I can't help but say that if your 'averaged behaviour' converges for very large numbers of agents, you'd in effect be modelling some partial differential equation.

[[Matt McMahon]] Thanks, Steven. Not obvious to me though ... Can you elucidate?

[[Steven Lade]] It seems that as you reach a large number of agents, and your grid becomes small, you'd most likely reach some partial differential equation in the density of agents. Say a diffusion equation. Or a Navier-Stokes (fluid flow) equation. Not sure how easy it would be to derive, but this would be my intuition. It would be easiest for local interactions only (i.e. some radius which you could let approach zero) but non-local interactions might be possible too. It would of course all depend on the agent rules you use. If you're lucky one might even be able to derive some analytical results for special cases. If you want to chat more, find me in person. (Anyone: does this allnmake sense?)

=== Resilience to invaders in social systems ===

A piece of anecdata from my organizing days: the effect of an external organizer coming to help on a local campaign had one of two -- very different -- effects: either further coalescing the local campaign, or fragmenting it.

I'm curious how well social structures tolerate interlopers and what drives their resilience.

Possible metaphors/methods which could be useful:
* An agent-based models of the connectivity of the underlying social structure & reaction to interloper?
* Analogizing to food-web/ecology with the interloper as an invasive species?
* Analogizing the interloper to a crystal defect?

BUT I have no idea 1) how to parameterize this and 2) whether there are data (of any sort -- eg resilience to colonists/prophets/carpetbaggers) to which the model could be compared for sanity-checking.

I know '''nothing''' about sociology & related fields, so maybe this is a well-studied problem. Or an ill-posed problem. Or maybe it's not a problem at all. In any event, I'd be curious to hear other's thoughts.

==Final Projects==

Please place your final project ideas here: details should include clear and objective outlines.

===The Effect of Gossip on Social Networks===
In this project we look at the effects of gossip spread on social network structure. We define gossip as information passed between two individuals A and B about an individual C who is not present, which has the potential to affect the strengths of all three relationships A-B, B-C, and A-C. This work is novel in two respects: first, there is no theoretical work on how network structure changes when information passing through a network has the potential to affect edges not in the direct path, and second while past studies have looked at how network structure affects gossip spread, there is no work done on how gossip spread affects network structure.

Page: [[Modeling gossip networks]]

Members:
* [[Allison Shaw]]
* [[Chang Yu]]
* [[Dave Brooks]]
* [[Milena Tsvetkova]]
* [[Roozbeh Daneshvar ]]

===1,2,3, language!===

In a nutshell:
In this project we will make use of information theoretic measures of similarity between data sets, such as mutual information
or more specifically some global allignment methods coming from evolutionary biology to build up a distance matrix between languages.
The data under study are simply the numbers 1,2,3...,10, for which we have access to a massive dataset that enumerates the spelling of the first ten numbers in more than 4,000 languages. We will finally derive the phylogenetic tree of languages, and compare it with the state of the art.

Members:
* [[Andrew Berdahl]]
* [[Lucas Lacasa]]

===Social mitosis in group conversations: a cooperative phenomenon approach===

In a nutshell:
When you participate in a conversation, you typically expect to (i) actively participate and (ii) be confortable in it. These arguments somewhat put some constraints in the number of persons attending the same conversation. In other words, when people are forced to stay in the same confined space, they tend to undertake a conversation, however if too many people are present, the conversation rapidly splits in two, three... some nucleation phenomenon takes place. In this project we approach this subject from a complex systems point of view and want to understand if the 'conversation mitosis' is a collective phenomenon, much in the vein of a symmetry-breaking phenomenon in statistical physics. We will develop an agent based model that captures the essential mechanisms of conversation dynamics and will characterize such behaviors. Analytical developments will also be addressed. Finally, we will compare our analytical/numerical results with empirical data gathered through e-mail surveys.

Members:
* [[Massimo Mastrangeli]]
* [[Martin Schmidt]]
* [[Lucas Lacasa]]

===Modeling mesoscopic sequential self-assembly===

One of the reasons for the huge success of microelectronics is the ability to produce very large amounts of devices at very small price. Anyway, a large part of the final price of electronic devices is due to assembly and packaging issues. The standard procedure to package microdevices is by robotic or even manual manipulation, which while satisfactory for large sizes becomes inefficient and even practically incontrollable below the millimeter scale. Moreover, when dealing with very large amounts of components the task becomes time-consuming and this expensive.

Self-assembly techniques have the potential to boost electronic assembly by their intrinsic massive parallelism and advantageous scaling properties. Particularly, self-assembly performed in liquid environment has gained momentum by showing interesting performance. Anyway, the analytic modeling of the dynamics of this process is still limited and not capable of capturing the details of the stochastic dynamics of self-assembly. In this project, I want to simulate the dynamics 2D and 3D sequential self-assembly with agent-based models. This framework, never so far applied to this task, may help sheding light on the role of important parameters of the process such as dimensions of the assembly space, redundance of components, viscosity of the fluid carrier.

[[Massimo Mastrangeli]]

===Percolation-like phenomenon in the Google search engine===

In a nutshell:
Type a (short) random string of letters in Google. This mimics the effect of mispelling words, 'typos'. Surprisingly, you will find a non-null amount of results: the probability of finding such a word, even if it's a random string without a semantic meaning, is non-null, since (i) someone could have already 'invented it' (acronym or so), (ii) someone could have mispelled a word (committed a typographic error) in his/her website/blog etc. But repeat the procedure with larger strings, and look how the number of results rapidly drops to zero... Is this a phase transition? Can we characterize such phenomenon? What are the relations between language-like properties and this behavior? What information can we extract? In this project we will endeavor such questions, programming automatic queries to google of randomly-generated strings and relating the system's behavior to some collective phenomena such as Percolation.

Members:
* [[Jacopo Tagliabue]]
* [[Andrew Berdahl]]
* [[Lucas Lacasa]]

===Competitive spatial network growth===
Many large-scale aggregate networks are actually composed of several essentially independent subnetworks, each of which takes into account the other agents' actions. While traditional optimization methods yield insight into the most efficient network structures to satisfy a fixed objective, the presence of several overlapping and evolving networks may change the optimal strategy or create niches for otherwise suboptimal strategies. In this project we develop an agent-based network growth model to simulate competitive airline network growth, studying the effects of the demand distribution, entry time, and number of agents on the success and network structure of the agents.

[[Interacting distribution networks]]

Members:
* [[Brendan Colloran]]
* [[Caroline Farrior]]
* [[Daniel Wuellner]]
* [[Michael Schultz]]

===Spectral clustering of gene expression===

1. Can we differentiate between genes involved in separate biological functions (ie pathways) using spectral clustering?

2. If so, can we use this method to detect the genes activated in cancer?

Members:
* [[Rosemary Braun]]
* [[Corinne Teeter]]
* [[Elliot Martin]]
* [[Eric Kasper]]

===From Topology to Response===
[[From_Topology_to_Response]]

Members:
* [[Marek Kwiatkowski]]
* [[Rosemary Braun]]
* [[Alexander Mikheyev | Sasha Mikheyev]]

===Foraging on the move===
Many animals (e.g. caribou, wildebeest) forage in groups while moving from one location to another. This means individuals have to simultaneously balance several demands: finding the best resources, maintaining the cohesion of the group, and migration in a certain direction. While there is a vast literature on both flocking and optimal foraging, there has been no work done to understand how animals should trade off the decision to flock or forage (since it is difficult to do both simultaneously) during migration. We develop an individual-based model to address this, and implement a genetic algorithm to find the best decision-rule for switching between foraging and flocking, under a variety of conditions.

Page: [[Foraging on the move]]

Members:
* [[Allison Shaw]]
* [[Andrew Berdahl]]
* [[Kathrine Behrman|Kate Behrman]]
* [[Liliana Salvador]]
* [[Steven Lade]]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Members:
* [[Steven Lade]]
* [[Karen Simpson]]
* [[Almut Brunner]]

Update and own page following soon!

===Creative Process===
The project attempts to model the generation of ideas in the subconscious as a random combination of existing concepts (reflected as words) and their selection (reflected as variance). The selection filter determines the quality and quantity of ideas that rise to the conscious. Although the complete model may not be in place by the end of the week, the presentation will display a basic version of the final (and hopefully publishable) paper.

* [[Murad Mithani]]

===A Markov Model of Elite Factionalization===

Authoritarian regimes fracture when elites within the ruling coalition, which buttresses the dictator, defect. Consequently, regime change crucially depends on elite competition and coordination. Previous work has explored this topic through conventional formal models that make exacting informational and cognitive demands on agents. In contrast, this model will attempt to replicate these findings, while exploring additional dynamics and emergent behavior, by embedding boundedly rational agents in a Markovian system. Rather than assume hyper rational actors, capable of solving difficult dynamic programming problems, I assume that elites use relatively simple heuristics to navigate a stochastic environment.

* [[Trevor Johnston]]

===Radicalization Network (RadNet): Spread Mechanisms and Control===
Spread of radical ideologies is a key source of terrorist activities. Coordination and management of resources for preemptive control of such activities require network models that enable predictive identification of areas and agents before occurrence of terrorist action. Our RadNet project develops a layered network model where the dynamics of radicalization are used to define an evolving radical network layer that exploit an underlying social network layer. We develop quantitative models for change of state of nodes (representative of individuals in the social network) that capture progress of radicalization through intermediate stages of general, susceptible, indoctrinated, and radical. We analyze the model and its robustness to control actions by modeling in netlogo. The models developed and analyzed in RadNet, builds on insights from the contagion model, and introduces novel modular layered network approach to capture exploitation of social network for spread of ideology and control networks for monitoring and preemptive actions on the evolving radicalization network.

Members:
* [[Alhaji Cherif]]
* [[Prasanta Bose]]
* [[Hirotoshi Yoshioka]]
* [[Wei Ni]]
See [[Radicalization]]

===Guns, Germs and Steel: A Simulation===

We intend to model the spread of early human populations. In his popular work, Guns Germs, and Steel, Jared Diamond argues that the differential concentration of wealth and people is a result of geography and historical contingencies. Using net logo we will model the dynamics of hunter-gatherer spread and the emergence of early agriculture. Global bioproductivity maps will be used to quantitatively represent resource habitats.

Members:
* [[Randy Haas]]
* [[Nathan Hodas]]
* [[Alexander Mikheyev]]
[[Image:GGSscreencapture.jpg|400px|thumb|hunter-gatherer behavior of early humans]]

===Spiking Networks on the Cusp of Chaos===

The objective of this project is to investigate what small networks of idealized spiking neurons look and behave like at the edge of chaos. Specifically "edge of chaos" here means that special boundary area between the regions where we observe orderly periodic behavior on one side and chaotic non-periodic behavior on the other. (both have been observed previously in this dynamical system)

More details here: [[Spiking Networks on the Cusp of Chaos]]

===Game theoretic strategies in bike racing ===

Cooperation is often a paradoxical component of human groups. Seemingly altruistic behavior can often be explained by selfish strategies. The cultivation of cooperation in humans is crucial for solving the world's pressing problems such as climate change and international disputes. Apparent cooperation often shows up naturally in sports and is often attributed to social norms. Here we intend to set up the basic elements of a bike race where each individual is motivated to win, but can benefit through cooperation with other riders. Each rider will be given a set of skills and a cooperation strategy. The analysis will include competition among different strategies and an understanding of the emergent level of cooperation from only race environment (no social norms). We hope that the differences in qualitative properties between the modeled race and real races might teach us about the presence of social norms even in these competitive environments.

Members:
* [[Rhonda Hoenigman]]
* [[Joslyn Barnhart]]
* [[Caroline Farrior]]

=======Study of Sustainability of the Companies, Cities and Societies: Towards the Theory of Social Organizations.=======
=======BOEING AS A CASE STUDY===========
The first phase of this quantitative study will commence by analyzing Boeing as a complex adaptive system but with a view to start building a broad fundamental, quantitative, predictive theory of social organizations, with the help of the successful body of work that has already begun at SFI. A major component is to understand the role of innovation and adaptability in shaping the growth and sustainability of cities to corporations.
Why Boeing? Boeing itself is largely a structure facilitating and integrating the flow of information through the complex organization network consisting from its many divisions and its multi-tiered multi-national suppliers chains. Boeing’s outstanding longevity (has thrived for 100 years while others failed), outstanding complexity, high propensity for innovation and the availability of the data recommend the company as the perfect case study, from which in the future a bridge can be build towards developing some universal laws of sustainability.
The study will attempt to identify the reasons for the system’s longevity, to quantifiably measure its robustness to perturbations and to define the strategy for its optimization and sustainability as a complex network.
The study will attempt to look at this complex network’s topology and at the dynamics of the information flow across the network, studying the impact this dynamics has on technologies and speed of innovation within the company. The study will look at the efficiency of the company’s internal communication and especially try to determine the ways of the optimization of the information flow from the bottom up, with the goal to increase the speed of innovation and economic productivity.
The study will attempt to answer some specific questions, such as for example: how the number of internal patents has been growing with the growth of the company’s size? Can out of this data some dynamic laws be derived and what are the strategic implications and innovation expectations for a company of a certain size?
The study will especially focus on those areas of the system’s landscape where the knowledge spillover occur and will especially try to determine in quantifiable terms the rates of technological improvement due to the knowledge transform.
The analyses of the data on learning curves, design structure matrices and patents (preferably all of the above for several different models) could be very useful in further investigating questions surrounding how technology characteristics (such as modularity) affect rates of technological improvement.

Members:
* [[Michael Richey]]
* [[Lara Danilova-Burdess]]

===Leverage in Equities Markets===

When people get excited about their prospects on the stock market, they borrow money from the bank to invest. This leverage effectively couples the bank to the stock market. Thus, interest rates determine demand for stock, and demand for stock can determine interest rates. Does this interplay cause traders to naturally find a stable balance of leverage and aggressiveness? How do the behavioral traits of traders influence the stability of these interactions? Are there regulatory behaviors, such as limiting leverage or slowing margin calls, that would contribute to the overall health of the market?

Members:
*[[Nathan Hodas]]
*[[Jacopo Tagliabue]]
*[[Martin Schmidt]]
*[[Jeremy Barofsky]]
*[[John Paul Gonzales]]
(provisionally [[Corinne Teeter]])

===Deconstructing CSSS09 Social Network===
CSSS09 is a group of people interested in complex systems who are randomly chosen to attend the one month summer school. The international and interdisciplinary group spends four weeks together learning, discussing and working on projects related to complex systems. We are interested in understanding the following questions:
- What is the social interaction network?
- How does the network change over time?
- What other factors influence the evolution of the network?
- How frequently was there discussion between disciplines and did that lead to productive final projects?
- Is there a link between the social and final product networks?

Status

Stage 1: First survey (profile, social contacts, future collaboration) – after 2nd weeks

Stage 2: Second survey (language, social activities, social contacts, future collaboration) – end of 3rd week

Stage 3: Network visualisation in NetLogo (proto type) & discussion on approaches for further analysis of data

Stage 4: Survey at the end of 4th week and one month after summer school

Members
*[[Margreth Keiler]]
*[[Murad Mithani]]
*[[Roozbeh Daneshvar]]
*[[Wendy Ham]]

File:GGSscreencapture.jpg

2009-06-29T05:32:55Z

Nhodas: Hunter-gatherers spread through-out the old world

Hunter-gatherers spread through-out the old world

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-29T05:15:15Z

Nhodas:

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

===Problem solving and mating - are they similar?===

The discussions on this project have been moved to a separate page: [[Problem solving]]

=== Interacting distribution networks ===
Moved to its own page: [[Interacting distribution networks]]

===The Effect of Gossip on Social Networks===
Moved to a separate page: [[Modeling gossip networks]]

===Radicalization of Islamic Diasporas and Reactive Control Theoretical Approach===
See [[Radicalization]]

===From Topology to Response===
[[From_Topology_to_Response]]

===[[Spiking Networks on the Cusp of Chaos]]===

Please click the title to be transported to the project page.
===Modeling behaviors between students and teachers===
Update and Details about this project, please click here !
[http://www.santafe.edu/events/workshops/index.php/Modeling_behaviors_student&teacher Modeling behaviors between students and teachers]

===Determining spatial contact networks for pathogen transmission===
Project Page: [[Spatial contact networks]]

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

====Acupuncture/Chinese Alternative Medicine====

Here are some more papers regarding research that has been done on acupuncture. Some network analysis has been done. Very interesting stuff!

[[Media: AcupunctureOverview.pdf|Acupuncture Overview]]: Here is an overview of acupuncture from a journal entitled "Alternative Therapies" in 1998.

[[Media: AcupunctureGraphTheory.pdf| Acupuncture and Graph Theory]]: This paper was written in "Progress in Natural Science" in 2009 which implements the use of graph theory to make a model to understand the effects of acupunture on brain function.

[[Media: AcupunctureFibroblasts.pdf|Body-Wide Cellular Network of Fibroblast Cells]]: A paper relating the study of a body-wide network of fibroblasts to acupuncture. Written in "Histochemistry and Cell Biology" in 2004.

[[Media: AcupunctureNeedleAdmin.pdf|Acupuncture-Psychosocial Context]] And another which studies the effects of the procedure. Written in "Advanced Access Publication" in 2008.

Enjoy! [[Karen Simpson]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

[[Mahyar Malekpour]]: Update June 16, 2009 - Someone asked if there is an application for this. The answer is yes, categorically, any self-organizing system needs synchronization. However, my interest here are visualization and exploration using agent-based tools. I don not intend to develop a solution to this problem, rather build on an existing agent-based model (if there is any) and enhance its capabilities.

[[Massimo Mastrangeli]]: as someone said, there is vast literature on synchronization available; you can for example get a taste in [http://www.amazon.com/SYNC-Emerging-Science-Spontaneous-Order/dp/0786868449 Sync] by Steven Strogatz (also, check out his talk [http://www.ted.com/talks/steven_strogatz_on_sync.html at TED]). I am quite interested in the idea.

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski | Marek]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response | project page]] [[Marek Kwiatkowski | Marek]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

* [[Mauricio Gonzalez-Forero]]: I would like to hear more about this project. Can we meet sometime?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

*[[watson]]: hmmm ... yes. this is exactly what I am talking about. very cool to hear you have some experience with this Massimo. right now i am leaning on going ahead with this project. i think we have serious potential to make impact, elucidate new relationships and phenomena and educate in the process. and i think it could be a ton of fun. here are a couple of links i have found which could be of use:
**[http://fusionanomaly.net/polyrhythms.html this] page talks about history, theory and even mentions chaos.
**[http://web.mit.edu/cjoye/www/music/tabla/ this] is a good source for tabla samples. tabla is one of the simpler devices that has some melodic structure as well as rhythmic structure to it. we could work with others as well... one thought is even just a drum kit of different sounds (rock style).

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and has looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects, too. His papers might be a good starting point.

[[Nathan Hodas]] I'd like to be in on this. I've pondered a good deal about this since reading the book. Maybe we should contact Jared Diamond?

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

[[Margreth Keiler]]: Murad and I had the same idea yesterday, but we thought to make each week a surveys to see how the network change over time and to add also after CSSS surveys. Should we discuss our draft tomorrow at SFI?

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to
‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity
in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to
allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

===Economic Geography in the Lake Titicaca Basin===

Moved to [http://www.santafe.edu/events/workshops/index.php/Economic_Geography_and_State_Emergence Economic Geography and State Emergence]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

[[Image:Theoretical_framework.jpg|480px|thumb|Theoretical_framework]]

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Looking forward to exchanging ideas!

[[Steven Lade]] Hi Almut, As I've said, I think this is well suited to modelling with differential equations. Particularly if, as I assume to be the case, the GIS data comes already in a rectangular grid. Having said that, there are some complementary aspects for which ABM would be well-suited. For instance, following agents as they form streams, or if you were to have a localised thunderstorm. We could possibly do this in parallel and see if they match and/or use each method's particular advantages.

You may be interested in this paper, which I found through the SFI library database: [http://pubs.usgs.gov/sir/2007/5009/pdf/sir_2007-5009.pdf]. I think this one is more complicated though, because they need to consider a three-dimensional water table. More generally, what sort of modelling (if any) do people usually do in these sorts of topics?

