Author: Andrew Crooks

GeoSocial Gauge

Posted on by

Over the last couple of months we have been working on getting our GeoSocial Gauge system up and running. The idea behind the website is to bring together social media and geographical analysis to monitor and explore people’s views, reactions, and interactions through space and time. It takes advantage of the emergence of social media to observe the human landscape as the living, breathing organism that it is: we can witness the explosion-like dissemination of information within a society, or the clusters of individuals who share common opinions or attitudes, and map the locations of these clusters. This is an unprecedented development that broadens drastically our understanding of the way that people act, react to events, and interact with each other and with their environment. We refer to this novel approach to study the integration of geography and society as GeoSocial Analysis.
The GeoSocial Gauge has several live streams ranging from exploring the political issues (e.g. Sequester) to to see what people are tweeting about TV (The Walking Dead).

Screen shot of GeoSocial Gauge of the Sequester. Showing the location of tweets, the most frequent words and whether or not the messages are positive (green) or negative (red).
Screen shot of GeoSocial Gauge of The Walking Dead.

Some of our initial work on this type of analyis can be found at:

  • Stefanidis, T., Crooks, A.T. and Radzikowski, J. (2013), Harvesting Ambient Geospatial Information from Social Media Feeds, GeoJournal, 78, (2): 319-338.
  • Crooks, A.T., Croitoru, A., Stefanidis, A. and Radzikowski, J. (2013), #Earthquake: Twitter as a Distributed Sensor System, Transactions in GIS, 17(1): 124-147.

Continue reading »
CSS, GeoSocial, twitter

GeoSocial Gauge

Posted on by

Over the last couple of months we have been working on getting our GeoSocial Gauge system up and running. The idea behind the website is to bring together social media and geographical analysis to monitor and explore people’s views, reactions, and interactions through space and time. It takes advantage of the emergence of social media to observe the human landscape as the living, breathing organism that it is: we can witness the explosion-like dissemination of information within a society, or the clusters of individuals who share common opinions or attitudes, and map the locations of these clusters. This is an unprecedented development that broadens drastically our understanding of the way that people act, react to events, and interact with each other and with their environment. We refer to this novel approach to study the integration of geography and society as GeoSocial Analysis.
The GeoSocial Gauge has several live streams ranging from exploring the political issues (e.g. Sequester) to to see what people are tweeting about TV (The Walking Dead).

Screen shot of GeoSocial Gauge of the Sequester. Showing the location of tweets, the most frequent words and whether or not the messages are positive (green) or negative (red).
Screen shot of GeoSocial Gauge of The Walking Dead.

Some of our initial work on this type of analyis can be found at:

  • Stefanidis, T., Crooks, A.T. and Radzikowski, J. (2013), Harvesting Ambient Geospatial Information from Social Media Feeds, GeoJournal, 78, (2): 319-338.
  • Crooks, A.T., Croitoru, A., Stefanidis, A. and Radzikowski, J. (2013), #Earthquake: Twitter as a Distributed Sensor System, Transactions in GIS, 17(1): 124-147.

Continue reading »
CSS, GeoSocial, twitter

Modeling Human Behavior

Posted on by
I have recently been thinking about how do people go about implementing human behavior within agent-based models. There are several good papers out their including Bill Kennedy’s (2012) paper entitled ‘Modelling Human Behavior in Agent-Based Models‘. I thought I would attempt to sum up some of these readings in a blog post but also add to how it links to the main properties of agent-based models.

The reason I do this is that modeling human behavior is not as simple as it sounds. This is because, humans do not just make random decisions, but base their actions upon their knowledge and their abilities. Moreover, it might be nice to think that human behavior is rationale but this is not always the case, decisions can also be based on emotions (e.g. interest, happiness anger, and fear; see Izard, 2007). Moreover, emotions can influence ones decision making by altering our perceptions about the environment and future evaluations (Loewenstein and Lerner, 2003). The question therefore is how do we model human behavior? Over the last decade, one of the dominant ways of modeling human behavior in its many shapes and forms is through agent-based modeling (ABM). ABM allows us to focus on individuals or groups of individuals and give them diverse knowledge and abilities which is not possible in other modeling methodologies (see Crooks and Heppenstall, 2012). This is possible through the unique properties one can endow upon the agents (e.g. people) within such models (see Wooldridge and Jennings, 1995; Franklin and Graesser, 1996; Castle and Crooks, 2006). These properties include:

    • Autonomy: In sense that we can model individual autonomous units which are not centrally governed. Through this property agents are able to process and exchange information with other agents in order to make independent decisions.
    • Heterogeneity: Through using autonomous agents the notion of the average individual is redundant. Each agent can have their own properties and it’s these unique properties of individuals that cause more aggregate phenomena to develop.
    • Activity: As agents are autonomous individuals with heterogeneous properties, they can exert active independent influence within a simulation. There are several ways agents can do this from being proactive (goal directed) for example trying to solve a specific problem. Or they can be reactive, in the sense agents can be designed to perceive their surroundings and given prior knowledge based on experiences (e.g. learning) or observation and take actions accordingly.
      The primary strength of ABMs is as a testing ground for a variety of theoretical assumptions and concepts about human behavior (Stanilov, 2012) within the safe environment of a computer simulation. For example, we know humans process sensory information about the environment, their own current state, and their remembered history to decide what actions to take (Kennedy, 2012) all of which can be incorporated within ABMs. Through the ability to model heterogeneity within ABMs we can capture the uniqueness that makes us human, in the sense that all humans have diverse personality traits (e.g. motivation, emotion, risk avoidance) and complex psychology (Bonabeau, 2002). We also know that human behavior is influenced by others (Friedkin and Johnsen, 1999) say via their social networks which can introduce positive and negative feedbacks into the system and when people form groups, results from such groups can be greater than the sum of the group (Hong and Page, 2004). These properties again can be captured through the agent’s heterogeneity and active status. However, what drives humans? What motivates us to take certain actions? By agents being active we can test ideas and theories (e.g. Maslow’s “Hierarchy of Needs” (Maslow, 1943)) on what motivates people and why do they do certain things.

      Maslow’s Hierarchy of Needs (Source: Wikipedia)
      The question of how to model decision-making within an agent-based model is another important consideration. Kennedy lists three main approaches to capturing such cognitive processes within ABMs (Kennedy, 2012). The first, being a mathematical approach such as the use of ad hoc direct and custom coding of behaviors within the simulation such as using random number generators to select a predefined possible choice (e.g. to buy or sell) (Gode and Sunder, 1993). But, as noted above, people are not random which has lead researchers to develop other methods such as directly incorporating threshold-based rules, i.e. when an environment parameter passes a certain threshold a specific agent behavior will result (e.g. move to a new location when the neighborhood composition reaches a certain percentage) (Crooks, 2010). One could argue that these approaches of modeling are appropriate when behavior can be well specified. The second approach to modeling human behavior uses conceptual cognitive frameworks. Within such models, instead of using thresholds, more abstract concepts such as beliefs, desires, and intentions (BDI, (Rao and Georgeff, 1991)) or physical, emotional, cognitive, and social factors (PECS, (Schmidt, 2002)) are given to individual agents. Both the BDI and PECS frameworks have been successively applied to modeling human behavior in a number of applications such as what drives people to crime (see (Brantingham et al., 2005) and (Malleson, 2012) respectively). These conceptual cognitive frameworks and mathematical approaches for representing behavior can both be considered as rule based systems and are often applied to tens to millions of agents. The third approach, that of cognitive architectures, (e.g. Soar (Laird, 2012) and ACT-R (Anderson and Lebiere, 1998)) focus on abstract or theoretical cognition of one agent at a time with a strong emphasis on artificial intelligence compared to the other two approaches.