[[Karen Simpson]] I am interested in this project! I have studied these concepts in many of my classes. Through past research, I've looked at storm/rain events, and how a large runoff from stormwater causes high contaminant concentrations in streams and rivers. This research was done for urban, forest, and agricultural landuse types.(I will try to find the results of this research soon). Another thing to think about is the time between rain events. A long timespan between rainfall events will cause the soil to become unsaturated, and the next rainfall may have little effect on the stream. I also will not be around much this weekend, so would it be possible to meet sometime tomorrow (Thursday 6/18)?

===Scalable (parallel) Spatial Agent-Based Models===

This project idea is an exploration of what happens to agent-based models “in the large?” For example,
* As the number of interacting agents in a model increases, what happens to the dynamics of the model?
* What happens as the size of the agents’ domain increases (e.g. simulating a neighborhood versus simulating a city or country)
* How do the properties of the model change? Are there scaling laws in effect ?

In order to investigate these issues, we need a scalable simulation, i.e. a parallel implementation of the model that allows us to introduce arbitrarily large numbers of agents. There are many approaches to doing this [lit review needed here!], but for this project, I would like to focus on spatial agent-based models: models where there are N agents who exist in a geographical domain and possess “vision,” where vision can be optical/eye-based, local communications (audible or electromagnetic line of site).
A couple such models which can serve as starting points include the flocking model (aka “boids”) and Epstein’s model of civil violence (or its derivative “Rebellion” model).

The idea is to decompose the spatial domain into independent subdomains, distribute those subdomains to nodes on a compute cluster, amalgamate the results, wash-rinse-repeat. One possible approach is to use an adaptive mesh refinement (AMR) such as those used by engineers for finite element analysis or by physicists in hydrodynamics simulations. One concrete example, using a quad-tree decomposition to keep agent density constant on each processor (and thereby keeping computational load balanced), is as follows:

[[Image:Particle.PNG|thumb|left|An example showing decomposition of a particle system using a quad-tree. Each resulting square has (roughly) the same number of particles in it. Can this approach be used for parallelizing spatial agent-based models ?]]
I have a cluster available for implementation, along with the MPI libraries for parallel programming. Other suggested areas of expertise that would greatly benefit the project include:
Someone interested in evaluating simulation results, who can help ensure that we don’t break the model by decomposing it.
Someone interested in analysis, for exploring the effects of scaling on the model.
Someone interested in high-performance computing, to help with programming (probably c/c++ with MPI)

From talking to folks in our class, some other benefits of the approach include
* improving running time for very-long-running simulations
* aerospace applications—decomposing the National Air Space into computationally tractable subdomains for modeling or real-world purposes.
* Applying the decomposition technique to other model domains. For example, can a similar technique be used to decompose a social network, especially if a single model has both geographic spatial domains and also network domains?

Other approaches suggested by classmates have included implementation on GPUs (graphics processors used for general purpose computation) and sticking to an SMP implementation (multicore workstations with shared memory--simpler implementation/perhaps not as scalable), versus a distributed-memory cluster. I welcome further ideas that might help kick-start this zany scheme.

[[Steven Lade]] What I'm about to say seems kind of obvious, and I'm not sure it helps you at all, but I can't help but say that if your 'averaged behaviour' converges for very large numbers of agents, you'd in effect be modelling some partial differential equation.

[[Matt McMahon]] Thanks, Steven. Not obvious to me though ... Can you elucidate?

[[Steven Lade]] It seems that as you reach a large number of agents, and your grid becomes small, you'd most likely reach some partial differential equation in the density of agents. Say a diffusion equation. Or a Navier-Stokes (fluid flow) equation. Not sure how easy it would be to derive, but this would be my intuition. It would be easiest for local interactions only (i.e. some radius which you could let approach zero) but non-local interactions might be possible too. It would of course all depend on the agent rules you use. If you're lucky one might even be able to derive some analytical results for special cases. If you want to chat more, find me in person. (Anyone: does this allnmake sense?)

=== Resilience to invaders in social systems ===

A piece of anecdata from my organizing days: the effect of an external organizer coming to help on a local campaign had one of two -- very different -- effects: either further coalescing the local campaign, or fragmenting it.

I'm curious how well social structures tolerate interlopers and what drives their resilience.

Possible metaphors/methods which could be useful:
* An agent-based models of the connectivity of the underlying social structure & reaction to interloper?
* Analogizing to food-web/ecology with the interloper as an invasive species?
* Analogizing the interloper to a crystal defect?

BUT I have no idea 1) how to parameterize this and 2) whether there are data (of any sort -- eg resilience to colonists/prophets/carpetbaggers) to which the model could be compared for sanity-checking.

I know '''nothing''' about sociology & related fields, so maybe this is a well-studied problem. Or an ill-posed problem. Or maybe it's not a problem at all. In any event, I'd be curious to hear other's thoughts.

==Final Projects==

Please place your final project ideas here: details should include clear and objective outlines.

===The Effect of Gossip on Social Networks===
In this project we look at the effects of gossip spread on social network structure. We define gossip as information passed between two individuals A and B about an individual C who is not present, which has the potential to affect the strengths of all three relationships A-B, B-C, and A-C. This work is novel in two respects: first, there is no theoretical work on how network structure changes when information passing through a network has the potential to affect edges not in the direct path, and second while past studies have looked at how network structure affects gossip spread, there is no work done on how gossip spread affects network structure.

Page: [[Modeling gossip networks]]

Members:
* [[Allison Shaw]]
* [[Chang Yu]]
* [[Dave Brooks]]
* [[Milena Tsvetkova]]
* [[Roozbeh Daneshvar ]]

===1,2,3, language!===

In a nutshell:
In this project we will make use of information theoretic measures of similarity between data sets, such as mutual information
or more specifically some global allignment methods coming from evolutionary biology to build up a distance matrix between languages.
The data under study are simply the numbers 1,2,3...,10, for which we have access to a massive dataset that enumerates the spelling of the first ten numbers in more than 4,000 languages. We will finally derive the phylogenetic tree of languages, and compare it with the state of the art.

Members:
* [[Andrew Berdahl]]
* [[Lucas Lacasa]]

===Social mitosis in group conversations: a cooperative phenomenon approach===

In a nutshell:
When you participate in a conversation, you typically expect to (i) actively participate and (ii) be confortable in it. These arguments somewhat put some constraints in the number of persons attending the same conversation. In other words, when people are forced to stay in the same confined space, they tend to undertake a conversation, however if too many people are present, the conversation rapidly splits in two, three... some nucleation phenomenon takes place. In this project we approach this subject from a complex systems point of view and want to understand if the 'conversation mitosis' is a collective phenomenon, much in the vein of a symmetry-breaking phenomenon in statistical physics. We will develop an agent based model that captures the essential mechanisms of conversation dynamics and will characterize such behaviors. Analytical developments will also be addressed. Finally, we will compare our analytical/numerical results with empirical data gathered through e-mail surveys.

Members:
* [[Massimo Mastrangeli]]
* [[Martin Schmidt]]
* [[Lucas Lacasa]]

===Modeling mesoscopic sequential self-assembly===

One of the reasons for the huge success of microelectronics is the ability to produce very large amounts of devices at very small price. Anyway, a large part of the final price of electronic devices is due to assembly and packaging issues. The standard procedure to package microdevices is by robotic or even manual manipulation, which while satisfactory for large sizes becomes inefficient and even practically incontrollable below the millimeter scale. Moreover, when dealing with very large amounts of components the task becomes time-consuming and this expensive.

Self-assembly techniques have the potential to boost electronic assembly by their intrinsic massive parallelism and advantageous scaling properties. Particularly, self-assembly performed in liquid environment has gained momentum by showing interesting performance. Anyway, the analytic modeling of the dynamics of this process is still limited and not capable of capturing the details of the stochastic dynamics of self-assembly. In this project, I want to simulate the dynamics 2D and 3D sequential self-assembly with agent-based models. This framework, never so far applied to this task, may help sheding light on the role of important parameters of the process such as dimensions of the assembly space, redundance of components, viscosity of the fluid carrier.

[[Massimo Mastrangeli]]

===Percolation-like phenomenon in the Google search engine===

In a nutshell:
Type a (short) random string of letters in Google. This mimics the effect of mispelling words, 'typos'. Surprisingly, you will find a non-null amount of results: the probability of finding such a word, even if it's a random string without a semantic meaning, is non-null, since (i) someone could have already 'invented it' (acronym or so), (ii) someone could have mispelled a word (committed a typographic error) in his/her website/blog etc. But repeat the procedure with larger strings, and look how the number of results rapidly drops to zero... Is this a phase transition? Can we characterize such phenomenon? What are the relations between language-like properties and this behavior? What information can we extract? In this project we will endeavor such questions, programming automatic queries to google of randomly-generated strings and relating the system's behavior to some collective phenomena such as Percolation.

Members:
* [[Jacopo Tagliabue]]
* [[Andrew Berdahl]]
* [[Lucas Lacasa]]

===Competitive spatial network growth===
Many large-scale aggregate networks are actually composed of several essentially independent subnetworks, each of which takes into account the other agents' actions. While traditional optimization methods yield insight into the most efficient network structures to satisfy a fixed objective, the presence of several overlapping and evolving networks may change the optimal strategy or create niches for otherwise suboptimal strategies. In this project we develop an agent-based network growth model to simulate competitive airline network growth, studying the effects of the demand distribution, entry time, and number of agents on the success and network structure of the agents.

[[Interacting distribution networks]]

Members:
* [[Brendan Colloran]]
* [[Caroline Farrior]]
* [[Daniel Wuellner]]
* [[Michael Schultz]]

===Spectral clustering of gene expression===

1. Can we differentiate between genes involved in separate biological functions (ie pathways) using spectral clustering?

2. If so, can we use this method to detect the genes activated in cancer?

Members:
* [[Rosemary Braun]]
* [[Corinne Teeter]]
* [[Elliot Martin]]
* [[Eric Kasper]]

===From Topology to Response===
[[From_Topology_to_Response]]

Members:
* [[Marek Kwiatkowski]]
* [[Rosemary Braun]]
* [[Alexander Mikheyev | Sasha Mikheyev]]

===Foraging on the move===
Many animals (e.g. caribou, wildebeest) forage in groups while moving from one location to another. This means individuals have to simultaneously balance several demands: finding the best resources, maintaining the cohesion of the group, and migration in a certain direction. While there is a vast literature on both flocking and optimal foraging, there has been no work done to understand how animals should trade off the decision to flock or forage (since it is difficult to do both simultaneously) during migration. We develop an individual-based model to address this, and implement a genetic algorithm to find the best decision-rule for switching between foraging and flocking, under a variety of conditions.

Page: [[Foraging on the move]]

Members:
* [[Allison Shaw]]
* [[Andrew Berdahl]]
* [[Kathrine Behrman|Kate Behrman]]
* [[Liliana Salvador]]
* [[Steven Lade]]

===“Let it rain” - Simulating flood events by Agent-Based Modeling and GIS===

How much rain is required to flood the Grand Canyon?

The idea is to build an Agent-Based Model to simulate the impact of increased rainfall on flow dynamics of a specific river network of the Grand Canyon region. The agent for the ABM is the water flow (=runoff) moving from cell to cell, dependent upon topography (=slope/gradients of the neighboring cells).
The flow dynamics are therefore directly related to the Digital Elevation Model (DEM) of the region and indirectly to environmental parameters such as soil/substrate (e.g. stratigraphical units) and land cover/use (e.g. bare soil, shrubs, forest, settlement). The latter parameters could be integrated into the ABM by assuming a possible range of values influencing flow dynamics in relation to e.g. infiltration (if the soil is saturated, runoff occurs) and vegetation cover (high vegetation cover leads to high interception, less runoff).
The different data layers can be integrated into the ABM by GIS (Geographical Information Systems).

What is the relationship between rainfall pattern and runoff/ flooding?

What effects do topographical/environmental parameters (e.g. slope gradients, substrate, vegetation cover) have on runoff/flooding?

Are there non-linearities related to the dynamical flow network?

What are possible feedback mechanisms? (e.g. positive feedback mechanism: increased rainfall → increased runoff → erosion and hence deepening of channels → steeper slope gradients → increased runoff)

Members:
* [[Steven Lade]]
* [[Karen Simpson]]
* [[Almut Brunner]]

Update and own page following soon!

===Creative Process===
The project attempts to model the generation of ideas in the subconscious as a random combination of existing concepts (reflected as words) and their selection (reflected as variance). The selection filter determines the quality and quantity of ideas that rise to the conscious. Although the complete model may not be in place by the end of the week, the presentation will display a basic version of the final (and hopefully publishable) paper.

* [[Murad Mithani]]

===A Markov Model of Elite Factionalization===

Authoritarian regimes fracture when elites within the ruling coalition, which buttresses the dictator, defect. Consequently, regime change crucially depends on elite competition and coordination. Previous work has explored this topic through conventional formal models that make exacting informational and cognitive demands on agents. In contrast, this model will attempt to replicate these findings, while exploring additional dynamics and emergent behavior, by embedding boundedly rational agents in a Markovian system. Rather than assume hyper rational actors, capable of solving difficult dynamic programming problems, I assume that elites use relatively simple heuristics to navigate a stochastic environment.

* [[Trevor Johnston]]

===Radicalization Network (RadNet): Spread Mechanisms and Control===
Spread of radical ideologies is a key source of terrorist activities. Coordination and management of resources for preemptive control of such activities require network models that enable predictive identification of areas and agents before occurrence of terrorist action. Our RadNet project develops a layered network model where the dynamics of radicalization are used to define an evolving radical network layer that exploit an underlying social network layer. We develop quantitative models for change of state of nodes (representative of individuals in the social network) that capture progress of radicalization through intermediate stages of general, susceptible, indoctrinated, and radical. We analyze the model and its robustness to control actions by modeling in netlogo. The models developed and analyzed in RadNet, builds on insights from the contagion model, and introduces novel modular layered network approach to capture exploitation of social network for spread of ideology and control networks for monitoring and preemptive actions on the evolving radicalization network.

Members:
* [[Alhaji Cherif]]
* [[Prasanta Bose]]
* [[Hirotoshi Yoshioka]]
* [[Wei Ni]]
See [[Radicalization]]

===Guns, Germs and Steel: A Simulation===

We intend to model the spread of early human populations. In his popular work, Guns Germs, and Steel, Jared Diamond argues that the differential concentration of wealth and people is a result of geography and historical contingencies. Using net logo we will model the dynamics of hunter-gatherer spread and the emergence of early agriculture. Global bioproductivity maps will be used to quantitatively represent resource habitats.

Members:
* [[Randy Haas]]
* [[Nathan Hodas]]
* [[Alexander Mikheyev]]

===Spiking Networks on the Cusp of Chaos===

The objective of this project is to investigate what small networks of idealized spiking neurons look and behave like at the edge of chaos. Specifically "edge of chaos" here means that special boundary area between the regions where we observe orderly periodic behavior on one side and chaotic non-periodic behavior on the other. (both have been observed previously in this dynamical system)

More details here: [[Spiking Networks on the Cusp of Chaos]]

===Game theoretic strategies in bike racing ===

Cooperation is often a paradoxical component of human groups. Seemingly altruistic behavior can often be explained by selfish strategies. The cultivation of cooperation in humans is crucial for solving the world's pressing problems such as climate change and international disputes. Apparent cooperation often shows up naturally in sports and is often attributed to social norms. Here we intend to set up the basic elements of a bike race where each individual is motivated to win, but can benefit through cooperation with other riders. Each rider will be given a set of skills and a cooperation strategy. The analysis will include competition among different strategies and an understanding of the emergent level of cooperation from only race environment (no social norms). We hope that the differences in qualitative properties between the modeled race and real races might teach us about the presence of social norms even in these competitive environments.

Members:
* [[Rhonda Hoenigman]]
* [[Joslyn Barnhart]]
* [[Caroline Farrior]]

=======Study of Sustainability of the Companies, Cities and Societies: Towards the Theory of Social Organizations.=======
=======Boeing as a case study.===========
The first phase of this quantitative study will commence by analyzing Boeing as a complex adaptive system but with a view to start building a broad fundamental, quantitative, predictive theory of social organizations, with the help of the successful body of work that has already begun at SFI. A major component is to understand the role of innovation and adaptability in shaping the growth and sustainability of cities to corporations.
Why Boeing? Boeing itself is largely a structure facilitating and integrating the flow of information through the complex organization network consisting from its many divisions and its multi-tiered multi-national suppliers chains. Boeing’s outstanding longevity (has thrived for 100 years while others failed), outstanding complexity, high propensity for innovation and the availability of the data recommend the company as the perfect case study, from which in the future a bridge can be build towards developing some universal laws of sustainability.

Members:
* [[Michael Richey]]
* [[Lara Danilova-Burdess]]

===Leverage in Equities Markets===

When people get excited about their prospects on the stock market, they borrow money from the bank to invest. This leverage effectively couples the bank to the stock market. Thus, interest rates determine demand for stock, and demand for stock can determine interest rates. Does this interplay cause traders to naturally find a stable balance of leverage and aggressiveness? How do the behavioral traits of traders influence the stability of these interactions? Are there regulatory behaviors, such as limiting leverage or slowing margin calls, that would contribute to the overall health of the market?

Members:
*[[Nathan Hodas]]
*[[Jacopo Tagliabue]]
*[[Martin Schmidt]]
*[[Jeremy Barofsky]]
*[[John Paul Gonzales]]
(provisionally [[Corinne Teeter]])

Rafting Trip Cars

2009-06-25T14:01:55Z

Nhodas: /* Wendy's Car */

=Corrine's Car=

1. Corrine

2. Karen

=Randy's Truck=

1. Randy

2. Rosemary

3. Jacopo

=Watson's Car=

1. Watson

=Wendy's Car=

1. Wendy

2. Brian (shotgun)

3. May

4. Nathan

=Rental Car #1=

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-17T04:38:24Z

Nhodas: /* Problem solving and mating - are they similar? */

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

[[Mahyar Malekpour]]: Update June 16, 2009 - Someone asked if there is an application for this. The answer is yes, categorically, any self-organizing system needs synchronization. However, my interest here are visualization and exploration using agent-based tools. I don not intend to develop a solution to this problem, rather build on an existing agent-based model (if there is any) and enhance its capabilities.

===The Global Spread of Cricket===
No I'm not actually intending to study this particular topic. But there is one interesting article published in 2005 (Kaufman and Patterson, American Sociological Review) that examined why cricket continues to be popular in many British-influenced societies while it is not in the U.S. and Canada. This is interesting given the fact that cricket was very popular in the two countries and that the first official international cricket match took place between the two countries in the mid-19th century. So, not only how cultures, ideas, technologies, etc. diffuse across nations, populations, and so on, but also mechanisms that influence the retention after the initial adoption merit serious attention I think. One possible topic include is modern contraceptive use in developing countries. I guess modeling such mechanisms would require taking into account the models presented by Nathan Collins and Peter Dodds, in addition to signed networks (Doreian). One difficulty of modeling this kind of mechanism is that both structural and individual factors should be considered [[Hirotoshi Yoshioka]].

There is a nice article by H. Peyton Young ('Social Dynamics: Theory and Applications', Handbook of Computational Economics, Vol. II; you can download it at http://www.econ.jhu.edu/people/young/Publications.html) which investigates the evolution of norms or conventions in an agent-based modelling / evolutionary game theoretic setting (in our small library there is also a whole book about that by Peyton Young). It might be interesting to analyze the diverging popularity of cricket in those countries (which can be interpreted as a kind of convention) in this framework. [[Mareen Hofmann]]

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response]] project page. '''We still need a biologist.''' [[Marek Kwiatkowski]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Problem solving and mating - are they similar?===
I was intrigued by Tom's model of mating and began to wonder whether we can think of problem solving in a similar way. If we were to model problem solving, how would we do it? I'd like to think that problems and solutions are components that combine to generate an emergent property. (After a problem meets a solution--or a solution meets a problem--something new is allowed to emerge. While one instance of problem solving does not exactly create a complex system, many instances may.) That said, there are several questions/considerations to think about before/while we create a proper model of problem solving:

* Given a population of information/knowledge, how can we identify what are problems and what are solutions?
* Actually, which comes first: knowledge, information, problems, or solutions?
* What are some important dimensions of problems and solutions? (These dimensions should inform some kind of a matching probability for problems and solutions.)
* What is the difference between problems and solutions anyway?
* What makes certain kinds of problems and solutions "hang out" in a cluster or neighboring clusters? Is this primarily due to path-dependence?
* When there is a difficult problem (tentatively defined as a problem for which there is no nearby solutions), how can we tell which clusters have the greatest probability of containing the solution(s)? (Can some of the network stuff we learned be of help here?)
* It is of course important to remember that a problem can have many solutions, and a solution can solve many problems, but that they may have different degrees of affinity (just like a ligand-receptor interaction in molecular biology). Also, occasionally a problem needs a combination of several solutions ("AND" as opposed to "OR").