      Determining the strongest motive before planing an action (Source: Malleson, 2012).
      ABM offers a new lens to explore human behavior allowing us to move away from more traditional methods such as rational choice theory (Coleman, 1990), where it is assumed that humans behave in ways to maximize their benefits or minimize their costs. However, people rarely meet the requirements of rational choice models (Axelrod, 1997) in the sense that most, if not all people have limited cognitive abilities and limited time to make decisions (Simon, 1996). The incorporation of this bounded rationality (e.g. limited access to information) within agent-based models addresses this issue and can be used to explore many application domains where human behavior is important.

      Any thoughts or comments on what I have written here would be most appreciated.

      References

      • Anderson, J.R. and Lebiere, C. (1998), The Atomic Components of Thought, Mahwah, NJ.
      • Axelrod, R. (1997), ‘Advancing the Art of Simulation in the Social Sciences’, in Conte, R., Hegselmann, R. and Terno, P. (eds.), Simulating Social Phenomena, Springer, Berlin, Germany, pp. 21-40.
      • Bonabeau, E. (2002), ‘Agent-Based Modelling: Methods and Techniques for Simulating Human Systems’, Proceedings of the National Academy of Sciences of the United States of America, 99(3): 7280-7287.
      • Brantingham, P., Glasser, U., Kinney, B., Singh, K. and Vajihollahi, M. (2005), ‘A Computational Model for Simulating Spatial Aspects of Crime in Urban Environments.’ 2005 IEEE International Conference on Systems, Man and Cybernetics (Vol. 4), pp. 3667-3674.
      • Coleman, J.S. (1990), Foundations of Social Theory, Harvard University Press, Cambridge, MA.
      • Crooks, A.T. (2010), ‘Constructing and Implementing an Agent-Based Model of Residential Segregation through Vector GIS’, International Journal of GIS, 24(5): 661-675.
      • Friedkin, N.E. and Johnsen, E.C. (1999), ‘Social Influence Networks and Opinion Change’, Advances in Group Processes, 16(1-29).
      • Gode, D.K. and Sunder, S. (1993), ‘Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality’, The Journal of Political Economy, 101: 119-137.
      • Hong, L. and Page, S.E. (2004), ‘Groups of Diverse Problem Solvers Can Outperform Groups of High-ability Problem Solvers’, Proceedings of the National Academic Sciences, 101(46): 16385-16389.
      • Kennedy, W. (2012), ‘Modelling Human Behaviour in Agent-Based Models’, in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 167-180.
      • Laird, J.E. (2012), The Soar Cognitive Architecture, The MIT Press, Cambridge, MA.
      • Malleson, N. (2012), ‘Using Agent-Based Models to Simulate Crime’, in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 411-434.
      • Maslow, A.H. (1943), ‘A Theory of Human Motivation’, Psychological Review, 50(4): 370-396.
      • Rao, A.S. and Georgeff, M.P. (1991), ‘Modeling Rational Agents within a BDI-architecture’, in Allen, J., Fikes, R. and Sandewall, E. (eds.), Proceedings of the Second International Conference on Principles of Knowledge Representation and Reasoning, San Mateo, CA.
      • Schmidt, B. (2002), ‘The Modelling of Human Behaviour: The PECS Reference Model’, Proceedings 14th European Simulation Symposium, Dresden, Germany.
      • Simon, H.A. (1996), The Sciences of the Artificial (3rd Edition), MIT Press, Cambridge, M. A.
      • Stanilov, K. (2012), ‘Space in Agent-Based Models’, in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 253-271.
        Continue reading »
        ABM, Agent Based Models

        Modeling Human Behavior

        Posted on by
        I have recently been thinking about how do people go about implementing human behavior within agent-based models. There are several good papers out their including Bill Kennedy’s (2012) paper entitled ‘Modelling Human Behavior in Agent-Based Models‘. I thought I would attempt to sum up some of these readings in a blog post but also add to how it links to the main properties of agent-based models.

        The reason I do this is that modeling human behavior is not as simple as it sounds. This is because, humans do not just make random decisions, but base their actions upon their knowledge and their abilities. Moreover, it might be nice to think that human behavior is rationale but this is not always the case, decisions can also be based on emotions (e.g. interest, happiness anger, and fear; see Izard, 2007). Moreover, emotions can influence ones decision making by altering our perceptions about the environment and future evaluations (Loewenstein and Lerner, 2003). The question therefore is how do we model human behavior? Over the last decade, one of the dominant ways of modeling human behavior in its many shapes and forms is through agent-based modeling (ABM). ABM allows us to focus on individuals or groups of individuals and give them diverse knowledge and abilities which is not possible in other modeling methodologies (see Crooks and Heppenstall, 2012). This is possible through the unique properties one can endow upon the agents (e.g. people) within such models (see Wooldridge and Jennings, 1995; Franklin and Graesser, 1996; Castle and Crooks, 2006). These properties include:

          • Autonomy: In sense that we can model individual autonomous units which are not centrally governed. Through this property agents are able to process and exchange information with other agents in order to make independent decisions.
          • Heterogeneity: Through using autonomous agents the notion of the average individual is redundant. Each agent can have their own properties and it’s these unique properties of individuals that cause more aggregate phenomena to develop.
          • Activity: As agents are autonomous individuals with heterogeneous properties, they can exert active independent influence within a simulation. There are several ways agents can do this from being proactive (goal directed) for example trying to solve a specific problem. Or they can be reactive, in the sense agents can be designed to perceive their surroundings and given prior knowledge based on experiences (e.g. learning) or observation and take actions accordingly.
            The primary strength of ABMs is as a testing ground for a variety of theoretical assumptions and concepts about human behavior (Stanilov, 2012) within the safe environment of a computer simulation. For example, we know humans process sensory information about the environment, their own current state, and their remembered history to decide what actions to take (Kennedy, 2012) all of which can be incorporated within ABMs. Through the ability to model heterogeneity within ABMs we can capture the uniqueness that makes us human, in the sense that all humans have diverse personality traits (e.g. motivation, emotion, risk avoidance) and complex psychology (Bonabeau, 2002). We also know that human behavior is influenced by others (Friedkin and Johnsen, 1999) say via their social networks which can introduce positive and negative feedbacks into the system and when people form groups, results from such groups can be greater than the sum of the group (Hong and Page, 2004). These properties again can be captured through the agent’s heterogeneity and active status. However, what drives humans? What motivates us to take certain actions? By agents being active we can test ideas and theories (e.g. Maslow’s “Hierarchy of Needs” (Maslow, 1943)) on what motivates people and why do they do certain things.

            Maslow’s Hierarchy of Needs (Source: Wikipedia)
            The question of how to model decision-making within an agent-based model is another important consideration. Kennedy lists three main approaches to capturing such cognitive processes within ABMs (Kennedy, 2012). The first, being a mathematical approach such as the use of ad hoc direct and custom coding of behaviors within the simulation such as using random number generators to select a predefined possible choice (e.g. to buy or sell) (Gode and Sunder, 1993). But, as noted above, people are not random which has lead researchers to develop other methods such as directly incorporating threshold-based rules, i.e. when an environment parameter passes a certain threshold a specific agent behavior will result (e.g. move to a new location when the neighborhood composition reaches a certain percentage) (Crooks, 2010). One could argue that these approaches of modeling are appropriate when behavior can be well specified. The second approach to modeling human behavior uses conceptual cognitive frameworks. Within such models, instead of using thresholds, more abstract concepts such as beliefs, desires, and intentions (BDI, (Rao and Georgeff, 1991)) or physical, emotional, cognitive, and social factors (PECS, (Schmidt, 2002)) are given to individual agents. Both the BDI and PECS frameworks have been successively applied to modeling human behavior in a number of applications such as what drives people to crime (see (Brantingham et al., 2005) and (Malleson, 2012) respectively). These conceptual cognitive frameworks and mathematical approaches for representing behavior can both be considered as rule based systems and are often applied to tens to millions of agents. The third approach, that of cognitive architectures, (e.g. Soar (Laird, 2012) and ACT-R (Anderson and Lebiere, 1998)) focus on abstract or theoretical cognition of one agent at a time with a strong emphasis on artificial intelligence compared to the other two approaches.