I would love to hear your thoughts and comments, and I'm hoping that someone may actually share some of my interests in figuring out the answers to the questions above! [[Wendy Ham]]

[[Murad Mithani]]: We can look at problem solving as a special case of idea generation. See if you find any parallels between what you have in mind to what is written in the creative process.

[[David Brooks]]: This matching of past solutions or components to new problems leads to several interesting topics of discussion: (1) Shouldn't the process of developing a solution path be treated as a potentially complex system, (2) How do we describe the process without providing a falsely formulaic structure (3) When is the problem, the set of goals, and the process considered to be identified and what elements of the description may hint to the fragility of understanding? I have quite a bit of experience researching and addressing these issues and can help if this becomes a project.

[[Image:Bjh_singles_map.png|250px|right]]'''[[Brian Hollar]]:''' I've been doing some research for my dissertation on '''the effects of gender-imbalances on marriage markets''' and think this would be a fun project to try to model in NetLogo and something that would tie in nicely with Wendy's idea. The basic concept is to try to model the effects of "marriage markets" with more men in them than women or vice-versa, with possible extension to see if this same concept could be expanded to problem-solution matching. Examples of social groups which experience a gender imbalances in marriage markets include: most religious groups, college campuses, some large cities (such as New York and Washington, DC), the African-American community, and some nations (notably China). I am interested in how these gender imbalances affect social norms, marriage and divorce rates, and dating/matching behavior in each of these various groups. Other problem-solution matchings might include: employer-employee, entrepreneur-investor, buyer-seller, etc. If we make the model robust enough, we might be able to extend it to these and other contexts as well.

Some thoughts I have of what to incorporate into the model include:
* The effects of social capital.
* Vision (limited ability to see other agents).
* Open vs. closed groups. (Adjusting rate of entry/exit of agents.)
* Slider-switch for adjusting sex-ratios.
* "Tainting effects" for failure.
* Heterogeneous "attraction" characteristics of each agent.

I'd love to hear ideas anyone might have for this. [[Nathan Hodas]]: Brian, don't forget that the minor party has an incentive to wait for the best possible match, and for the majority party, their may not be more fish in the sea, so they must grab what they can get. This will likely produce some skew in the "optimality" of pairing. You could create some measure of compatibility between individuals and see how this measure varies with system parameters.

[[Wendy Ham]]: [[Jacopo Tagliabue]] shared some interesting thoughts on how to define problems and solutions --> 1) The first one is to define a problem as a lack of knowledge (where knowledge may be theoretical, knowing that, or applied, knowing how) and then use a doxastic logic approach to clarify the notion. The idea is that there is a set of possible states of the world, so-called possible worlds in formal semantic, and our world is one of them: the more you know about the world, the more worlds you can rule out (in the end, with perfect knowledge you will find out which is our world among the infinite set of possibility). You may represent a world as a long description: the set of possible worlds is thus the set pf possible descriptions. Just one of them happens to be THE TRUE description of our world: our tricky task is to find out which one is. For example, since we know that gravity is inversely proportional to distance, we know that all the description saying that gravity is not inversely proportional to distance are false, and cannot be the description of our world. The idea that increasing knowledge means reducing possibilities is analogous to the idea that acquiring information decrease the uncertainties. A problem can be modeled by a set of possible worlds, where each world in the set may actually be the world we live in. A solution is a function from this set to a sub-set of the set (or something similar, I haven't think in depth about this). 2) A second approach may be incorporating some notion from formal learning theorem, where the scientific enterprise is modeled using result from recursion theory (look at this: http://www.princeton.edu/~osherson/papers/hist25.pdf).

[[Wendy Ham]]: My thought originally was to use ABM to model a population of problems and solutions by: 1) determining what counts as problems and as solutions, 2) assigning dimensions to problems and solutions, which determine how they subsequently form a cluster in someone's head, and 3) determining how these heads subsequently form a larger cluster of disciplines, 4) demonstrating that compatible problems and solutions can occasionally end up in faraway clusters (such that they need to be brought back together to generate innovation - possibly using random shortcuts a la those found in small world networks). Jacopo's ideas are making me reevaluate these thoughts...

[[Wendy Ham]]: (Credit to [[Nathan Hodas]]) To be a bit more empirical, it would be interesting to examine a major innovative problem solving event in history that involve the cross-pollination of ideas from several disciplines, e.g., the discovery of the double helix structure, and ask: what kind of structure or system could we have put in place to make such event occur sooner? In other words, what can be done - structurally speaking - to expedite the 'mating' of problems and solutions from traditionally separate fields?

[[Nathan Hodas]]: I really like this problem, because it can be attacked in so many ways. Consider the following two problems, which we should also be able to explain: 1) I just got locked out of my room. (a mundane problem) and 2) How do I build a time machine (an impossible problem)?. PS. I'm no longer locked out of my room. problem solved.

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===Modeling Gossip Networks===

It could be neat to develop a model of gossip networks. If you define gossip as information passed between 2 individuals (call them A and B) about a third party (C), then the act of gossiping has the potential to change the status/connection strength of all parties involved (e.g. maybe strength A-B, and weaken A-C and B-C bonds). Essentially passing information along a path in the network changes the value of BOTH edges in the direct pathway as well as other edges in the network. These are just preliminary ideas, but perhaps we could model how gossip tendency/frequency influences the structure of a network. Also, is it possible for individuals to influence their location in a network (e.g. increase centrality) by changing their gossiping frequency? (Although this is potentially a complicated rather than complex model idea...) Let me know what you guys think! [[Allison Shaw]]
* [[Milena Tsvetkova]]: This is a very interesting idea from sociological point of view. The effect of networks on the spread of gossip is well understood: some of the social dynamics at play include biases in the selection of trusted third parties (one draws a sample of information consistent with one’s predisposition), the reinforcement of opinions in dyads due to an etiquette mechanism, the exaggeration of information in triads due to echo effects. However, I am not aware of any studies that investigate how the spread of gossip affects network structure. My work is on the coevolution of behavior and social networks so we should talk!

* XOXO [[Chang Yu]]:Interesting! Gossip is not always bad. If we can model its spreading mechanism, it could help especially when you want to spread information unofficially. I get some inspirations from Tom’s last lecture on Friday. In the gossip network, what kind of properties of these agents can speed up or reduce information spread, the range of social circle, poverty, wealth, the information itself, or even the locations of houses in a community? I think we may model the different spreading results under different properties.

[[David Brooks]]: I agree that this concept of Gossip Networks is a generic for the analysis of several potential problems. I would like to talk to you about your intended direction and methods.

[[Gustavo Lacerda]]: sounds like some interesting dynamics, but how are you going to get data?

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Interacting distribution networks ===
Moved to its own page: [[Interacting distribution networks]]

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Modeling behaviors between students and teachers===

[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !

Interesting idea, you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know, I know some of the authors of the two papers. Alhaji Cherif

[[Chang Yu]]:Thank u Alhaji! I really appreciate if you could tell me how to get the papers. Have you done any research in this area? We should talk about it! Thanks.

[[Image:student&teacher-Chang.jpg|200px|thumb|left|]] [[Chang Yu]]:I think this picture could be a better way to explain this project.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and has looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects, too. His papers might be a good starting point.

[[Nathan Hodas]] I'd like to be in on this. I've pondered a good deal about this since reading the book. Maybe we should contact Jared Diamond?

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to ‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

===Economic Geography in the Lake Titicaca Basin===

By A.D. 1200, the Tiwanaku Empire reached an apex in population and social complexity. The distribution of settlement sizes at that time approximated a power-law or log-normal distribution with the urban center of Tiwanaku as the capitol. Archaeologists such as Stanish, Janusek, and others have argued that, while economic factors drive early settlement patterns, differential growth in settlements is primarily a function of competing political polities. They argue that polities with the most integrative religious traditions continued to grow relative to surrounding communities. In this project, we wish to explore the possibility that a simpler economic /demographic model can explain the emergence of such settlement patterns through time. Specifically, we intend to follow the general modeling framework created by Paul Krugman, which he calls the “New Economic Geography.”

Our working method would be to use the behaviors proposed by Krugman in an agent-based model that predicts the spatial distributions of the empirical settlement patterns in the Lake Titicaca Basin. The model explains settlement change by assuming that separate forces encourage production to simultaneously concentrate and disperse. The centrifugal forces that encourage dispersed production come from the uniformly distributed farming population which demands goods. Transportation costs mean that production should be close to farmers and therefore dispersed across the landscape to serve farmers efficiently. At the same time, we assume that increasing returns to scale obtains for production, meaning that larger production facilities can produce at lower marginal costs. This centripetal force encourages production to be centered in one area. In addition, production centered in one area attracts more workers and increases local demand for the product. Depending on model parameters, these dynamics can give rise to a dominant production and population centered, as seen in the Lake Titicaca data.

We don’t think we can take this project on by ourselves, so we are writing to see if we can get 1 or 2 more people interested and then move forward. If interested, please talk to [[Randy Haas]] or [[Jeremy Barofsky]]. Thanks!

==Acupuncture/Chinese Alternative Medicine==

Here are some more papers regarding research that has been done on acupuncture. Some network analysis has been done. Very interesting stuff!

[[Media:AcupunctureOverview.pdf]]: Here is an overview of acupuncture from a journal entitled "Alternative Therapies" in 1998.
[[Media:AcupuntureGraphTheory.pdf]]: This paper was written in "Progress in Natural Science" in 2009 which implements the use of graph theory to make a model to understand the effects of acupunture on brain function.
[[Media:AcupunctureFibroblasts]]: A paper relating the study of a body-wide network of fibroblasts to acupuncture. Written in "Histochemistry and Cell Biology" in 2004.
[[Media:AcupunctureNeedleAdmin]] And another which studies the effects of the procedure. Written in "Advanced Access Publication" in 2008.

Enjoy! [[Karen Simpson]]

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-17T04:18:48Z

Nhodas: /* Guns, Germs and Steel: Modeling the fates of human societies */

{{CSSS 2009 Santa Fe}}
==Project Groups==
===Foraging on the move===
[[Allison Shaw]]: I've moved the discussion of this idea to a separate project page -- see ([[Foraging on the move]]) for more detail and feel free to join in!

==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Complex networks of acupuncture points around the body===

what this project supposed to do is to set up the correlations of 720 acupuncture points complex network to do some interesting research on it. And what is important is such kind of work hasn't been done as i know. Feel free to have some discusstions on it to excite some good ideas. You could search "acupuncture" on wiki to get some general knowledge, Part of them are as belows.

Acupuncture is a technique of inserting and manipulating fine filiform needles into specific points on the body to relieve pain or for therapeutic purposes. The word acupuncture comes from the Latin acus, "needle", and pungere, "to prick". In Standard Mandarin, 針砭 (zhēn biān) (a related word, 針灸 (zhēn jiǔ), refers to acupuncture together with moxibustion).

According to traditional Chinese medical theory, acupuncture points are situated on meridians along which qi, the vital energy, flows. There is no known anatomical or histological basis for the existence of acupuncture points or meridians. Modern acupuncture texts present them as ideas that are useful in clinical practice. According to the NIH consensus statement on acupuncture, these traditional Chinese medical concepts "are difficult to reconcile with contemporary biomedical information but continue to play an important role in the evaluation of patients and the formulation of treatment in acupuncture."

The earliest written record that is available about acupuncture is Huangdi Neijing (黄帝内经 or Yellow Emperor's Inner Canon), which suggests acupuncture originated in China and would explain why it is most commonly associated with traditional Chinese medicine (TCM). Different types of acupuncture (Classical Chinese, Japanese, Tibetan, Vietnamese and Korean acupuncture) are practiced and taught throughout the world. [[Guimei Zhu]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

[[Almut Brunner]] Sounds like a very challenging project. As climate modelling is a very broad issue in itself, I would suggest to look at a specific example of threshold values in climate models, e.g. changes in rainfall patterns in Saharan environment and its impact on vegetation cover and finally desertification. It is known, for example, that if the rainfall amount in the Sahara drops below a critical value of 100mm/yr, the vegetation cover will change extremely due to reduced water availability and hence cause irreversible environmental changes. But I am not sure, if we could model that due to complicated/complex feedback mechanism and limited access to data. Another idea could be to simulate the other extreme - increased rainfalls. Is there a critical threshold value/tipping point causing extreme floods and environmental hazards in exposed, vulnerable landscapes (e.g. lowlands, coastal regions or even around here in the Grand Canyon region for which we can certainly get some nice data?).
Looking forward to discuss these issues a bit more with you.
What kind of model did you have in mind for simulating tipping point and feedback mechanism?

[[Steven Lade]] I wasn't thinking of modelling any of the physics or ecology directly, but at a coarse level with something like
<pre>node_i (time) = f_i[global temperature(time - delay_i), outputs of other nodes(time - delay_i)]
global temperature(time) = IPCC[time] + g[outputs of nodes(time)]</pre>
Each of the nodes would be a local tipping element. Lenton et al. already provide the global average temperature thresholds for the tipping elements and the time delay for the element to actually tip. We can then specify the part of the function <code> f_i[global temperature] </code> with something like a sigmoidal function. For the base time course of global temperature we could use IPCC projections or hold it fixed and just see what the feedbacks do to it. What Lenton et al. doesn't specify in detail is these feedbacks -- i.e. the dependence of nodes and the global temperature on the other nodes. Someone suggested to me that for a more abstract study we could look at the behaviour of the system over a range of possible feedbacks.

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

[[Mahyar Malekpour]]: Update June 16, 2009 - Someone asked if there is an application for this. The answer is yes, categorically, any self-organizing system needs synchronization. However, my interest here are visualization and exploration using agent-based tools. I don not intend to develop a solution to this problem, rather build on an existing agent-based model (if there is any) and enhance its capabilities.

===The Global Spread of Cricket===
No I'm not actually intending to study this particular topic. But there is one interesting article published in 2005 (Kaufman and Patterson, American Sociological Review) that examined why cricket continues to be popular in many British-influenced societies while it is not in the U.S. and Canada. This is interesting given the fact that cricket was very popular in the two countries and that the first official international cricket match took place between the two countries in the mid-19th century. So, not only how cultures, ideas, technologies, etc. diffuse across nations, populations, and so on, but also mechanisms that influence the retention after the initial adoption merit serious attention I think. One possible topic include is modern contraceptive use in developing countries. I guess modeling such mechanisms would require taking into account the models presented by Nathan Collins and Peter Dodds, in addition to signed networks (Doreian). One difficulty of modeling this kind of mechanism is that both structural and individual factors should be considered [[Hirotoshi Yoshioka]].

There is a nice article by H. Peyton Young ('Social Dynamics: Theory and Applications', Handbook of Computational Economics, Vol. II; you can download it at http://www.econ.jhu.edu/people/young/Publications.html) which investigates the evolution of norms or conventions in an agent-based modelling / evolutionary game theoretic setting (in our small library there is also a whole book about that by Peyton Young). It might be interesting to analyze the diverging popularity of cricket in those countries (which can be interpreted as a kind of convention) in this framework. [[Mareen Hofmann]]

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

* Perhaps this concept could be related to ecological food webs and the success of invasive species. The "epidemic" would be an introduced species, and the "spreading of the disease" would be how successful the alien species is within that food web. There are plenty of journal articles attempting to study the success of biological invasion, and I think in addition to looking at the food web networks, generating an agent based model would be ideal! It could be related to your idea, Roozbeh, in that the cities represent "habitats", and the "epidemics" represent the introduction of an alien species.

* [[Karen Simpson]]: Introducing Agent-Based Modeling: Several concepts (external and internal inputs) have been discussed that are said to contribute to whether or not a species succeeds in it's novel environment. These include: how many individuals are in the founding population, the "strength" of any competing organisms (this would be 0 is there are no competitors), the amount resources available, the ability of organism to adapt to the new environment, physiological advantages of new species over native species (i.e. defense mechanisms), and many more. I think we could find properties of ecological foodwebs, and then introduce a species (or epidemic) into the network and see what happens based on these inputs. Let me know your thoughts.

* [[Roozbeh Daneshvar]]: Karen, this sounds interesting to me and I'd like to know more. Shall we have more discussion over it on Tuesday?

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]
: OK then, I am going to start a [[From Topology to Response]] project page. '''We still need a biologist.''' [[Marek Kwiatkowski]]
If you did not do this yet, I suggest you to have a look at "Small Worlds" by Duncan Watts. It containts useful information, models and mathematics on the topic. -[[Massimo Mastrangeli]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

* [[Jacopo Tagliabue]]: Premise: I don't know if it makes sense at all, and even if it fits the project. I was thinking that just not the fact that some areas are connected makes a difference, but also the way they are connected. For example, the synchronization of neurons plays a pivotal role in the proper behaviour of the brain: when some disease (such as [http://en.wikipedia.org/wiki/Multiple_sclerosis multiple sclerosis]) leads to [http://en.wikipedia.org/wiki/Demyelinating_disease demyelination], the signals in the axioms can no more be processed at the right speed. The upshot is progressive cognitive and physical disability. Can we use agend-base models and/or network analysis to better understand what happens (and why, for example, multiple sclerosis may evolve in four different ways)? If someone with some neuroscience background would like to talk about this (or just explain why this doesn't make sense at all),I'd be glad to learn!

[[Karen Simpson]]: This is interesting to me, especially in the case of food webs merely because that is what I am most familiar with. Within an ecological community, there are certain links that depict the dynamics within that community. If we remove a link (or change it somehow, maybe by redirecting it through another organism), the community is stressed. The community may be resilient and the underlying dynamics may shift back to equilibrium. On the other hand, it may lead to the extinction of certain organisms.
One way that these links are changed is by introducing another node into the system, this node representing an introduced species. The success of this species depends largely on its position in the food web and its connecting links. My question (from an ecological perspective) is: Does introducing a non-native species result in different underlying dynamics and patterns? My intuition says yes, but it largely depends on the ability of the non-native organism to succeed in it's new environment. (See my thoughts under "Contagion in Networks" for more on this topic)

* [[Massimo Mastrangeli]]: The topic looks very interesting. I read a lot on Kauffmans' approach and I would probably like to get dirty hands on it. The idea in my opinion is to create a network with a plausibly vast and interesting state space, and explore it using some tools. Analysis of the dynamics of the transitions from one steady state to another might be interesting.

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Problem solving and mating - are they similar?===
I was intrigued by Tom's model of mating and began to wonder whether we can think of problem solving in a similar way. If we were to model problem solving, how would we do it? I'd like to think that problems and solutions are components that combine to generate an emergent property. (After a problem meets a solution--or a solution meets a problem--something new is allowed to emerge. While one instance of problem solving does not exactly create a complex system, many instances may.) That said, there are several questions/considerations to think about before/while we create a proper model of problem solving:

* Given a population of information/knowledge, how can we identify what are problems and what are solutions?
* Actually, which comes first: knowledge, information, problems, or solutions?
* What are some important dimensions of problems and solutions? (These dimensions should inform some kind of a matching probability for problems and solutions.)
* What is the difference between problems and solutions anyway?
* What makes certain kinds of problems and solutions "hang out" in a cluster or neighboring clusters? Is this primarily due to path-dependence?
* When there is a difficult problem (tentatively defined as a problem for which there is no nearby solutions), how can we tell which clusters have the greatest probability of containing the solution(s)? (Can some of the network stuff we learned be of help here?)
* It is of course important to remember that a problem can have many solutions, and a solution can solve many problems, but that they may have different degrees of affinity (just like a ligand-receptor interaction in molecular biology). Also, occasionally a problem needs a combination of several solutions ("AND" as opposed to "OR").

I would love to hear your thoughts and comments, and I'm hoping that someone may actually share some of my interests in figuring out the answers to the questions above! [[Wendy Ham]]

[[Murad Mithani]]: We can look at problem solving as a special case of idea generation. See if you find any parallels between what you have in mind to what is written in the creative process.

[[David Brooks]]: This matching of past solutions or components to new problems leads to several interesting topics of discussion: (1) Shouldn't the process of developing a solution path be treated as a potentially complex system, (2) How do we describe the process without providing a falsely formulaic structure (3) When is the problem, the set of goals, and the process considered to be identified and what elements of the description may hint to the fragility of understanding? I have quite a bit of experience researching and addressing these issues and can help if this becomes a project.