            Determining the strongest motive before planing an action (Source: Malleson, 2012).
            ABM offers a new lens to explore human behavior allowing us to move away from more traditional methods such as rational choice theory (Coleman, 1990), where it is assumed that humans behave in ways to maximize their benefits or minimize their costs. However, people rarely meet the requirements of rational choice models (Axelrod, 1997) in the sense that most, if not all people have limited cognitive abilities and limited time to make decisions (Simon, 1996). The incorporation of this bounded rationality (e.g. limited access to information) within agent-based models addresses this issue and can be used to explore many application domains where human behavior is important.

            Any thoughts or comments on what I have written here would be most appreciated.

            References

            • Anderson, J.R. and Lebiere, C. (1998), The Atomic Components of Thought, Mahwah, NJ.
            • Axelrod, R. (1997), ‘Advancing the Art of Simulation in the Social Sciences’, in Conte, R., Hegselmann, R. and Terno, P. (eds.), Simulating Social Phenomena, Springer, Berlin, Germany, pp. 21-40.
            • Bonabeau, E. (2002), ‘Agent-Based Modelling: Methods and Techniques for Simulating Human Systems’, Proceedings of the National Academy of Sciences of the United States of America, 99(3): 7280-7287.
            • Brantingham, P., Glasser, U., Kinney, B., Singh, K. and Vajihollahi, M. (2005), ‘A Computational Model for Simulating Spatial Aspects of Crime in Urban Environments.’ 2005 IEEE International Conference on Systems, Man and Cybernetics (Vol. 4), pp. 3667-3674.
            • Coleman, J.S. (1990), Foundations of Social Theory, Harvard University Press, Cambridge, MA.
            • Crooks, A.T. (2010), ‘Constructing and Implementing an Agent-Based Model of Residential Segregation through Vector GIS’, International Journal of GIS, 24(5): 661-675.
            • Friedkin, N.E. and Johnsen, E.C. (1999), ‘Social Influence Networks and Opinion Change’, Advances in Group Processes, 16(1-29).
            • Gode, D.K. and Sunder, S. (1993), ‘Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality’, The Journal of Political Economy, 101: 119-137.
            • Hong, L. and Page, S.E. (2004), ‘Groups of Diverse Problem Solvers Can Outperform Groups of High-ability Problem Solvers’, Proceedings of the National Academic Sciences, 101(46): 16385-16389.
            • Kennedy, W. (2012), ‘Modelling Human Behaviour in Agent-Based Models’, in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 167-180.
            • Laird, J.E. (2012), The Soar Cognitive Architecture, The MIT Press, Cambridge, MA.
            • Malleson, N. (2012), ‘Using Agent-Based Models to Simulate Crime’, in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 411-434.
            • Maslow, A.H. (1943), ‘A Theory of Human Motivation’, Psychological Review, 50(4): 370-396.
            • Rao, A.S. and Georgeff, M.P. (1991), ‘Modeling Rational Agents within a BDI-architecture’, in Allen, J., Fikes, R. and Sandewall, E. (eds.), Proceedings of the Second International Conference on Principles of Knowledge Representation and Reasoning, San Mateo, CA.
            • Schmidt, B. (2002), ‘The Modelling of Human Behaviour: The PECS Reference Model’, Proceedings 14th European Simulation Symposium, Dresden, Germany.
            • Simon, H.A. (1996), The Sciences of the Artificial (3rd Edition), MIT Press, Cambridge, M. A.
            • Stanilov, K. (2012), ‘Space in Agent-Based Models’, in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 253-271.
              Continue reading »
              ABM, Agent Based Models