[[Image:Bjh_singles_map.png|250px|right]]'''[[Brian Hollar]]:''' I've been doing some research for my dissertation on '''the effects of gender-imbalances on marriage markets''' and think this would be a fun project to try to model in NetLogo and something that would tie in nicely with Wendy's idea. The basic concept is to try to model the effects of "marriage markets" with more men in them than women or vice-versa, with possible extension to see if this same concept could be expanded to problem-solution matching. Examples of social groups which experience a gender imbalances in marriage markets include: most religious groups, college campuses, some large cities (such as New York and Washington, DC), the African-American community, and some nations (notably China). I am interested in how these gender imbalances affect social norms, marriage and divorce rates, and dating/matching behavior in each of these various groups. Other problem-solution matchings might include: employer-employee, entrepreneur-investor, buyer-seller, etc. If we make the model robust enough, we might be able to extend it to these and other contexts as well.

Some thoughts I have of what to incorporate into the model include:
* The effects of social capital.
* Vision (limited ability to see other agents).
* Open vs. closed groups. (Adjusting rate of entry/exit of agents.)
* Slider-switch for adjusting sex-ratios.
* "Tainting effects" for failure.
* Heterogeneous "attraction" characteristics of each agent.

I'd love to hear ideas anyone might have for this.

[[Wendy Ham]]: [[Jacopo Tagliabue]] shared some interesting thoughts on how to define problems and solutions --> 1) The first one is to define a problem as a lack of knowledge (where knowledge may be theoretical, knowing that, or applied, knowing how) and then use a doxastic logic approach to clarify the notion. The idea is that there is a set of possible states of the world, so-called possible worlds in formal semantic, and our world is one of them: the more you know about the world, the more worlds you can rule out (in the end, with perfect knowledge you will find out which is our world among the infinite set of possibility). You may represent a world as a long description: the set of possible worlds is thus the set pf possible descriptions. Just one of them happens to be THE TRUE description of our world: our tricky task is to find out which one is. For example, since we know that gravity is inversely proportional to distance, we know that all the description saying that gravity is not inversely proportional to distance are false, and cannot be the description of our world. The idea that increasing knowledge means reducing possibilities is analogous to the idea that acquiring information decrease the uncertainties. A problem can be modeled by a set of possible worlds, where each world in the set may actually be the world we live in. A solution is a function from this set to a sub-set of the set (or something similar, I haven't think in depth about this). 2) A second approach may be incorporating some notion from formal learning theorem, where the scientific enterprise is modeled using result from recursion theory (look at this: http://www.princeton.edu/~osherson/papers/hist25.pdf).

[[Wendy Ham]]: My thought originally was to use ABM to model a population of problems and solutions by: 1) determining what counts as problems and as solutions, 2) assigning dimensions to problems and solutions, which determine how they subsequently form a cluster in someone's head, and 3) determining how these heads subsequently form a larger cluster of disciplines, 4) demonstrating that compatible problems and solutions can occasionally end up in faraway clusters (such that they need to be brought back together to generate innovation - possibly using random shortcuts a la those found in small world networks). Jacopo's ideas are making me reevaluate these thoughts...

[[Wendy Ham]]: (Credit to [[Nathan Hodas]]) To be a bit more empirical, it would be interesting to examine a major innovative problem solving event in history that involve the cross-pollination of ideas from several disciplines, e.g., the discovery of the double helix structure, and ask: what kind of structure or system could we have put in place to make such event occur sooner? In other words, what can be done - structurally speaking - to expedite the 'mating' of problems and solutions from traditionally separate fields?

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?
* [[Steven Lade]] Wendy, can you give some examples for evolving systems moving from "highly modular to highly integrated"? Also Roozbeh I don't understand what you mean by "behavior of complexity". Maybe we should talk.

* I like this idea. Clearly it needs some more fleshing out, but its a good direction. One thing to think about when you see modularity biologically is whether certain 'modules' can be reused multiple places. Komolgorov complexity is something that you might look at... [[watson]]

* [[Wendy Ham]] Thanks Watson, I will check out the Komolgorov reference. To Steve: Roozbeh and I started thinking about this when we discussed how some societies have evolved from having a clear demarcation between the gender roles (e.g., men work and make money, women stay home and take care of kids) to not having this demarcation anymore (i.e., gender equality, etc). So at least with regards to gender roles, these societies have evolved from being modular to being integrated. As a general rule, I tend to believe that modularity is important for allowing innovation and adaptation, which are important in a changing environment, whereas integration is good for efficiency. So, the question here, for example, is whether these societies have reached a certain level of "stability" such that modularity is no longer important. Aside from this example, people have shown that bacteria that live in changing environments tend to be modular, whereas those that live in a stable environment tend to be more integrated. Furthermore, organizations (e.g., business firms) also tend to become more integrated/tightly coupled as they mature.

* [[Roozbeh Daneshvar]]: Steven, we have a complexity landscape here that imposes where the system should stay. That is normally somewhere between order and disorder that gives the system the highest capabilities. I also associated order with modularity and disorder with dis-modularity ([[Wendy Ham]] seemed to agree with this!). Now the amount (and perhaps form) of modularity has changed. So, my intuition is that the complexity landscape (which determines the future behaviors of the system) is changed. This is what I meant by change in "behavior of complexity". I meant that the dynamics of that complex system is changed and hence, the equilibrium is somewhere that did not use to be equilibrium before this (there were some topics related to this area on Monday June 15 lectures).

* [[Gustavo Lacerda]]: Watson, Kolmogorov Complexity is a very general concept. Do you mean "motif discovery"?

===Evolving nanomachines===

Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

* [[Roozbeh Daneshvar]]: I am interested in this. Although I am curious to know what methods do you want to pursue for this matter? ABM? By the way, I deeply believe that this is the kind of research which determines the future of robotics!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]
* [[Jacopo Tagliabue]]: It could be interesting to embed insights on risk-seeking and risk-averse behaviour from prospect theory and behavioural economics. I am also interested in agent-based simulations of a simple economy, where agents may use different heuristics (rational decision theory, Simon's model, Kahneman and Tversky theory, etc) to decide what to do. It is often said that in the market "errors cancel each other out", leaving a optimal or quasi-optimal global outcome: but is it true? And what's the relationship between individual strategies and this dynamics?

*[[john paul]]: I'd like to throw my weight in with this one to see how this is addressed. Mr. Hodas and I have been talking about real-world risk associated with credit and defaults as noise in a system, and directed flows of current cash, credit and derivatives as three possible visualizations. Ideally we can pull out some real-world credit data and begin to construct a scale market of one economy (or sector of an economy, like government spending) and then hopefully either scale that up or adjust as needed to other data.

* [[Wendy Ham]] Do you guys consider credit default swaps (CDS) as a special kind of financial instrument - one that almost completely lacks inhibitory mechanisms and thus is able to grow indefinitely? (Analogy to cancer cells?)

===Creative Process===
This is a very preliminary attempt to analyze the creative process in order to identify how we come up with ideas.

Creation of ideas as a process of random combination of concepts and connections taking place in the subconscious. Most of these ideas are filtered before reaching the conscious. Those ideas that rise above the conscious are new to the individual, some of which may also be new to the world. We generally classify the latter ideas as creative. Furthermore, the creativity literature refers to ideas as creative only when they are immediately useful in solving some problem or condition.

The existing concepts and connections can be considered as nodes or agents. A new idea can be a combination of at least 2 concepts + a connection or two connections, or some superposition of them. The following rules obey at the subconscious level:

1. The random process is taking place all the time with a single combination at one time

2. Each idea (which is a newly created concept or connection) attempts to pass through a filter. It either passes through or it doesn’t. If it does pass through, the idea is recognized and the coupling between the concepts/connections is raised. Each increase is by a factor of 0.1 (starting from 0) of the existing coupling until it reaches a maximum of 1. If it doesn't pass through, it ceases to exist (however, it may reappear later and given a change in the characteristics of the filter, they may be allowed to pass through).

The rules that define the ideas that pass through are:

1. The database of filters (individual’s understanding of the external environment, self control, etc.) defined in terms of what concept and connection associations are allowed to pass through as well as 20% deviation in them. [Ques: How can the deviation of a concept be evaluated numerically?]

Using complexity theory:

1. Agent based modeling can be used to identify how newer ideas rise to the level of consciousness, how the filters affect them

2. The network analysis can be used to understand how the coupling affects the creation of new ideas (concepts/connections)

[[Murad Mithani]]

[[Wendy Ham]]: Hi Murad, there are definitely some overlaps in our interests.

===The Biological Evolution and Social Learning of Cooperation===
Both evolutionary biologists and social scientists have convincingly shown that cooperation can emerge and persist in human society. Although the two have employed the same methods (game theory and agent-based modeling), they have proposed different mechanisms: on the one hand, biological evolution based on kin selection, group selection, the “green-beard” effect or reciprocity and on the other, socio-cultural adaptation due to social learning. The two mechanisms act on different time scales and make different assumptions on the agents’ behavior (fixed vs adaptive) and the underlying dynamics (reproduction vs imitation). I think it will be interesting to combine the two mechanisms in a single agent-based model and to explore how they relate to each other. Following standard practice, the model will consist of agents on a spatial grid or a(n evolving) network who play a game such as the Prisoner’s Dilemma or Hawk-Dove. [[Milena Tsvetkova]]

Nice. Indeed, one can reinterpret things to some extent and understand cultural and biological evolution in similar veins. In both sorts of evolutionary processes, individuals can be assigned fitness. In the biological case fitness refers to ability to leave offspring, while in the cultural case fitness might refer to ability to be imitated by others. So, reproduction can be understood as genetic or cultural. Mainstream evolutionary biologists use these interpretations, but I wonder if they break in some cases. [[Mauricio Gonzalez-Forero]]

[[Mauricio Gonzalez-Forero]]: Mareen, Varsha and I have sketched a potential agent-based model for the evolution of division of labor. It needs more thought, and the input from social sciences people would be very valuable. The model considers two labors performed by agents and a cooperative trait. Given spatial structure and dispersal restriction, we expect the cooperative trait to allow for the division in labor to evolve. It should be straightforward to implement in NetLogo. After an analysis of the simulations, it would be neat to synthesize the model analytically. Interested people are certainly welcome to help!

[[Gustavo Lacerda]]: Mauricio, this sounds interesting.

===Modeling Gossip Networks===

It could be neat to develop a model of gossip networks. If you define gossip as information passed between 2 individuals (call them A and B) about a third party (C), then the act of gossiping has the potential to change the status/connection strength of all parties involved (e.g. maybe strength A-B, and weaken A-C and B-C bonds). Essentially passing information along a path in the network changes the value of BOTH edges in the direct pathway as well as other edges in the network. These are just preliminary ideas, but perhaps we could model how gossip tendency/frequency influences the structure of a network. Also, is it possible for individuals to influence their location in a network (e.g. increase centrality) by changing their gossiping frequency? (Although this is potentially a complicated rather than complex model idea...) Let me know what you guys think! [[Allison Shaw]]
* [[Milena Tsvetkova]]: This is a very interesting idea from sociological point of view. The effect of networks on the spread of gossip is well understood: some of the social dynamics at play include biases in the selection of trusted third parties (one draws a sample of information consistent with one’s predisposition), the reinforcement of opinions in dyads due to an etiquette mechanism, the exaggeration of information in triads due to echo effects. However, I am not aware of any studies that investigate how the spread of gossip affects network structure. My work is on the coevolution of behavior and social networks so we should talk!

* XOXO [[Chang Yu]]:Interesting! Gossip is not always bad. If we can model its spreading mechanism, it could help especially when you want to spread information unofficially. I get some inspirations from Tom’s last lecture on Friday. In the gossip network, what kind of properties of these agents can speed up or reduce information spread, the range of social circle, poverty, wealth, the information itself, or even the locations of houses in a community? I think we may model the different spreading results under different properties.

[[David Brooks]]: I agree that this concept of Gossip Networks is a generic for the analysis of several potential problems. I would like to talk to you about your intended direction and methods.

[[Gustavo Lacerda]]: sounds like some interesting dynamics, but how are you going to get data?

===The Emergence of Meaning and the Evolution of Language===

There are several attempts in the philosophical and psychological literature (see [http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Lewis’ work] on convention and [http://en.wikipedia.org/wiki/Paul_Grice Grice’s] analysis of meaning) to analyze the emergence of meaning. Most accounts (it not all) make extensive use of meta-representations, that is, the ability we have to understand other people intentions and “read” the content of their mental states. There are two problems with these theories: first, they are developed in a static fashion, while it may well be the case that the emergence of meaning is the result of a continuous, adaptive process; second, they seem to be plainly false, at least if we are willing to say that people affected by autism – and thus unable to read others mind – understand and produce meaning (see this recent paper by [http://people.su.se/~ppagin/papers/Autism5D.pdf Gluer and Pagin]).
Brian Skyrms and others used evolutionary game theory to evolve proto-languages, so-called “signaling games”, to understand how meaning dynamically emerges without meta-representations (it turns out that meaning can be understood as a form of equilibrium in these evolutionary dynamics). It could be interesting to further develop these insights, adding more realistic features to AB models:

* adding noise
* explore the same game in different topologies and see if the emergent behaviour depends in some way on constraints on how agents move
* see if it is possible to evolve language with a proto-grammar

These are just some preliminary considerations. Let me know what you think! [[Jacopo Tagliabue]]

* [[Gustavo Lacerda]]: Jacopo, I'm a fan of Simon Kirby's work.

=== Biological Pathways ===

Loosely defined, biological pathways are networks of molecular interactions that achieve a specific biological function. I'm interested in using the information we already have about them in the analysis of microarray data. I have a bunch of half-baked ideas; here are two.

* [[Steven Lade]] I'm interested in one or both of these.
* [[Gustavo Lacerda]]: Me too! I'm interested in statistics in the "small n, large d" setting, sparse regression, and incorporating structural knowledge through e.g. strong Bayesian priors.

==== Many hits vs. critical hits ====

[[Rosemary Braun]]

Microarrays assay 10^5-10^6 biological markers per sample. The most basic analysis is to ask whether each marker, individually, is disease-associated; common multi-marker approach is to sort the markers based on the magnitude of their association with disease, and then ask whether the high-scoring markers are over-represented in some pathways (biological interaction networks). By systematically performing an enrichment analysis on all known pathways, it is possible to elucidate which ones may play a role in disease. (cf [http://www.ncbi.nlm.nih.gov/pubmed/16199517 GSEA].)

On the other hand, it is well known that the centrality of a molecule in the biological pathway is strongly correlated with its biological importance -- the lethality of knocking out a gene is related to its centrality (eg [http://www.ncbi.nlm.nih.gov/pubmed/11333967 Jeong 2001]). This finding has been used to study individual markers 'within' a given pathway to predict which ones would be the most biologically relevant (eg by ranking the markers based on centrality, ([http://www.ncbi.nlm.nih.gov/pubmed/18586725 Ozgur 2008]).

One of the drawbacks of GSEA-type enrichment approaches is that they do ''not'' consider the centrality of each marker, ie, they are pathway-topology-ignorant. To the best of my knowledge, while centrality has been looked at to examine the importance of individual genes to a given function, it has not been incorporated in enrichment analyses. I would like to answer the question "is a pathway more ''critically'' hit with disease-associate alterations than would be expected by chance alone" using a centrality-aware scoring function.

One very naive way to do this would be to simply scale the single-marker association statistic used in GSEA by the centrality of the gene in the network. This raises a question of its own, however: to what degree do the results depend on the severity of the scaling?

Anyway, that's one half-baked idea. [Resources available: tons of data; adjacency matrices for pathways represented in KEGG, BioCarta, Reactome, and the NCI/Nature pathway database; useful ancillary functions in R; a cluster for permutation testing/exploring the parameter space.]

==== Gene expression time-course spectra ====

[[Rosemary Braun]]

Consider all the genes involved in a given pathway. Consider, also, a set of data that gives us the expression values for each gene at a handful of timepoints, eg, before (t=t0) and after (t=tf) an environmental exposure.

Next, suppose we describe the activity of that pathway by completely connected directed graph, for which the weight of the edge from gene_i to gene_j is given by MI(gene_i(t=t0),gene_j(t=tf)) (in the case of multiple timepoints, we could extend this -- eg transfer enropy). That is, the weight of each directed edge from gene_i to gene_j would tell us how well gene_i at t=t0 predicts gene_j at t=tf.

(I suggest the complete graph, rather than using the known pathway topology, because in practice the time differences tf-t0 may result in multiple "hops" -- so we may have correlations between next-next-neighbors rather than nearest neighbors, etc.)

So, we now have a description of signal propagation through the pathway over the time t0->tf, which we could summarize using the eigenvectors of the Laplacian. If we have two classes, eg cells which do/don't respond to the exposure, will we see statistically significant differences in the spectra for certain pathways, and thus infer that those pathways are involved in the response?

Possible pitfall: most time-course experiments only have a handful of samples for each timepoint.

=== Interacting distribution networks ===

I'm interested in thinking about evolving, interacting (re)distribution networks. Many large-scale aggregate networks are actually composed of several essentially independent subnetworks (e.g. individual airline carriers, local utility distribution companies), each of which takes into account the other agents' actions. While there may be interesting structure in the aggregate view, we know that the system followed an evolutionary path affected by interactions and should expect evidence of that process in the network structure. In other words: let's think of an agent-based model where each agent is a subnetwork maximizing some objective in a shared environment with constrained resources. I know there is some work on creating networks using games, but the agents are typically single nodes - see [http://portal.acm.org/citation.cfm?id=872035.872088 On a network creation game]

There may be some reasonable biological applications (for example, competing fungal hyphae networks; there was a recent work which modeled individual fungal growth - see [http://rspb.royalsocietypublishing.org/content/274/1623/2307.abstract Biological solutions to transport network design], possibly root structures, functional neural modules?) or social applications (competing idea networks). At the moment I'd love to think about anything other than airline networks.

There are many directions to take this depending on the system in question. Off the top of my head:

* Under what conditions (i.e. which games) can competing entities coexist? In this case, do they all form similar network structures, or do different structures allow them to occupy noncompeting niches?

* How does the game structure affect equilibrium network structure?

* Apparently certain environments support different size networks (small-scale regional carriers, large-scale national/international carriers) - is this realizable with an identical objective function for all agents?

I know basically nothing about game theory, and I'd love to take this in a biological direction. I'm also happy to go off in another direction if this inspires a tangential idea. [[Daniel Wuellner]]

[[Caroline Farrior]] This sounds pretty cool. I don't know much about networks, or airlines, but I do know about evolutionary game theory.

[[Randy Haas]] I'd like to talk some more with ya'll about this. It sounds relevant to a problem I'm interested in, which involves the emergence of settlement hierarchies in 'prisitine' state societies. I'm playing with the idea that such hierarchies are a redistribution solution to optimal resource allocation. Do you see a connection with Christaller's [http://en.wikipedia.org/wiki/Central_Place_Theory Central Place Theory]? It's an oldy, but it seems relevant that a cited critique of the theory is its inability to capture dynamic process.

[[David Brooks]] I have some experience in the analysis and specification of multi-modal and multi-step transportation systems and would like to discuss your intended direction and methods.

=== Network structure of personality ===

[[Sean Brocklebank | Sean]] is interested in using the methods [http://www.santafe.edu/events/workshops/index.php/CSSS_2009_Santa_Fe-Readings#Scott_Pauls:__Partition_Decoupling_for_Roll_Call_Data presented] by Scott Pauls at SFI on Wednesday to analyze the structure of personality as revealed by personality psychology's canonical test, the NEO PI-R, and it's freeware version, the IPIP NEO.

These surveys consist of 240 and 300 questions, respectively, and have been analyzed using traditional factor analysis to reveal the Five Factor Model of personality (FFM, see [http://en.wikipedia.org/wiki/Five_Factor_Model Wikipedia article]). But there is much debate within personality psychology about the exact structure of the factors, and particularly the higher order correlations among them. Traditional factor analysis is not much use in resolving these disputes, but that is just about the only method which has been used so far. I've spoken to Scott Pauls about this already, and he says that his method might be useful to help to resolve the issue (see his comments below).