              Fusing remote sensing with demographic data for synthetic population generation

              Posted on by
              When building agent-based models related to “real” world locations and people, the challenge is to build agents which resemble people on the ground. I have blogged about microsimulation approaches before and their utility with respect to agent-based models. A new paper in the International Journal of Geographical Information Science by alumnae from the Department of Computational Social Science here at GMU have developed a new algorithm which could prove useful. Below is abstract of the paper:

              We develop a new algorithm for population synthesis that fuses remote-sensing data with partial and sparse demographic surveys. The algorithm addresses non-binding constraints and complex sampling designs by translating population synthesis into a computationally efficient procedure for constrained network growth. As a case, we synthesize the rural population of Afghanistan, validate the algorithm with in-sample and out-of-sample tests, examine the variability of algorithm outputs over k-nearest neighbor manifolds, and show the responsiveness of our algorithm to additional data as a constraint on marginal population counts.

              Full Reference:

              Rizi, S.M.M., Łatek, M.M. and Geller, A. (2012), ‘Fusing Remote Sensing with Sparse Demographic Data for Synthetic Population Generation: An Algorithm and Application to Rural Afghanistan’, International Journal of Geographical Information Science, DOI:10.1080/13658816.2012.734825.

              Continue reading »
              ABM, Microsimulation, Remote Sensing

              Fusing remote sensing with demographic data for synthetic population generation

              Posted on by
              When building agent-based models related to “real” world locations and people, the challenge is to build agents which resemble people on the ground. I have blogged about microsimulation approaches before and their utility with respect to agent-based models. A new paper in the International Journal of Geographical Information Science by alumnae from the Department of Computational Social Science here at GMU have developed a new algorithm which could prove useful. Below is abstract of the paper:

              We develop a new algorithm for population synthesis that fuses remote-sensing data with partial and sparse demographic surveys. The algorithm addresses non-binding constraints and complex sampling designs by translating population synthesis into a computationally efficient procedure for constrained network growth. As a case, we synthesize the rural population of Afghanistan, validate the algorithm with in-sample and out-of-sample tests, examine the variability of algorithm outputs over k-nearest neighbor manifolds, and show the responsiveness of our algorithm to additional data as a constraint on marginal population counts.

              Full Reference:

              Rizi, S.M.M., Łatek, M.M. and Geller, A. (2012), ‘Fusing Remote Sensing with Sparse Demographic Data for Synthetic Population Generation: An Algorithm and Application to Rural Afghanistan’, International Journal of Geographical Information Science, DOI:10.1080/13658816.2012.734825.

              Continue reading »
              ABM, Microsimulation, Remote Sensing

              Call for Papers: 9th European Social Simulation Association Conference

              Posted on by
              9th European Social Simulation Association Conference
              Warsaw School of Economics, Warsaw, Poland
              September 16-20, 2013

              The European Social Simulation Association and Warsaw School of Economics, Division of Decision Analysis and Support, invite researchers and scholars interested in applications and theoretical foundations of simulation modeling in social sciences to participate in the 9th European Social Simulation Association Conference.

              The conference aims to provide an interdisciplinary forum for social scientists, theorists, applied researchers and simulation modelers to cooperate and exchange ideas concerning state of the art in methods and applications of computational social sciences.