I've got a dataset of about 1000 responses to the NEO-PI-R and 21,000 responses to the IPIP NEO, and I can get access to a smaller dataset which also includes some info on FMRI imaging and some other personality tests if necessary.

This is not a subject which I was originally planning on pursuing when I came to the CSSS, but I think that the central importance of this test to personality psychology means that the project will have a reasonable chance of getting published regardless of the results, and anyone working on it should learn some cool data analysis techniques along the way.

If you'd like more information about what I've written here, note that I will be talking about the subject over lunch on Tuesday the 16th. Just find my table (or avoid it, depending on your preferences).

I'm in. [[Marek Kwiatkowski]]

[[Murad Mithani]]: I would like to know more about this.

I'd like to talk more. [[Casey Helgeson | Casey ]]

[[Scott Pauls]]: Some comments on this idea.

I think this is a very interesting application of the PDM or some variant of it). One of the aspects of the "Five Factor model" is the controversy around the selection of the factors and their putative independence (they are not). The collection of tools we use will allow for a data driven extraction of factors on multiple scales. I suspect, although it is not a given, that the top layer of factors will reflect to some extent the "five factors" already used. However, it will give detailed information on the relationships between the pieces. Moreover, the multi-scale decomposition should yield a very textured description of the personality factors and their interactions.

A couple of technical points - given the length of the data series (200-300 questions), I would probably limit the analysis to roughly 150 respondents at a time. The wealth of data available means that one can do multiple experiments using ~150 members allowing for a good analysis of the robustness of the factor results.
[[Guimei Zhu]] interested in it, i am also curious on persons.

===Modeling behaviors between students and teachers===

[[Chang Yu]]:I’m doing some research about a marginalized group of high school students under Chinese elite education policy. These students can’t handle the exam-oriented school circumstance and get ignored and even discriminated. Some of them have character defect. From the six-month field research and data analysis, I find teachers’ attitudes and behaviors are the most significant factors when children grow up. Now I hope to use NetLogo to model the bidirectional behaviors between students and teachers.

Here are some draft ideas I’m thinking about:
* Student’s properties: learning skill (Sp1), normalized character (Sp2), normalized behavior(Sp3) ,acceptance to teacher (Sp4)
* Student’s actions: be willing to learn (Sa1), be willing to associate and communicate (Sa2)
* Teacher’s properties: salary (Tp1), sense of achievement (Tp2)
* Teacher’s actions: encourage students (Ta1), organize social activities (Ta2)
* Rules: (I’m still thinking)
** If teacher acts Ta1----> Sa1----> Sp1 + 1, Sp4+1----> Tp1+1, Tp2+1
** If teacher acts Ta2----> Sa2----> Sp2 + 1, Sp3 +1---> Tp1+1, Tp2+1
(Also have the negative rules and combination rules, like Ta1+Ta2--->Sa2----> Sp2 + 1, Sp3 +1)

SOS!! If you guys have any ideas, suggestions, help about NetLogo, please please please tell me !

Interesting idea, you might want to take a look at the following working papers (they are mathematical (math. epidemiology) in nature):

Katie Diazrlene, Cassie Fett, Griselle Torres-Garcia, Nicolas M. Crisosto (2003) The Effects of Student-Teacher Ratio and Interactions on Student/Teacher Performance in High School Scenarios. MTBI BU-1645-M

Abstract:
We develop a model that incorporates the impact of sudden-teacher ratio on the performance dynamics of both teachers and students. The model assumes that the members of both populations may be found in three dynamics states: positive, discouraged and reluctant. The role of complex nonlinear interactions between students and teachers, as well as the role of recruitment and intervention, are studied via analytic and numerical studies. Using center manifold theory we find conditions for the existence of a backward bifurcation that support endemic stationary states below the critical threshold value, R0 < 1, when normally only a positive environment would be supported. Our simulations show that in order to maintain a positive environment for students and teachers, R0 must be reduced significantly. Since R0 is a function of student-teacher ratio this can be achieved by decreasing class size.

Corvina Boyd, Alison Castro, Nicolas M. Crisosto, Arlene Evangelista, Christogher Kribs-Zaleta, Carlos Castillo-Chávez (2000) A Socially Transmitted Disease: Teacher Qualifications and High School Drop-Out Rates MTBI BU-1526-M

Abstract
The main goal of this study is to quantify the impact of teacher interactions on student achievement to facilitate recommending policy strategies that minimize high school dropout rates. This study derives a system of differential equations that examine the effects that teachers have on minority high school students' learning experience in California and Arizona. The first mathematical model focuses on the impact that teacher dynamics have on a school's faculty composition. Teacher's dynamics are coupled with a second system that models student responses to teacher preparation and experience in order to investigate the effects of these interactions on high school dropout and completion rates.

If you cannot locate the paper online, let me know, I know some of the authors of the two papers. Alhaji Cherif

[[Chang Yu]]:Thank u Alhaji! I really appreciate if you could tell me how to get the papers. Have you done any research in this area? We should talk about it! Thanks.

===Music Rhythm Pattern Generation with Hierarchies and Dynamics (PROGRAMMERS WANTED!)===

Western based music comes in boring measures. 4 beats, 16 beats and then repeat plus a little modification. Boring!

Even exotic music from India or Bali sticks to one particular measure ... even if it's some bizarre integer, a prime number say, like 17. But what if we introduce hierarchies of measures?

So lets say a measure is one minute long. Between every beat of your 4 measure I introduce 7 beats. And between the first four of those I introduce 2 beats; between the 2nd 5 beats and between the third and fourth 3 beats each. What does that music sound like!?

Clearly there is synchrony every x beats between different patterns but in between there is something which bears some relationship over time but takes a little listening to understand.

What music is most pleasing? What do you want to hear more of? What is too complicated/random and what is too boring?

I have worked previously on such a system written in Java called the [http://mf.media.mit.edu/pubs/conference/EmonicReport.pdf Emonic Environment]. But this was many years ago and I have learned much about much since then.

What can we create now?

A few people have exhuberated interest including Murad and Casey but I need at least one or two other people who are capable of contributing to the implementation before we can go ahead with the project.

Do you find yourself fascinated by your own attraction to different sorts of rhythm? Do you sense that this summer school could be a pathway for reigniting your own passion for creativity and expression, while maintaining some connection to science? Both Liz Bradley and Peter Dodds encouraged us to nurse the flame fueled by playfulness and creation, to keep ourselves engaged by having fun and staying curious. If a group of us got together and really inspired one another with our ideas and passion, maybe we could make something compelling and bring out the curious 5-year-old latent in all of us.

What are interesting ways to create hierarchies and change them dynamically? What sort of dependence should one structural or functional parameter have on others in order to create sequences of sounds that aren't just random but rich in some sense?

[[watson]]

*[[Massimo Mastrangeli]]: I guess Watson is referring to polyrithm(ics), which is a way of layering musical compositions with parts having each its own signature/tempo. This is traditional in some african cultures, and is anyway sometime used also in western modern music (e.g. Strawinski's "Rite of spring"; also, those who know of metal bands like Meshugga, Pain of Salvation and similar can have an immediate idea). Odd time signatures are also quite common in muzak/klezmer tradition (and progressive rock!). They bring an overall impression of dynamism and energy, given that the beat patterns can be richer and more unpredictable than in common 4 beat time signatures. I like quite a lot this type of music (you had doubt still? :) ), I could contribute to the project with my musical experience. It can be a nice occasion also to learn about new tools. The project may have some substantial physiological/esthetic components to it.

===Rebellion===
The results of Iran's recently held presidential election (June 12, 2009) is very controversial. Demonstrations are being held across Iran and some have turned violent with a few fatalities reported. Demonstrations are also being held in major cities across the world. It is reminiscent of the Iran's revolution about 30 years ago. So, here is an idea for an agent-based modeling of a rebellion; what does it take to tip the balance to successfully influence the election process for a possible re-election? What kind of networks to model the rebellious groups? Or, to take it to the extreme, what does it take to have another revolution?
[[Mahyar Malekpour]]

[[David Brooks]] This seems to be the same problem as the Gossip suggestion from above. Perhaps we could combine the two adding factors such as participation hesitation to represent the stability that must be overcome to induce action (participation in gossip or revolution). Perhaps we could get together with the gossip model team to discuss the potential.

[[Scott Pauls]] There are interesting discussions in the political science literature concerning revolutions in relatively authoritarian regimes. [http://fds.duke.edu/db/aas/PoliticalScience/faculty/t.kuran/publications T. Kuran] has spent most of his career on such models. One of his first papers on this is T. Kuran, Now out of never: The element of surprise in the East European Revolution of 1989, World Politics, vol. 44 (October, 1991), pp. 7-48.

===Mesoscopic self-assembly of passive functional components===
Self-assembly is being recognized in the field of microelectronics as a viable way to assemble multifunctional systems in a cheap and efficient way. Beside speeding up the assembly procedures that are now standard (e.g. pick-and-place), self-assembly is enabling the construction of unique systems which could otherwise be not possible. This is particularly important and promising for devices whose size ranges from microns to millimeters, i.e. devices which are too large to be assembled by supramolecular assembly and also too small to be assembled by robotic assembly.

This project would aim at designing ensembles of electronic components (i.e. devices endowed with electromechanical interconnecting structures which constraint the possible arrangements) and the constraints on the physical environment that would result in the autonomous formation of standalone and functional systems. It is a type of static self-assembly, where the energy is dissipated only while the system is reaching its thermodynamical minimum energy state. I propose agent-based models which should encode physical forces among components and/or templates (e.g. gravity, capillarity, electromagnetic fields, chemical forces), and should bring about a plausible dynamics and parameter space for successful assemblies.

[[Massimo Mastrangeli]]

===Guns, Germs and Steel: Modeling the fates of human societies===
In his hugely influential book ''Guns, Germs and Steel'' J. Diamond tries to answer a question once posed to him by his field assistnat: "Why is it that you white people developed so much cargo and brough it to New Guinea, but we black peope had little cargo of our own?" The book is a verbal model, suggests that the fate of human society is a product of the locally available resources, such as which crops could be domesticated, and the geographic configuration of regions, which then allowed these resources to be transmitted. The book has many intriguing and testable elements. In effect, Diamond describes a network model, where success is determined by connectedness and information transfer. The ideas of GGS can be tested by taking the underlying patterns of resource distribution and feeding them into an explicitly specified the information transfer networks. You can even permute various parts of the system and see whether you would still get the same historical dynamics. [[Alexander Mikheyev | Sasha]]

[[Randy Haas]] Sasha, I have lots of thoughts on this, and it is similar to a problem I've considered posting. I can certainly contribute an anthropoloigcal perspective on the problem, and the archaeology of agricultural origins is an area of specialty for me. let's talk about it.

[[Alhaji Cherif]] There is a nice book by Peter Turchin Historical dynamics where he studies cliodynamics and has looked at some of these questions from both empirical and mathematical models. He has written some papers too on the subjects, too. His papers might be a good starting point.

[[Nathan Hodas]] I'd like to be in on this. I've pondered a good deal about this since reading the book. Maybe we should contact Jared Diamond?

===Regional language differentiation===
The goal of the [http://dare.wisc.edu/?q=node/1 Dictionary of American Regional English] is to capture how colloquial expressions vary across the United States, based on interviews conducted in the mid-20th century. Check out this [http://dare.wisc.edu/?q=node/4 sample entry]. There is also a collection of recordings where >800 people from various regions read the [http://dare.wisc.edu/?q=node/44 same text]. I am not exactly sure what one can do with this resource, but I maybe someone can come up with a good idea. [[Alexander Mikheyev | Sasha]]

===Deconstructing CSSS09===
One fun and easy application of network theory would be to look at ourselves at the end of the course, using an anonymous survey. What was the social interaction network? How frequently was there ''discussion'' between disciplines and did that lead to productive final projects? Is there a link between the social and final product networks? In prinicple, these data can potentially be linked to those collected by SFI at the beginning of the summer school. This could be an interesting way to see how the summer school (and more broadly interdisciplinary interactions) actually works. These data mihgt also be useful for planning the structure/composition of future classes. [[Alexander Mikheyev | Sasha]]

[[Wendy Ham]]: I agree Sasha, would love to help out with designing surveys, etc.

===Biodiversity, evolution, modularity--ideas from Doug Erwin's lecture===
Here is a list of ideas mostly inspired by Doug Erwin’s lecture. I haven’t written anything very in depth due to lack of time but I think it would be fun to think about how to model any of these topics. Many of the topics are highly interrelated. I would recommend looking at Doug’s 2007 article on the readings page if interested.

How to model biodiversity.
Why would greater bio diversity rise out of extinction?
Does evolution reach sort of a stability point when all the niches are ‘full’ and is there is a lot of competition?
Does lack of competition (due to extinction or whatever) create the opportunity to diversify more?
Why does biodiversity cluster?
Two models in the paper:
Genetic or developmental hypothesis: mutation driven model of change. Corresponds to ‘supply driven’ innovation in economics
Ecospace hypot: variations in ecological opportunity control the success of major new morphologies. Corresponds to ‘demand driven’ innovation.
Genetic kernels
How are they developed?
Why did they all develop at the same time after extinction?
Why did animals develop kernels and not plants?
Modularity. http://en.wikipedia.org/wiki/Modularity_(biology)
Why do biological organisms develop modules?
How many components make up one module?
Is there a difference in the modularity of ‘higher’ versus ‘lower’ level organisms? (There is well studied modularity in the central nervous systems of long swimming organisms such as leeches or electric eels).
Why do nonvertebrates develop locomotion modules (repeating, identical body part segments hooked together in some way to allow motion) but vertebrates do not (only have 2 or 4 legs).
[[Corinne Teeter]]

===Economic Geography in the Lake Titicaca Basin===

By A.D. 1200, the Tiwanaku Empire reached an apex in population and social complexity. The distribution of settlement sizes at that time approximated a power-law or log-normal distribution with the urban center of Tiwanaku as the capitol. Archaeologists such as Stanish, Janusek, and others have argued that, while economic factors drive early settlement patterns, differential growth in settlements is primarily a function of competing political polities. They argue that polities with the most integrative religious traditions continued to grow relative to surrounding communities. In this project, we wish to explore the possibility that a simpler economic /demographic model can explain the emergence of such settlement patterns through time. Specifically, we intend to follow the general modeling framework created by Paul Krugman, which he calls the “New Economic Geography.”

Our working method would be to use the behaviors proposed by Krugman in an agent-based model that predicts the spatial distributions of the empirical settlement patterns in the Lake Titicaca Basin. The model explains settlement change by assuming that separate forces encourage production to simultaneously concentrate and disperse. The centrifugal forces that encourage dispersed production come from the uniformly distributed farming population which demands goods. Transportation costs mean that production should be close to farmers and therefore dispersed across the landscape to serve farmers efficiently. At the same time, we assume that increasing returns to scale obtains for production, meaning that larger production facilities can produce at lower marginal costs. This centripetal force encourages production to be centered in one area. In addition, production centered in one area attracts more workers and increases local demand for the product. Depending on model parameters, these dynamics can give rise to a dominant production and population centered, as seen in the Lake Titicaca data.

We don’t think we can take this project on by ourselves, so we are writing to see if we can get 1 or 2 more people interested and then move forward. If interested, please talk to [[Randy Haas]] or [[Jeremy Barofsky]]. Thanks!

CSSS 2009 Santa Fe-Tutorials

2009-06-16T05:13:03Z

Nhodas: /* (Evolutionary) Game Theory */

{{CSSS 2009 Santa Fe}}

Feel free to organize your own tutorials. 
Post tutorial schedules here.

'''For ease of scheduling, please make amendments to the schedule'''

Also, a [[tutorial outline page]] wouldn't be bad.

== Information theory ==
Discussions and resources are archived at the [[CSSS 2009 Santa Fe-Information theory tutorial]] page. Also see this page for recent/current follow up discussion...

== [[MATLAB / Mathematica]] ==
[[Steven Lade]]: Hiro, I will happily give a tutorial on the basics of these languages (but only the basics, I don't use any of the fancy bits). Anyone here know if the labs here run MATLAB/Mathematica?

If there is anyone else interested (leave your name) I'll arrange a time, otherwise I'll talk directly with you Hiro.

[[Karen Simpson]]: I would also like a tutorial on MATLAB. I know some things about it, but never really learned the basics so it takes me a long time to do things. The computer labs should be equipped with MATLAB. I also have a fairly updated version on my laptop.

[[Lucas Lacasa]]I'm also interested! Also know some basics but I usually program in Fortran so I'd love to learn it.

Steven, many thanks! I think that the computers in the lab have MATLAB. If not, we can access it through my school's server as long as the internet connection is stable. [[Hirotoshi Yoshioka]]/lakiaypayaska

[[Brian Hollar]] I'm also very interested! I've never used these languages, but have some basic knowledge of Java, NetLogo, and FORTRAN. I'd appreciate the help and would love to learn.

[[Milena Tsvetkova]] Count me in! Steve, what is a good time for you?

[[Wei Ni]]: Hi ppl. Could I join in? I know MATLAB and I would like to further polish my MATLAB skills. Meanwhile I want to learn Mathematica.

Thanks for coordinating the tutorial and for 'setting up' the facilities, Steve and Lucas.

[[Chang Yu]]:Hello,I'm here! Thank you guys for this tutorial.I'm a kid in MATLAB and very courious about it.I know M is a giant in Mathematics,Statistic Analysis,Images and Genetic Algorithm and really want to learn more about that.By the way, I have the installation of Matlab 7.0 and I'll take it in my flash drive.If you have updated version, that would be better.

[[Steven Lade]]: Too late for this week I think, and next week is pretty full, so let's aim for 7PM Monday June 22.

[[Elliot Martin]]: I would be happy to help out with the MATLAB tutorial if you want. I don't use any of the fancy packages either though.

[[Marek Kwiatkowski]]: Excellent tutorial idea, I'll be there.

[[Margreth Keiler]]: I would like to join. I've never used these languages but I would like to get an idea.

[[Liliana Salvador]]: Good idea. I am in! I have some knowledge of matlab (but never used very fancy packages). I would like to learn Mathematica.

== [[Statistical physics: applications to complex systems]] ==
[[Lucas Lacasa]]:Statistical physics is a rather huge field, so I'm thinking on building a tutorial that focus on some specific topics related to complexity science in a chat-like level, namely:

- Fundamentals of statistical mechanics: ensembles, partition function and associated thermodynamic quantities (free energy, entropy) and some other basic stuff.

- Critical phenomena: Phase transitions in physical, social, and algorithmic systems. Self-organized criticality as the counterpart of a critical phase transition. Relation between phase transitions and local bifurcations of dynamical systems.

- Monte Carlo simulations, ergodic theorem

- Specific example gathering all of the above: Ising model

- Other related topics that you may like to listen to

Maybe we could schedule it for next week, something like next wednesday (17) at 7pm? (provided that no PRIORITY things such as basketball or soccer games are scheduled). If anyone else is interested please leave your name. Depending on the 'audience' we can fix one place or another...

*[[Steven Lade]] Fantastic, I'm in. But there is the 'Music on the Hill' 6-8pm Wednesdays. Is 8pm getting too late for 'work'? Or I miss an hour of the music. (Tuesday and thursdays there's the nonlinear dynamics labs)

*[[Lucas Lacasa]] You're right. What about friday 19 after lunch?

*[[Roozbeh Daneshvar]] I'd like to join this one.

*[[Corinne Teeter]] I'd also like to come.

*[[Barbara Bauer]] I'm interested too.

*[[Hirotoshi Yoshioka]]: I'd like to come.

*[[Gustavo Lacerda]]: I'm interested.

*[[Allison Shaw]]: I'd like to join as well.

*[[watson]]: maybe you guys should rope

*[[Andrew Berdahl]] into this as well. sounds like he could have something to add...

*[[Elliot Martin]]: I could talk a bit about self organized criticality, and a bit about some mean field theory if you want some company

*[[Jacopo Tagliabue]]: I'm interested, especially if tutorial includes a for-dummies part.

*[[Massimo Mastrangeli]]: I will join you!

*[[Lara Danilova-Burdess]]: Excellent. I'd like to come.

*[[Mareen Hofmann]]: Super! I'm in as well.

*[[Margreth Keiler]]: I'm interested, and join the for-dummies part ;-)

*[[Liliana Salvador]]: I am in!

*[[Varsha Kulkarni]]: I'd like to come!

== [[Mathematical modeling in ecology ]] ==
If we want to do this before the talks next week, Monday evening?

*[[Barbara Bauer]] whoah, I wish i had an overview on the whole field...
But a subset of this are food webs: recommended readings for the next week's talks of Jennifer Dunne & Neo Martinez are
Berlow et al. 2009 Simple predictions of interaction strengths in complex food webs
and
Dunne et al. 2008 Compilation and network analysis of Cambrian food webs
(i can send both if you're interested). This is the latest and probably best of their research agenda, so we could discuss them, and Sasha & me could talk about different approaches/remaining big unsolved questions of community ecology. It would be cool if it'd happen before the talks (this would enable better questions and discussion during the talks), but looking at our schedule, there's not much chance for this...

About other ecol.modeling:
I guess I could give a general introduction to it, which will probably be biased towards the stuff i actually know something about- ODE-based modeling, networks and probably adaptive dynamics. For the sake of complexity, I can tell you on what fields they use other types of models (e.g. individual-based or spatial distribution models), but i don't know too much about the details of these.

*[[Caroline Farrior]] I agree an overview of the whole field is difficult. I would be willing to take part in this discussion and talk about the kinds of work I do with forest models and models I have seen done in many other areas of Ecology. There a lot of different kinds of models in Ecology which all have their own goals and utilities in combination with data and ideas. I would be very happy to listen to and perhaps contribute to such a discussion.

*[[Roozbeh Daneshvar]] I'd like to join. I do not have much background in this area and any amount of review before the relevant classes is highly appreciated.

*[[Marek Kwiatkowski]] I would be interested in learning about this too.

*[[Andrew Noble]] This all sounds great. I'd be happy to lead a brief overview of stochastic process models in community ecology (focusing on Hubbell's neutral theory of biodiversity). How about next Wednesday at 7pm? I'd like to attend the MaxEnt workshop Monday evening, and there are conflicts with Hubler's nonlinear workshops on Tuesday and Thursday.

*[[Jacopo Tagliabue]] I'd like to join, since it sounds very interesting. I do not have much background, so some introductory remarks will be appreciated!

*[[Lucas Lacasa]] I'm in

*[[Steven Lade]] Yep sounds great -- for scheduling just be aware there's also the 'music on the hill' Wed 6-8pm.

*[[Barbara Bauer]] Yeah, you have to choose between music and science here, we have such a tight schedule :( So, the final plan looks like: i will give an outline of math. modeling in ecology in 10 minutes, and after that every ecologist who wants and is there (including me) talks about his-her 'specialty' in cca 5 minutes. Speakers please: forget the details, concentrate on what we already know on your field-what we learned from using your method, and where are the major questions. thx for every contribution!

== [[(Evolutionary) Game Theory]] ==

[[Allison Shaw]]: Since there are a couple requests for this I'd be happy to organize a chat about game theory and evolutionary game theory. I can give an intro to some basic ideas in evolutionary game theory, namely the hawk-dove game, prisoner's dilemma, pure vs mixed vs conditional strategies, Nash equilibrium, evolutionarily stable strategies, and neighbor-invading and convergent stable strategies. Presumably there are people out there with more expertise in general game theory, who could join in...

In terms of a time, since the evenings look pretty crowded, how about at 4:45 after the last lectures, either Tues or Thurs this week, or some day next week?

Post if you're interested.

I'm interested. [[Marek Kwiatkowski]]

[[Lucas Lacasa]] I'm interested 2

Thanks! Interested as well. [[Mauricio Gonzalez-Forero]]

[[Mareen Hofmann]] I'd like to join as well! Regarding a time: I'd suggest Thursday after the last lecture.

[[Chang Yu]]: Thank u guys. I'm in!

[[Allison Shaw]]: Great, let's do this Thursday after the last lecture (4:45ish). I'm just planning to chat about the concepts I listed above, so anyone with more knowledge in game theory is more than welcome to join in.

[[Hirotoshi Yoshioka]]: I'm interested too.

[[Nathan Hodas]]: Please count me in!

== Tutorial requests! ==
Either a formal 'lecture' or a casual 'chat' is fine!
* Time series analysis (requested by Steve Lade)
**I second this request, particularly nonlinear TS and/or using TISEAN (Matt McMahon)
* Maximum entropy / Maximum entropy production (requested by Steve Lade)
* Mathematical modelling in ecology (requested by Steve Lade)
* Some physics stuff: statistical mechanics; mean field theory; self-organized criticality and phase transitions; Ising model and the like (requested by Mareen Hofmann and Roozbeh Daneshvar)
** [[Lucas Lacasa]] I can talk you about phase transitions, Ising model, SOC and general stat phys...
** [[Steven Lade]] I'd like to hear about this too; can you schedule a tutorial?
* Evolutionary game theory (requested by Mareen Hofmann, Roozbeh Daneshvar, [[Massimo Mastrangeli]])
* Ergodic theory (requested by Roozbeh Daneshvar)
* Information theory (requested by Roozbeh Daneshvar and Lucas Lacasa)
* Spectral graph theory (requested by Lucas Lacasa)
* Spin glass theory: Replica method (requested by Lucas Lacasa)

*If somebody can give a tutorial on Matlab and/or Mathematica, that would be nice. I'm also interested in the difference between the two programs (e.g., what each program is good at). Thanks in advance! Hiro/lakiaypayaska

*Fitting high dimensional data with functions--is there such thing as 4D maximum likelihood estimation (MLE)? Also how to tell if two 'blobs' of high dimensional data are statistically the same or different.[[Corinne Teeter]]
** The MLE is defined for any parameter vector, i.e. any number of dimensions. Finding it is a different story, and can be hard if the objective is not convex. The fully non-parametric ("assumption-free") way to do this is a two-sample test. For 1D data, you could use something like the [http://en.wikipedia.org/wiki/Kolmogorov-Smirnov_test#Two-sample_Kolmogorov.E2.80.93Smirnov_test Kolmogorov-Smirnov test] or [http://en.wikipedia.org/wiki/Cram%C3%A9r%E2%80%93von-Mises_criterion#Cram.C3.A9r-von-Mises_test_.28two_samples.29 Cramer-von Mises criterion]. Essentially you are stuffing the points from sample 2 into bins defined by the points from sample 1 (or vice-versa): the null hypothesis is that the bins are filled uniformly. You want to find a generalization of this into higher dimensions. I imagine that textbooks on spatial statistics would have this information.
UPDATE: I think you could use Voronoi cells as your bins, and this works in any number of dimensions! -[[Gustavo Lacerda]]

*Bayesian inference. Maximum likelihood estimation. (requested by Steve Lade)
** I'd be happy to give a "casual chat" on this. [[Gustavo Lacerda]]

* I'd like to learn about normal modes and the Lagrangian and/or Hamiltonian formulation of classical mechanics. -[[Gustavo Lacerda]] **I would be glad to discuss this with you. You might also be interested in how these deterministic systems give rise to randomness [[Nathan Hodas]]

* I'd like to learn some beyond-the-basic econometrics and data analysis, possibly focusing on how to build models in social sciences. Anyone? -[[Jacopo Tagliabue]] and [[Roozbeh Daneshvar]]

* I'd love to get introduced to game theory ([[Massimo Mastrangeli]] and [[Roozbeh Daneshvar]] and [[Nathan Hodas]])

* Anyone can tell me about graph grammars? ([[Massimo Mastrangeli]])

* Could someone give a quick overview of percolation theory? [[Allison Shaw]]

* Would anyone be willing to give a primer on artificial intelligence and machine learning? [[Nathan Hodas]]

CSSS 2009 Santa Fe-Tutorials

2009-06-16T05:11:44Z

Nhodas:

{{CSSS 2009 Santa Fe}}

Feel free to organize your own tutorials. 
Post tutorial schedules here.

'''For ease of scheduling, please make amendments to the schedule'''

Also, a [[tutorial outline page]] wouldn't be bad.

== Information theory ==
Discussions and resources are archived at the [[CSSS 2009 Santa Fe-Information theory tutorial]] page. Also see this page for recent/current follow up discussion...

== [[MATLAB / Mathematica]] ==
[[Steven Lade]]: Hiro, I will happily give a tutorial on the basics of these languages (but only the basics, I don't use any of the fancy bits). Anyone here know if the labs here run MATLAB/Mathematica?

If there is anyone else interested (leave your name) I'll arrange a time, otherwise I'll talk directly with you Hiro.

[[Karen Simpson]]: I would also like a tutorial on MATLAB. I know some things about it, but never really learned the basics so it takes me a long time to do things. The computer labs should be equipped with MATLAB. I also have a fairly updated version on my laptop.

[[Lucas Lacasa]]I'm also interested! Also know some basics but I usually program in Fortran so I'd love to learn it.

Steven, many thanks! I think that the computers in the lab have MATLAB. If not, we can access it through my school's server as long as the internet connection is stable. [[Hirotoshi Yoshioka]]/lakiaypayaska

[[Brian Hollar]] I'm also very interested! I've never used these languages, but have some basic knowledge of Java, NetLogo, and FORTRAN. I'd appreciate the help and would love to learn.

[[Milena Tsvetkova]] Count me in! Steve, what is a good time for you?

[[Wei Ni]]: Hi ppl. Could I join in? I know MATLAB and I would like to further polish my MATLAB skills. Meanwhile I want to learn Mathematica.

Thanks for coordinating the tutorial and for 'setting up' the facilities, Steve and Lucas.

[[Chang Yu]]:Hello,I'm here! Thank you guys for this tutorial.I'm a kid in MATLAB and very courious about it.I know M is a giant in Mathematics,Statistic Analysis,Images and Genetic Algorithm and really want to learn more about that.By the way, I have the installation of Matlab 7.0 and I'll take it in my flash drive.If you have updated version, that would be better.

[[Steven Lade]]: Too late for this week I think, and next week is pretty full, so let's aim for 7PM Monday June 22.

[[Elliot Martin]]: I would be happy to help out with the MATLAB tutorial if you want. I don't use any of the fancy packages either though.

[[Marek Kwiatkowski]]: Excellent tutorial idea, I'll be there.

[[Margreth Keiler]]: I would like to join. I've never used these languages but I would like to get an idea.

[[Liliana Salvador]]: Good idea. I am in! I have some knowledge of matlab (but never used very fancy packages). I would like to learn Mathematica.

== [[Statistical physics: applications to complex systems]] ==
[[Lucas Lacasa]]:Statistical physics is a rather huge field, so I'm thinking on building a tutorial that focus on some specific topics related to complexity science in a chat-like level, namely:

- Fundamentals of statistical mechanics: ensembles, partition function and associated thermodynamic quantities (free energy, entropy) and some other basic stuff.

- Critical phenomena: Phase transitions in physical, social, and algorithmic systems. Self-organized criticality as the counterpart of a critical phase transition. Relation between phase transitions and local bifurcations of dynamical systems.

- Monte Carlo simulations, ergodic theorem

- Specific example gathering all of the above: Ising model

- Other related topics that you may like to listen to

Maybe we could schedule it for next week, something like next wednesday (17) at 7pm? (provided that no PRIORITY things such as basketball or soccer games are scheduled). If anyone else is interested please leave your name. Depending on the 'audience' we can fix one place or another...

*[[Steven Lade]] Fantastic, I'm in. But there is the 'Music on the Hill' 6-8pm Wednesdays. Is 8pm getting too late for 'work'? Or I miss an hour of the music. (Tuesday and thursdays there's the nonlinear dynamics labs)

*[[Lucas Lacasa]] You're right. What about friday 19 after lunch?

*[[Roozbeh Daneshvar]] I'd like to join this one.

*[[Corinne Teeter]] I'd also like to come.

*[[Barbara Bauer]] I'm interested too.

*[[Hirotoshi Yoshioka]]: I'd like to come.

*[[Gustavo Lacerda]]: I'm interested.

*[[Allison Shaw]]: I'd like to join as well.

*[[watson]]: maybe you guys should rope

*[[Andrew Berdahl]] into this as well. sounds like he could have something to add...

*[[Elliot Martin]]: I could talk a bit about self organized criticality, and a bit about some mean field theory if you want some company

*[[Jacopo Tagliabue]]: I'm interested, especially if tutorial includes a for-dummies part.

*[[Massimo Mastrangeli]]: I will join you!

*[[Lara Danilova-Burdess]]: Excellent. I'd like to come.

*[[Mareen Hofmann]]: Super! I'm in as well.

*[[Margreth Keiler]]: I'm interested, and join the for-dummies part ;-)

*[[Liliana Salvador]]: I am in!

*[[Varsha Kulkarni]]: I'd like to come!

== [[Mathematical modeling in ecology ]] ==
If we want to do this before the talks next week, Monday evening?

*[[Barbara Bauer]] whoah, I wish i had an overview on the whole field...
But a subset of this are food webs: recommended readings for the next week's talks of Jennifer Dunne & Neo Martinez are
Berlow et al. 2009 Simple predictions of interaction strengths in complex food webs
and
Dunne et al. 2008 Compilation and network analysis of Cambrian food webs
(i can send both if you're interested). This is the latest and probably best of their research agenda, so we could discuss them, and Sasha & me could talk about different approaches/remaining big unsolved questions of community ecology. It would be cool if it'd happen before the talks (this would enable better questions and discussion during the talks), but looking at our schedule, there's not much chance for this...

About other ecol.modeling:
I guess I could give a general introduction to it, which will probably be biased towards the stuff i actually know something about- ODE-based modeling, networks and probably adaptive dynamics. For the sake of complexity, I can tell you on what fields they use other types of models (e.g. individual-based or spatial distribution models), but i don't know too much about the details of these.

*[[Caroline Farrior]] I agree an overview of the whole field is difficult. I would be willing to take part in this discussion and talk about the kinds of work I do with forest models and models I have seen done in many other areas of Ecology. There a lot of different kinds of models in Ecology which all have their own goals and utilities in combination with data and ideas. I would be very happy to listen to and perhaps contribute to such a discussion.

*[[Roozbeh Daneshvar]] I'd like to join. I do not have much background in this area and any amount of review before the relevant classes is highly appreciated.

*[[Marek Kwiatkowski]] I would be interested in learning about this too.

*[[Andrew Noble]] This all sounds great. I'd be happy to lead a brief overview of stochastic process models in community ecology (focusing on Hubbell's neutral theory of biodiversity). How about next Wednesday at 7pm? I'd like to attend the MaxEnt workshop Monday evening, and there are conflicts with Hubler's nonlinear workshops on Tuesday and Thursday.

*[[Jacopo Tagliabue]] I'd like to join, since it sounds very interesting. I do not have much background, so some introductory remarks will be appreciated!

*[[Lucas Lacasa]] I'm in

*[[Steven Lade]] Yep sounds great -- for scheduling just be aware there's also the 'music on the hill' Wed 6-8pm.

*[[Barbara Bauer]] Yeah, you have to choose between music and science here, we have such a tight schedule :( So, the final plan looks like: i will give an outline of math. modeling in ecology in 10 minutes, and after that every ecologist who wants and is there (including me) talks about his-her 'specialty' in cca 5 minutes. Speakers please: forget the details, concentrate on what we already know on your field-what we learned from using your method, and where are the major questions. thx for every contribution!

== [[(Evolutionary) Game Theory]] ==

[[Allison Shaw]]: Since there are a couple requests for this I'd be happy to organize a chat about game theory and evolutionary game theory. I can give an intro to some basic ideas in evolutionary game theory, namely the hawk-dove game, prisoner's dilemma, pure vs mixed vs conditional strategies, Nash equilibrium, evolutionarily stable strategies, and neighbor-invading and convergent stable strategies. Presumably there are people out there with more expertise in general game theory, who could join in...

In terms of a time, since the evenings look pretty crowded, how about at 4:45 after the last lectures, either Tues or Thurs this week, or some day next week?

Post if you're interested.

I'm interested. [[Marek Kwiatkowski]]

[[Lucas Lacasa]] I'm interested 2

Thanks! Interested as well. [[Mauricio Gonzalez-Forero]]

[[Mareen Hofmann]] I'd like to join as well! Regarding a time: I'd suggest Thursday after the last lecture.

[[Chang Yu]]: Thank u guys. I'm in!

[[Allison Shaw]]: Great, let's do this Thursday after the last lecture (4:45ish). I'm just planning to chat about the concepts I listed above, so anyone with more knowledge in game theory is more than welcome to join in.

[[Hirotoshi Yoshioka]]: I'm interested too.

== Tutorial requests! ==
Either a formal 'lecture' or a casual 'chat' is fine!
* Time series analysis (requested by Steve Lade)
**I second this request, particularly nonlinear TS and/or using TISEAN (Matt McMahon)
* Maximum entropy / Maximum entropy production (requested by Steve Lade)
* Mathematical modelling in ecology (requested by Steve Lade)
* Some physics stuff: statistical mechanics; mean field theory; self-organized criticality and phase transitions; Ising model and the like (requested by Mareen Hofmann and Roozbeh Daneshvar)
** [[Lucas Lacasa]] I can talk you about phase transitions, Ising model, SOC and general stat phys...
** [[Steven Lade]] I'd like to hear about this too; can you schedule a tutorial?
* Evolutionary game theory (requested by Mareen Hofmann, Roozbeh Daneshvar, [[Massimo Mastrangeli]])
* Ergodic theory (requested by Roozbeh Daneshvar)
* Information theory (requested by Roozbeh Daneshvar and Lucas Lacasa)
* Spectral graph theory (requested by Lucas Lacasa)
* Spin glass theory: Replica method (requested by Lucas Lacasa)

*If somebody can give a tutorial on Matlab and/or Mathematica, that would be nice. I'm also interested in the difference between the two programs (e.g., what each program is good at). Thanks in advance! Hiro/lakiaypayaska

*Fitting high dimensional data with functions--is there such thing as 4D maximum likelihood estimation (MLE)? Also how to tell if two 'blobs' of high dimensional data are statistically the same or different.[[Corinne Teeter]]
** The MLE is defined for any parameter vector, i.e. any number of dimensions. Finding it is a different story, and can be hard if the objective is not convex. The fully non-parametric ("assumption-free") way to do this is a two-sample test. For 1D data, you could use something like the [http://en.wikipedia.org/wiki/Kolmogorov-Smirnov_test#Two-sample_Kolmogorov.E2.80.93Smirnov_test Kolmogorov-Smirnov test] or [http://en.wikipedia.org/wiki/Cram%C3%A9r%E2%80%93von-Mises_criterion#Cram.C3.A9r-von-Mises_test_.28two_samples.29 Cramer-von Mises criterion]. Essentially you are stuffing the points from sample 2 into bins defined by the points from sample 1 (or vice-versa): the null hypothesis is that the bins are filled uniformly. You want to find a generalization of this into higher dimensions. I imagine that textbooks on spatial statistics would have this information.
UPDATE: I think you could use Voronoi cells as your bins, and this works in any number of dimensions! -[[Gustavo Lacerda]]

*Bayesian inference. Maximum likelihood estimation. (requested by Steve Lade)
** I'd be happy to give a "casual chat" on this. [[Gustavo Lacerda]]

* I'd like to learn about normal modes and the Lagrangian and/or Hamiltonian formulation of classical mechanics. -[[Gustavo Lacerda]] **I would be glad to discuss this with you. You might also be interested in how these deterministic systems give rise to randomness [[Nathan Hodas]]

* I'd like to learn some beyond-the-basic econometrics and data analysis, possibly focusing on how to build models in social sciences. Anyone? -[[Jacopo Tagliabue]] and [[Roozbeh Daneshvar]]

* I'd love to get introduced to game theory ([[Massimo Mastrangeli]] and [[Roozbeh Daneshvar]] and [[Nathan Hodas]])

* Anyone can tell me about graph grammars? ([[Massimo Mastrangeli]])

* Could someone give a quick overview of percolation theory? [[Allison Shaw]]

* Would anyone be willing to give a primer on artificial intelligence and machine learning? [[Nathan Hodas]]

CSSS 2009 Santa Fe-Participants

2009-06-15T05:00:17Z

Nhodas:

{{CSSS 2009 Santa Fe}}

[[Alexander Mikheyev]] 

[[Alhaji Cherif]] 
Arizona State University

[[Allison Shaw]] 
Princeton University

[[Almut Brunner]] 
University of Bonn

[[Andrew Berdahl]] 
University of Calgary

[[Andrew Noble]] 

[[Angela Onslow]] 

[[Barbara Bauer]] 

[[Brendan Colloran]] 
San Francisco State University

[[Brian Hollar]] 
George Mason University

[[Caroline Farrior]] 
Princeton University

[[Casey Helgeson]] 
University of Wisconsin—Madison

[[Chang Yu]] 
Harbin Institute of Technology

[[Corinne Teeter]] 

[[Daniel Wuellner]] 
UC Davis

[[Damian Winters]] 

[[Dave Brooks]] 
MITRE

[[David Jones]] 
University of Durham

[[Elliot Martin]] 
University of Calgary

[[Eric Kasper]] 
University of Minnesota

[[Erin Taylor]] 
University of Florida

[[Guimei Zhu]] 

[[Gustavo Lacerda]] 

[[Hamid Benbrahim]] 
Fidelity Investments

[[Hirotoshi Yoshioka]] 
University of Texas at Austin

[[Jacopo Tagliabue]] 

[[Jennifer Terpstra]] 

[[Jeremy Barofsky]] 

[[John Paul Gonzales]] 
SFI Staff

[[Joslyn Barnhart]] 

[[Karen Simpson]] 

[[Kathrine Behrman]] 

[[Lara Danilova-Burdess]] 

[[Liliana Salvador]] 

[[Lucas Lacasa]] 
Universidad Politécnica de Madrid

[[Mahyar Malekpour]] 
NASA

[[Mareen Hofmann]] 

[[Marek Kwiatkowski]] 

[[Margreth Keiler]] 
University of Vienna

[[Maria Consiglio]] 
NASA

[[Martin Schmidt]] 
University of Maryland, College Park

[[Massimiliano Spaziani Brunella]] 

[[Massimo Mastrangeli]] 

[[Matt McMahon]] 
MITRE

[[Mauricio Gonzalez-Forero]] 

[[Micah Hawkins]] 

[[Michael Richey]] 
Boeing

[[Michael Schultz]] 

[[Milena Tsvetkova]] 
Utrecht University

[[Murad Mithani]] 

[[Nathan Hodas]] 
Caltech

[[Philips Akinwole]] 

[[Prasanta Bose]] 
Lockheed Martin

[[Randy Haas]] 

[[Rhonda Hoenigman]] 

[[Roozbeh Daneshvar]] 
Texas A&M University

[[Rosemary Braun]] 
National Institutes of Health

[[Sean Brocklebank]] 
University of Edinburgh

[[Sergey Melnick]] 
University of Limerick

[[Steven Lade]] 
The Australian National University

[[Trevor Johnston]] 

[[Varsha Kulkarni]] 

[[Watson]] 

[[Wei Ni]] 

[[Wendy Ham]] 
The Wharton School, University of Pennsylvania

Nathan Hodas

2009-06-13T17:08:25Z

Nhodas:

Talk:Steven Lade

2009-06-13T16:23:20Z

Nhodas: New page: Hi Steve, for your nanomachine evolution, a major problem currently is stiction. This prevents most nanomachines from functioning properly. One recently proposed idea is using a repulsiv...

Hi Steve, for your nanomachine evolution, a major problem currently is stiction. This prevents most nanomachines from functioning properly. One recently proposed idea is using a repulsive casimir force to repel parts that shouldn't be touching. This force is very sensitive to underlying material properties. It would be interesting to toss this in the evolutionary mix. [[Nathan Hodas]]

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-13T16:19:33Z

Nhodas:

{{CSSS 2009 Santa Fe}}
==Brainstorming==
===Disease ecology of media hype===
How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 

You could look at the dynamics from agent-based (ABM) perspective. There is a recent paper by Epstein and colleague that focuses on the impact of fear on disease from agent-based perspective, but does not capture this dynamics. However, my collaborator and I are currently writing a paper on the same problem you just outline from mathematical epidemiological perspective. Our results show some interesting dynamics. I think its extension in ABM might provide richer dynamics.
Another relevant paper: S. Funk, E. Gilad, C. Watkins and V.A.A Jansen (2009) the spread of awareness and its impact on epidemic outbreaks. PNAS early edition
[[Alhaji Cherif]]

===Housing prices.===
[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 

===Movie Turnouts===
Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

===Climate network model.===
''Requires someone with climatology knowledge.'' Lenton et al. recently published a [http://www.pnas.org/content/105/6/1786 paper] listing 'policy-relevant' 'tipping elements' in the Earth's climate system and the temperature tipping points required to initiate them. (Basically, the tipping elements are components of the climate system where a bifurcation leading to a different stable state can be induced. The tipping point is the temperature at the bifurcation.) Surely, many of these tipping elements would have feedback effects on other tipping elements or the climate system as a whole. I would like to make a network model of these tipping elements and look at the tipping (or other) dynamics of the whole system. But Lenton et al. don't discuss these feedbacks much in their model, so we need some expert knowledge. [[Steven Lade]]

===Synchronised magma oscillations===
''Requires someone with geological knowledge'' In a recent [http://www.springerlink.com/content/n76781712g2q3578/?p=ec0c1ffe588f473a8dbe9637a3822ebf&pi=2 paper], which was also [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B83WY-4WBRC9H-G&_user=554534&_coverDate=05%2F20%2F2009&_alid=931681330&_rdoc=1&_fmt=high&_orig=search&_cdi=33799&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=5dc46c822607723e06f9b72fb16d1463 reported] by New Scientist, Mjelde and Faleide report on seismological measurements that allowed them to infer past rates of magma flow in the plume generally though to rise beneath Iceland. When the plume is strong it thickens the Earth's crust at this point. They found the crust thickened approximately every 15 million years, and inferred that the magma plume must also have pulsed with this period. These pulsations have also been observed in the crust under Hawaii, with almost exactly the same period! Mjelde and Faleide hypothesise that there must be some giant heating oscillation in the Earth's core which drives these two oscillations at very different parts of the Earth. But other geologists are skeptical because of the huge energy required and lack of other evidence of such oscillations. But all this reminds me of the synchronisation phenomenon, where coupled oscillators, even if only weakly coupled, tend to synchronise. So the oscillations under Hawaii and Iceland may be generated independently, but have some weak coupling that has led them to synchronise. We can make coupled oscillator models, that's easy, but someone to provide more context on possible forms of coupling and their parameterisation is more what we need. They only observe about three periods of this oscillation and the data is quite imprecise so we can't do much direct data analysis, unfortunately. [[Steven Lade]]

===Implementing Synchronization using NetLogo===
Since I just learned about NetLogo, I look forward to the tutorial sessions and would like to implement a synchronization scheme of a group of entities. If I find out how the fireflies synchronize themselves, then that would be an option. Of course, I'll be surprised if this has not been done before in NetLogo. I'll welcome any help and suggestions.[[Mahyar Malekpour]]

===The Global Spread of Cricket===
No I'm not actually intending to study this particular topic. But there is one interesting article published in 2005 (Kaufman and Patterson, American Sociological Review) that examined why cricket continues to be popular in many British-influenced societies while it is not in the U.S. and Canada. This is interesting given the fact that cricket was very popular in the two countries and that the first official international cricket match took place between the two countries in the mid-19th century. So, not only how cultures, ideas, technologies, etc. diffuse across nations, populations, and so on, but also mechanisms that influence the retention after the initial adoption merit serious attention I think. One possible topic include is modern contraceptive use in developing countries. I guess modeling such mechanisms would require taking into account the models presented by Nathan Collins and Peter Dodds, in addition to signed networks (Doreian). One difficulty of modeling this kind of mechanism is that both structural and individual factors should be considered [[Hirotoshi Yoshioka]].

===Contagion in Networks===
[[Peter Dodds]] discussed contagion in a simplified network in which all the nodes have certain amount of threshold for changing. I thought that if the thresholds are various, that can lead to new behaviors in group level. For instance, people in different cities might have different resistances against inputs. Hence, we might see that an epidemic issue spreads in one city but not in the other. Consider the cities as nodes in a higher level network. This means that we might see the same patterns in this higher level. Different nodes (cities) react differently to external inputs. This also seems to be a more realistic model of the real world. Any comments, suggestions or discussions, even in the order of minutes are appreciated!
[[Roozbeh Daneshvar]]

===Linking topology to dynamic response in small networks===
Imagine a small (3-7 nodes) network where every node represents a protein species, and every (directed) edge the activation relation between the proteins (i.e. A ---> B means that the protein A can react with B and activate it). Furthermore,
assume that there are two numbers associated with every node: the total number of protein molecules of the given type and the fraction of the active forms. Finally, let two nodes, R and E, be special and call them the Receptor and the Effector. What you have is a crude model of intracellular signalling.

This [http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=147 paper] considers such models and exhaustively classifies all the possible topologies (i.e. wirings) with respect to the activation pattern of the Effector in response to a standardized signal sent by the Receptor. The goal of our project would be to do the same experiment using different tools, and potentially obtain different results. The main difference would be to use stochastic (rather than deterministic) dynamics to determine the response. As the signalling systems operate with relatively low numbers of molecules, stochastic effects may be important. If we do this and have time left, we can try pushing it further and consider the issues of robustness and evolvability of these networks.

To put a nasty spin on the project, I propose that we use an obscure computational technique called [http://en.wikipedia.org/wiki/Model_checking model checking] to get the response profile of a network; partly just because we can, but partly also because it nicely deals away with the need of explicitely simulating and averaging of stochastic models.

Now, a couple of final remarks:
* Don't think of it as a network project. All networks involved will be rather trivial.
* The project group should include a biologist (to do sanity checks) and somebody familiar with parallel computing.
* Model checking is (very) expensive computationally, we will probably need a cluster.
* I have all the original results from the paper mentioned.
* The tool to use would probably be [http://www.prismmodelchecker.org/ PRISM].
[[Marek Kwiatkowski]]

: Marek, this dovetails nicely with my interests & I'd like to talk more about it with you. I have experience with -- and access to! -- a parallel cluster. No experience with prism, however. [[Rosemary Braun]]

===Pattern Generation in Dynamic Networks: Elucidating Structure-to-Behavior Relationships===
Many sorts of networks produce patterns when dynamics are active on them. The brain is a great example. In fact, the patterns generated in your head are not only interesting and perhaps beautiful, but crucial to your success in surviving and thriving in the world. Gene or protein networks are another example. Change a few genes around and suddenly your stuck with a nasty disease.

One question we can ask is: how do the patterns of behavior (or "function" if you want to presume as much) change when we change the structural connections in the dynamic network from which they emerge? Alternatively, for a given type of behavior (set of similar patterns), is there a class of networks which all exhibit this behavior? What is common between all of those networks? What is the underlying mechanistic explanation for how they all behave this way?

Some potential topics:
* Genetics - what patterns of proteins emerge depending on what genes are where on a genome? (maybe other questions ... I'm not a geneticist!)
* Spiking neural networks - I have a lot of experience with this.
* Kauffman-like Boolean networks
* Population biology / food webs?
* Economics?

We might even think of embedding this in some physical space. Perhaps neural nets drive the 'muscle' movements of creatures (a la the [http://www.karlsims.com/evolved-virtual-creatures.html Karl Sims 'Creatures'] video we saw in Olaf Sporn's lecture) or the motors of [http://people.cs.uchicago.edu/~wiseman/vehicles/test-run.html vehicles].

I have experience in Python, Java, Matlab and a few other languages and am open to working with whatever (NetLogo?). I also have experience with Information Theory, which could come in handy in digesting and analyzing the patterns.

Clearly this project could go multiple directions. Feel free to add ideas/comments here...

[[watson]]

=== All sorts of (mostly US-centric) data===
For fun, brainstorming, and sanity-checking:
[http://www.data.gov/ data.gov] has tons of data collected by the US Gov't.

===Problem solving and mating - are they similar?===
I was intrigued by Tom's model of mating and began to wonder whether we can think of problem solving in a similar way. If we were to model problem solving, how would we do it? I'd like to think that problems and solutions are components that combine to generate an emergent property. (After a problem meets a solution--or a solution meets a problem--something new is allowed to emerge. While one instance of problem solving does not exactly create a complex system, many instances may.) That said, there are several questions/considerations to think about before/while we create a proper model of problem solving:

* What is the difference between problems and solutions anyway?
* What makes certain kinds of problems and solutions "hang out" in a cluster or neighboring clusters? Is this primarily due to path-dependence?
* When there is a difficult problem (tentatively defined as a problem for which there is no nearby solutions), how can we tell which clusters have the greatest probability of containing the solution(s)? (Can some of the network stuff we learned be of help here?)
* It is of course important to remember that a problem can have many solutions, and a solution can solve many problems, but that they may have different degrees of affinity (just like a ligand-receptor interaction in molecular biology). Also, occasionally a problem needs a combination of several solutions ("AND" as opposed to "OR").

I would love to hear your thoughts and comments, and I'm hoping that someone may actually share some of my interests in figuring out the answers to the questions above! [[Wendy Ham]]

===Modularity in complex systems - why is it there and what does it do?===
Evolving systems often switch from being highly modular to highly integrated, and vice versa. Why is this so and how does it happen? [[Wendy Ham]] and [[Roozbeh Daneshvar]].

* [[Roozbeh Daneshvar]]: Today in a slide of [[Olaf Sporns]] presentation, I noticed a graph showing the relation between order/disorder and complexity. When the system becomes too much ordered or too much disordered, in both cases complexity reduces. There is somewhere in between that we have the most amount of complexity. I was thinking that the emergence of modules are also a movement towards orderliness. But, complex systems do not go beyond a limit and still keep some non-modularity. So, Wendy, we have contrasting views on modularity. But maybe we will meet somewhere in between, where we have the most amount of complexity!
** '''Question''': Why modularity changed in human societies? Did the behavior of complexity change?

===Evolving nanomachines===
Take the evolving motors animation we saw at the end of Olaf Sporn's talk, but instead put nanoscale physics, i.e. overdamped motion with Brownian noise, into the simulation. Perhaps put some basic chemistry in too. Evolve possible designs for nanomotors! What we get may include existing biological molecular motors. Or even more crazy idea: put in the physics of quantum mechanics. [[Steven Lade]] but with credits to Lilliana!

===Credit Market Simulation===
Money is loaned every day on the bond and money markets between banks, corporations, and individuals. It usually works very efficiently, but, ultimately, it is driven by humans. An agent simulation could provide us with insight into what behavior patterns give rise to the booms and busts that we have been experiencing. My guess is that it boils down to how individuals estimate risk and future reward. Nathan Collins suggested a learning model for how people get habituated to reward, expecting more and more for satisfaction. However, what happens to our estimates of risk in the face of increasing rewards? When the two are out of sync, we would likely see interesting dynamics. We've come up with a few ideas for how to implement this. [[Nathan Hodas]]

CSSS 2009 Santa Fe-Tutorials

2009-06-12T05:58:36Z

Nhodas:

{{CSSS 2009 Santa Fe}}

Feel free to organize your own tutorials. 
Post tutorial schedules here.

'''For ease of scheduling, please make amendments to the schedule'''

Also, a [[tutorial outline page]] wouldn't be bad.

== [[Information theory leading on to Maximum Entropy (and maybe maximum entropy production)]] ==
[[Steven Lade]]: Well, nobody responded to my request so perhaps I'll do it myself :) I learnt a little at another summer school, so maybe I'm the best qualified, I was just hoping I wasn't. :)

Maximum entropy comes out of information theory (which I'm not expert in, so if there's someone knowledgeable to help me there, that would be great). It states simply that the most likely state of a system is the one which maximises its (information-theoretic) entropy. The entropy of statistical mechanics comes naturally from it: indeed, stat mech can be viewed as a subset of information theory from this perspective. But you can apply it to any sort of system, not just physics. It's the closest thing to a 'theory of complex systems' that I've seen!

Tentative time: 7PM, Monday June 15. Add your name if interested.

* I am interested and will happily wear the skeptic hat: essentially if the entropy is sensitive to your parametrization, it loses much of its meaning. I think this is why some people (such as Amari) like to do "coordinate-free statistics". -Gustavo Lacerda

* I'm no expert, but I do have information theory basics under my belt... and more importantly, I happen to have some introductory info. theo. latex slides for a talk I recently gave. So I can offer help with the background, and then sit back & listen to the higher level, post-Shannon applications :) -[[Rosemary Braun]]
** On edit: I just noticed Lucas & Roozbeh requested an info theo tutorial -- shall we just roll it all together? -[[Rosemary Braun]]

*[[Lucas Lacasa]] Great, I'm in. Steve, with the Max-ent stuff you mean Jaynes? I've seen Jaynes formalism two or three times but never saw the difference with a merely Lagrange multiplier optimization, so great to hear about that. Rosemary, I would also be glad to learn some information theory stuff. I know the ultra-basics (Shannon entropy, mutual information, and not much more...).

[[Angela Onslow]]: I'd like to come along to this

[[Roozbeh Daneshvar]]: FYI, We have ABM tutorial on Monday, June 15th starting at 04:30 PM. I assume that it is finished by 07:00 PM.

[[Kate Behrman]]: I also do not claim to be an expert, but have gone over a lot of the theory that is commonly used in biology. I would like to know what physicists think. Count me in!

[[Steven Lade]]: Rosemary, would be great if you could start with information theory. Lucas, yes I do mean Jaynes. Lagrange multiplier optimisation is really all that happens, so maybe it is all the same -- I confess I don't know much stat mech either! This session is going to be very much everyone contributing I think. And sounds like you may know more than me, Kate.

[[Allison Shaw]]: I'd like to come -- I've had the basics of information theory, but could definitely use a refresher and I'd love to learn the more advanced stuff.

[[watson]]: Sounds great. I know nothing about entropy maximization (other than the basic statement of the [http://en.wikipedia.org/wiki/Second_law_of_thermodynamics 2nd law of thermodynamics]). But I would be glad to help out Rosemary with explaining some info theory stuff... Rosemary, perhaps we can chat before Monday. I'm thinking: entropy, conditional entropy, mutual information and possibly some topics beyond.

[[Elliot Martin]]: There are some differences between maximum entropy in thermodynamics, and what Jaynes does. I would be happy to help out if you want, but I just know a little bit about this stuff.

[[Massimo Mastrangeli]]: I am definitely not an expert, anyway I had several bits of information theory throughout time, so I'd like to wrap it up somehow. I'll join you.

Lara Danilova-Burdess Count me in

== [[MATLAB / Mathematica]] ==
[[Steven Lade]]: Hiro, I will happily give a tutorial on the basics of these languages (but only the basics, I don't use any of the fancy bits). Anyone here know if the labs here run MATLAB/Mathematica?

If there is anyone else interested (leave your name) I'll arrange a time, otherwise I'll talk directly with you Hiro.

[[Karen Simpson]]: I would also like a tutorial on MATLAB. I know some things about it, but never really learned the basics so it takes me a long time to do things. The computer labs should be equipped with MATLAB. I also have a fairly updated version on my laptop.

[[Lucas Lacasa]]I'm also interested! Also know some basics but I usually program in Fortran so I'd love to learn it.

Steven, many thanks! I think that the computers in the lab have MATLAB. If not, we can access it through my school's server as long as the internet connection is stable. [[Hirotoshi Yoshioka]]/lakiaypayaska

[[Brian Hollar]] I'm also very interested! I've never used these languages, but have some basic knowledge of Java, NetLogo, and FORTRAN. I'd appreciate the help and would love to learn.

[[Milena Tsvetkova]] Count me in! Steve, what is a good time for you?

[[Wei Ni]]: Hi ppl. Could I join in? I know MATLAB and I would like to further polish my MATLAB skills. Meanwhile I want to learn Mathematica.

Thanks for coordinating the tutorial and for 'setting up' the facilities, Steve and Lucas.

[[Chang Yu]]:Hello,I'm here! Thank you guys for this tutorial.I'm a kid in MATLAB and very courious about it.I know M is a giant in Mathematics,Statistic Analysis,Images and Genetic Algorithm and really want to learn more about that.By the way, I have the installation of Matlab 7.0 and I'll take it in my flash drive.If you have updated version, that would be better.

[[Steven Lade]]: Too late for this week I think, and next week is pretty full, so let's aim for 7PM Monday June 22.

[[Elliot Martin]]: I would be happy to help out with the MATLAB tutorial if you want. I don't use any of the fancy packages either though.

[[Marek Kwiatkowski]]: Excellent tutorial idea, I'll be there.

== [[Statistical physics: applications to complex systems]] ==
[[Lucas Lacasa]]:Statistical physics is a rather huge field, so I'm thinking on building a tutorial that focus on some specific topics related to complexity science in a chat-like level, namely:

- Fundamentals of statistical mechanics: ensembles, partition function and associated thermodynamic quantities (free energy, entropy) and some other basic stuff.

- Critical phenomena: Phase transitions in physical, social, and algorithmic systems. Self-organized criticality as the counterpart of a critical phase transition. Relation between phase transitions and local bifurcations of dynamical systems.

- Monte Carlo simulations, ergodic theorem

- Specific example gathering all of the above: Ising model

- Other related topics that you may like to listen to

Maybe we could schedule it for next week, something like next wednesday (17) at 7pm? (provided that no PRIORITY things such as basketball or soccer games are scheduled). If anyone else is interested please leave your name. Depending on the 'audience' we can fix one place or another...

*[[Steven Lade]] Fantastic, I'm in. But there is the 'Music on the Hill' 6-8pm Wednesdays. Is 8pm getting too late for 'work'? Or I miss an hour of the music. (Tuesday and thursdays there's the nonlinear dynamics labs)

*[[Lucas Lacasa]] You're right. What about friday 19 after lunch?

*[[Roozbeh Daneshvar]] I'd like to join this one.

*[[Corinne Teeter]] I'd also like to come.

*[[Barbara Bauer]] I'm interested too.

*[[Hirotoshi Yoshioka]]: I'd like to come.

*[[Gustavo Lacerda]]: I'm interested.

*[[Allison Shaw]]: I'd like to join as well.

*[[watson]]: maybe you guys should rope [[Andrew Berdahl]] into this as well. sounds like he could have something to add...

*[[Elliot Martin]]: I could talk a bit about self organized criticality, and a bit about some mean field theory if you want some company

*[[Jacopo Tagliabue]]: I'm interested, especially if tutorial includes a for-dummies part.

*[[Massimo Mastrangeli]]: I will join you!

Lara Danilova-Burdess Excellent. I'd like to come.

*[[Mareen Hofmann]]: Super! I'm in as well.

== [[Mathematical modeling in ecology]] ==
If we want to do this before the talks next week, Monday evening?

*[[Barbara Bauer]] whoah, I wish i had an overview on the whole field...
But a subset of this are food webs: recommended readings for the next week's talks of Jennifer Dunne & Neo Martinez are
Berlow et al. 2009 Simple predictions of interaction strengths in complex food webs
and
Dunne et al. 2008 Compilation and network analysis of Cambrian food webs
(i can send both if you're interested). This is the latest and probably best of their research agenda, so we could discuss them, and Sasha & me could talk about different approaches/remaining big unsolved questions of community ecology. It would be cool if it'd happen before the talks (this would enable better questions and discussion during the talks), but looking at our schedule, there's not much chance for this...

About other ecol.modeling:
I guess I could give a general introduction to it, which will probably be biased towards the stuff i actually know something about- ODE-based modeling, networks and probably adaptive dynamics. For the sake of complexity, I can tell you on what fields they use other types of models (e.g. individual-based or spatial distribution models), but i don't know too much about the details of these.

*[[Caroline Farrior]] I agree an overview of the whole field is difficult. I would be willing to take part in this discussion and talk about the kinds of work I do with forest models and models I have seen done in many other areas of Ecology. There a lot of different kinds of models in Ecology which all have their own goals and utilities in combination with data and ideas. I would be very happy to listen to and perhaps contribute to such a discussion.

*[[Roozbeh Daneshvar]] I'd like to join. I do not have much background in this area and any amount of review before the relevant classes is highly appreciated.

*[[Marek Kwiatkowski]] I would be interested in learning about this too.

== Tutorial requests! ==
Either a formal 'lecture' or a casual 'chat' is fine!
* Time series analysis (requested by Steve Lade)
* Maximum entropy / Maximum entropy production (requested by Steve Lade)
* Mathematical modelling in ecology (requested by Steve Lade)
* Some physics stuff: statistical mechanics; mean field theory; self-organized criticality and phase transitions; Ising model and the like (requested by Mareen Hofmann and Roozbeh Daneshvar)
** [[Lucas Lacasa]] I can talk you about phase transitions, Ising model, SOC and general stat phys...
** [[Steven Lade]] I'd like to hear about this too; can you schedule a tutorial?
* Evolutionary game theory (requested by Mareen Hofmann and Roozbeh Daneshvar)
* Ergodic theory (requested by Roozbeh Daneshvar)
* Information theory (requested by Roozbeh Daneshvar and Lucas Lacasa)
* Spectral graph theory (requested by Lucas Lacasa)
* Spin glass theory: Replica method (requested by Lucas Lacasa)

*If somebody can give a tutorial on Matlab and/or Mathematica, that would be nice. I'm also interested in the difference between the two programs (e.g., what each program is good at). Thanks in advance! Hiro/lakiaypayaska

*Fitting high dimensional data with functions--is there such thing as 4D maximum likelihood estimation (MLE)? Also how to tell if two 'blobs' of high dimensional data are statistically the same or different.[[Corinne Teeter]]
** The MLE is defined for any parameter vector, i.e. any number of dimensions. Finding it is a different story, and can be hard if the objective is not convex. The fully non-parametric ("assumption-free") way to do this is a two-sample test. For 1D data, you could use something like the [http://en.wikipedia.org/wiki/Kolmogorov-Smirnov_test#Two-sample_Kolmogorov.E2.80.93Smirnov_test Kolmogorov-Smirnov test] or [http://en.wikipedia.org/wiki/Cram%C3%A9r%E2%80%93von-Mises_criterion#Cram.C3.A9r-von-Mises_test_.28two_samples.29 Cramer-von Mises criterion]. Essentially you are stuffing the points from sample 2 into bins defined by the points from sample 1 (or vice-versa): the null hypothesis is that the bins are filled uniformly. You want to find a generalization of this into higher dimensions. I imagine that textbooks on spatial statistics would have this information.
UPDATE: I think you could use Voronoi cells as your bins, and this works in any number of dimensions! -[[Gustavo Lacerda]]

*Bayesian inference. Maximum likelihood estimation. (requested by Steve Lade)
** I'd be happy to give a "casual chat" on this. [[Gustavo Lacerda]]

* I'd like to learn about normal modes and the Lagrangian and/or Hamiltonian formulation of classical mechanics. -[[Gustavo Lacerda]] **I would be glad to discuss this with you. You might also be interested in how these deterministic systems give rise to randomness [[Nathan Hodas]]

* I'd like to learn some beyond-the-basic econometrics and data analysis, possibly focusing on how to build models in social sciences. Anyone? -[[Jacopo Tagliabue]] and [[Roozbeh Daneshvar]]

* I'd love to get introduced to game theory ([[Massimo Mastrangeli]] and [[Roozbeh Daneshvar]])

* Anyone can tell me about graph grammars? ([[Massimo Mastrangeli]])

Talk:Randy Haas

2009-06-11T18:34:07Z

Nhodas:

Hi Randy,

Here are the references I spoke about, from Tom Vanderbilt's Traffic:
Cesare Marchetti, "Anthropological Invariants in Travel Behavior," Technological Forecasting and Social Change, vol 47 (1994), pp 75-88
M Wachs et al, "The Changing Commute: A Case-study of the Jobs-Housing Relationship over Time," Urban Studies, vol. 30, no 10 (1993), pp 1711-29
--Nathan

Talk:Randy Haas

2009-06-11T18:33:47Z

Nhodas: New page: Hi Randy, Here are the references I spoke about, from Tom Vanderbilt's Traffic: Cesare Marchetti, "Anthropological Invariants in Travel Behavior," Technological Forecasting and Social Cha...

Alfred Hubler's Nonlinear Dynamics Lab

2009-06-11T01:01:36Z

Nhodas:

{{CSSS 2009 Santa Fe}}

Alfred will be hosting a lab where students get to play around with a few concepts from nonlinearity and complexity science.

Class size is limited to 12 students.

===Tuesday June 16, 7:00 p.m.===
1[[Erin Taylor]] 
2[[Roozbeh Daneshvar]] 
3[[Allison Shaw]] 
4[[Matt_McMahon]] 
5[[Nathan Hodas]] 
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===Wednesday June 17, 8:30 a.m.===
1 
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12 
===Thursday June 18 7:00 p.m.===
1 
2 
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12 

[[Roozbeh Daneshvar]] is absolutely in! (should I sign-up here?)
 ------- [[jp]] Yep, got you in

[[Erin Taylor]] will be there for Tuesday the 16th's lab

[[Allison Shaw]] will be there for lab on the 16th too

CSSS 2009 Santa Fe-After Hours

2009-06-10T02:29:11Z

Nhodas: /* Sports */

{{CSSS 2009 Santa Fe}}

== Hiking ==
I'm excited by the excellent hiking and backpacking available around Santa Fe! For now, if you're interested in some hiking put down your name, previous experience and equipment you will be bringing (for those interested in overnight hiking). Later, we can use this page to arrange specific plans -- although if you have some ideas or intentions already, please put them down. [[User:SteveLade|SteveLade]] 03:28, 25 May 2009 (UTC)

- [[Steven Lade]]. Experience: many multi-day hikes ("backpacks"?) in Australia. Equipment: probably 2-man tent and my sleeping bag. Won't fit my stove I think. I enjoy strenuous hikes but am no masochist.

- [[Daniel Wuellner]]. Experience: several backpacking trips (2-5 days) in Wisconsin and California. Equipment: 2-person tent, stove, water filter. I'll also have a car (seats 4). I like strenuous day hikes and easy-moderate overnight trips.

- [[Casey Helgeson]]. I did a three month hike on the Florida National Trail in 2000, but very little since then. I won't be equipped for overnight camping, but would love to do some day hikes.

- [[watson]]. I've done many backpacking trips over the last decade and a half or so in various parts of the country. I brought camping supplies including a 2-human tent and a stove. Observing beauty is my main objective when hiking, whether the trail is difficult or not. Strenuous is a good substitute sometimes.

=== Hiking Plans ===
* Tentative plan for Atalaya Mountain, Saturday. Trail starts at the parking lot here at St John's! Meet after breakfast at 9AM? Trail is 7 miles return, 2000ft elevation gain, I've seen no recommended times but I'd expect we'd return mid-afternoon. Good views back over Santa Fe. Trail easy at start but steep towards end. Should be a nice warm up before moving onto more difficult hikes. You'll need walking shoes, lunch, water (I suggest 3L), hat, sunscreen, rain jacket and a sweater just in case. Add your name if you're interested, but watch this space for confirmation (or alternative hikes -- did you buy that book Margreth?). Interested people: Steve Lade, Milena (POSSIBLY CHANGING TO SUNDAY TO MAKE ROOM FOR A BANDELIER TRIP ON SAT)
[[Mahyar Malekpour]]: If this hike is rescheduled for Sunday, I'm in.

*[http://www.nps.gov/band Bandelier National Monument] day trip *this* Saturday (13th)? I'm thinking a stop at the museum, hike the short trail to the cave sites, and then do a longer hike to a backcountry ruin (e.g., [http://www.nps.gov/band/planyourvisit/yapashi-pueblo.htm Yapeshi Pueblo]). Also, I have a friend who, for his dissertation, incorporated [http://en.wikipedia.org/wiki/Resilience_(ecology) ecological resilience] theory into a time-series analysis of the archaeological settlements there...I could get a summary of his research, which could make for interesting and potentially relevant discussion during our visit to the sites. It takes about 1.5 hours to drive there. I can comfortably take one other person in my truck, and uncomfortably two people. Interested peops: Randy Haas
**this was originally scheduled for the following weekend, but I've moved it up to the 13th by suggestion
**Things you'll need: a ride, walking shoes, ~3L water, food, a warm coat or sweater (it's good to have a mix of warm and cool-weather clothes because the weather can change rapidly here).
**There's a cafe at the visitors center where we can get lunch or dinner. I'll see if the diner here can provide us with to-go lunches. Otherwise, we can plan on making a grocery store stop on the way out.
**Things you may want to bring: a hat, sun screen, a rain coat, camera....
[[Mahyar Malekpour]]: Count me in. I have a car and room for three.
[[Steven Lade]]: I'd like to come, and hereby claim a car space!

==Sports==

===St. John's Facilities===
Please let John Paul know if you want to schedule court (basketball/volleyball) or field (soccer/football/rugby) games in advance. We need to get approval from conference services to make sure there are no *other* events going on.

===Soccer===

Mahyar would like to start up a soccer game, Tuesday 6/9, 7:00 on the St. John's soccer field. We'll bring the ball. You bring the game. [[Mahyar Malekpour]]

[http://www.santafe.edu/events/workshops/index.php/Roozbeh_Daneshvar Roozbeh]: I will play.

[http://www.santafe.edu/events/workshops/index.php/Marek_Kwiatkowski Marek]: So will I. If anyone has size 8 (Eur 42) or similar sport shoes to lend, I'd be very grateful.

[[Randy Haas|Randy Haas]]: I'm in.

===Basketball===

We just started doing some sort of static simulation of what it's usually called "basketball". We plan to play on a somewhat regular basis after the afternoon lecture: the more we are, the better it is. [[Jacopo Tagliabue]]

===Climbing/Bouldering===

I brought shoes and a harness. Don't know the area yet. [[watson]]

===Squash===

The gym has two squash courts, and I have extra squash racquets I am happy to loan out. Please add your name or contact me if you would like to partake. [[Nathan Hodas]]

==Music==

===Music on the Hill™ 2009===
St. John's College Athletic Field
Wednesday evenings, 6-8 p.m.

This is something going on every Wednesday, for those who are interested in a bit of relaxing high culture.[[John Paul]]

Yes. Saw a posted add for this. Free every week. This (first) week looks to be featuring Brazilian Jazz! [[watson]]

===Music in Santa Fe===

Haven't done the legwork yet to see whats brewing down in the city, but I'd love to go hear some acoustic sounds or rock out to some electronic beats (SF has a vibrant scene I'm told). [[watson]]

==Yoga==
There were some people interested in doing yoga. I am not terribly good, but would love to join a group. [[Alexander Mikheyev]]

[[watson]]: Also totally into this. Mornings slightly preferred. I've practiced some [http://en.wikipedia.org/wiki/Anusara_Yoga Anusara] and have a very simple/short routine I do most mornings which I'd be happy to share.

I am sure I will do some yoga, I have been looking at the websites of some of the local studios. My ankle is bothering me a bit, so I will probably wait a couple days and then go check one out. I was thinking of trying the bikram place first although I am not too picky. http://www.bikramyoga-santafe.com/schedule.html. Bikram is hot yoga for those who don't know. Looks to be $10 for drop in students, 1.5 hour classes, supposed to be for any level. Corinne.

[[Erin Taylor]]: I'm interested in the Bikram yoga - let me know when you plan to go. Also, I brought a great yoga DVD by Bryan Kest [http://www.amazon.com/Bryan-Kest-Power-Complete-Collection/dp/B0001WTWYC/ref=sr_1_1?ie=UTF8&s=dvd&qid=1244598801&sr=8-1] which is Ashtanga style. I often do this in the mornings, it takes about 1 hour. The TVs in the common rooms don't seem to work but I can set it up on my laptop or someone else's, mine will require a power outlet however. For anyone interested, any thoughts on a suitable location?

==Games==

I brought Set, Backgammon and Cards. I'm particularly interested in getting a game of Set going soon. Anywhere between 2 and 10 people can play. If you haven't played yet, its all about pattern matching. Its addictive and I'd be happy to indoctrinate you. [[watson]]

Also I love the group game [http://en.wikipedia.org/wiki/Mafia_(game) Mafia/Werewolf/Assassin] and would be down to instigate a game. Its fun to play with anywhere between 8 and 20 people. [[watson]]

==Trips Beyond Santa Fe==

=== Rafting ===
Some interest has been expressed in whitewater rafting trips.

[http://www.kokopelliraft.com| Kokopelli Rafting] offers morning, afternoon and all day (even two day camping) excursions.

[[watson]]: Interested. Particularly since someone said it had been raining a bunch.

=== Telluride Bluegrass Festival ===

Some of us are interested in going to [http://www.bluegrass.com/telluride/ Telluride Bluegrass Festival] in the weekend of 20, 21 June. It's a 6-hour drive, and we probably need a hotel reservation in order to park in Telluride at all. [[Gustavo Lacerda]]

If interested, add your name and car status: Gustavo (no car)

== After Santa Fe ==
At this stage, after the summer school I expect to have a week off before my next appointment at UC Davis. If you have some time off and are interested in some hiking or other outdoor activities around Santa Fe, or between SF and Davis, please let me know. [[User:SteveLade|SteveLade]] 03:28, 25 May 2009 (UTC)

== Finished and Archived ==

=== Pre-Program ===
Anyone coming early to Santa Fe? You can coordinate contacts and meeting here:

Yes. [http://www.santafe.edu/events/workshops/index.php/Sean_Brocklebank Sean] has arrived early. Text me on 202-713-4282 if you'll be around.

CSSS 2009 Santa Fe-Projects & Working Groups

2009-06-10T02:26:06Z

Nhodas:

{{CSSS 2009 Santa Fe}}
==Brainstorming==
* '''Disease ecology of media hype.''' How much and event gets covered in the news often appears to depends on how much it is already covered in the news. Often this distorts reality. For example, the number of searches for "swine flu" (a proxy for media hype), do not reflect the patterns of disease spread over the same period.
[[Image:Flu_trends.png|thumb|Google searches for "swine flu"|left]]
[[Image:Flu_cases.png |thumb|Actual number of swine flu cases over the same period|left]]
While the number of flu cases increased, the searches died off, as interest in the topic waned. It would be interesting to follow the origin, spread and extinction of media hype, maybe applying models commonly used to study the spread of disease. [[Alexander Mikheyev]] 
*[[Image:Phoenix.jpg|thumb|Change in Phoenix home prices. Source: NYT|left]]
'''Housing prices.''' The New York Times has a set of [http://www.nytimes.com/interactive/2007/08/25/business/20070826_HOUSING_GRAPHIC.html?scp=3&sq=home%20prices%20graphic&st=cse dramatic graphs] showing the rise and fall of home prices in select cities. Again these graphs reminded me a bit of those produced by [http://www.math.duke.edu/education/ccp/materials/postcalc/sir/sir2.html susceptible-infected-recovered] models of disease spread. Maybe there is something to it? Or maybe this phenomenon is already well understood by economists? [[Alexander Mikheyev]] 
*'''Movie Turnouts''' Which would be the more popular movie -- a combination of Steven Spielberg, Eddie Murphy and Gwyneth Paltrow, or Woody Allen, Dwayne 'the rock' Johnson, and Tom Cruise? Using the adaptation and turnout models presented by Nathan Collins, could we construct a prediction for gross movie receipts or even movie ratings? [[Nathan Hodas]]

Nathan Hodas

2009-06-08T05:01:16Z

Nhodas:

Nathan Hodas

2009-05-24T04:24:03Z

Nhodas: New page: I am a graduate student in physics at Caltech in Pasadena, CA. I do both theoretical and experimental work in biophysics and optics. My theoretical work focuses on single molecule dynam...

I am a graduate student in physics at Caltech in Pasadena, CA. I do both theoretical and experimental work in biophysics and optics. My theoretical work focuses on single molecule dynamics and non-linear optical interaction between nanoparticles. I am looking forward to the CSSS and interacting with this diverse group. I hope there are others who more specifically share my enjoyment of hiking and playing squash on the St. John's courts.

'''1. What are your main interests? Feel free to include a "pie in the sky" big idea!''' I am curious about emergent behavior in networks. Cellular gene networks, traffic flow, and the economy are only a few examples of common phenomena where simple interactions properly coordinated give rise to wonderful results. To what extent does the underlying structure of the network determine the final dynamics? Is it possible to capture the overall behavior without knowing the microscopic details of individual interactions?

'''2. What sorts of expertise can you bring to the group?''' I hope my knowledge of non-equilibrium statistical mechanics, stochastic differential equations, and computer programming will help others answer questions they are curious about. In a past life, I studied highway traffic dynamics, and so I have experience connecting microscopic and macroscopic dynamics in the non-physical arena.

'''3. What do you hope to get out of the CSSS?''' I am excited about the multi-disciplinary atmosphere of the CSSS. I know that each discipline has its own perspective on how to interpret experimental data and how to how to critique hypotheses. I hope to absorb as much and as many of the diverse perspectives as I can.

'''4. Do you have any possible projects in mind for the CSSS? ''' I do not have any specific projects in mind, but I would like to do something in line with my interest in emergent behavior, as well as something outside of my primary field.