              Scope and Interests

              The topics of interest include, but are not limited to:

              • Applications of agent-based modeling in social sciences
                • Agent-based computational economics & finance
                • Conflict resolution & cooperation
                • Coupled human-natural systems
                • Diffusion of innovations
                • Dynamics of trust, social norm, structures, reputation & opinion
                • Epidemiology & pharmacoeconomics
                • Group decisions & collective behaviors
                • Market design, mechanism design & auctions
                • Privacy, safety & security
                • Public policy & regulatory issues
                • Resource management, environmental practices & policy
                • Social emergence & evolution of institutions
                • Social media and volunteered information
                • Social networks and their dynamics
              • Tools and methods for development of simulation models 
                • Advanced distributed computing
                • Agent ontologies
                • Agent-embodied Artificial Intelligence
                • Model replication, verification & validation
                • Participatory & Human-in-the-Loop simulations
                • Simulation software & programming computational frameworks
              • Techniques for visualizing, interpreting and analyzing simulation outputs
                • Coupling simulations and optimization methods
                • Data analysis software for simulations
                • Experiment design and data farming for simulations
                • Simulation metamodels
                • Statistical & data mining techniques for simulated data

              Submission

              • Full paper – in the length of 10 to 12 pages, which should be comprehensive and consists of detailed presentation of theory, methodology and simulation results
              • Extended abstract – in the length of 3 to 4 pages, which presents the current topic in progress with enough detail to ensure proper evaluation
              • Poster – should present the current work in progress

               Contributions can be submitted to general session or the following special tracks:

              • Adaptive behavior , social interactions and global environmental change: an agent-based perspective (chairs: T. Filatova, G. Polhill, R. van Duinen)
              • Applications of computational social science in conflict and sensitive contexts (chair: A. Geller)
              • Business applications of computational social science (chair: M. Łatek)
              • Heterogeneity and interaction in macroeconomic modeling (chair: G. Koloch)
              • Social simulation of science processes (chair: F. Squazzoni)
              • Statistical analysis of simulation models (chair: B. Kamiński)
              • Using qualitative rules to inform behavioral rules (chair: B. Edmonds)

              Important dates

              • Registration opens: 1 January 2013
              • Paper submission closes: 1 March 2013
              • Notification of acceptance: 15 April 2013
              • Final manuscript due: 15 May 2013

              Paper publication

              • Accepted full papers will be published by Springer in the conference proceedings series “Advances in Intelligent Systems and Computing”, http://www.springer.com/series/11156 (indexed by ISI Proceedings, DBLP, Ulrich’s, EI-Compendex, SCOPUS, Zentralblatt Math, MetaPress, Springerlink).
              • Authors of selected full papers or extended abstracts will be invited to submit their extended version for special issue of Central European Journal of Economic Modelling and Econometrics, http://cejeme.org/ (indexed by IndexCopernicus, IC Value in 2011: 4.85 and RePEc).
              • Full paper abstracts, extended abstracts and poster summaries will be presented on conference website.

              Local organizing committee contact: contact@essa2013.org
              Conference website: http://www.essa2013.org

              Continue reading »
              Uncategorized

              Call for Papers: 9th European Social Simulation Association Conference

              Posted on by
              9th European Social Simulation Association Conference
              Warsaw School of Economics, Warsaw, Poland
              September 16-20, 2013

              The European Social Simulation Association and Warsaw School of Economics, Division of Decision Analysis and Support, invite researchers and scholars interested in applications and theoretical foundations of simulation modeling in social sciences to participate in the 9th European Social Simulation Association Conference.

              The conference aims to provide an interdisciplinary forum for social scientists, theorists, applied researchers and simulation modelers to cooperate and exchange ideas concerning state of the art in methods and applications of computational social sciences.

              Scope and Interests

              The topics of interest include, but are not limited to:

              • Applications of agent-based modeling in social sciences
                • Agent-based computational economics & finance
                • Conflict resolution & cooperation
                • Coupled human-natural systems
                • Diffusion of innovations
                • Dynamics of trust, social norm, structures, reputation & opinion
                • Epidemiology & pharmacoeconomics
                • Group decisions & collective behaviors
                • Market design, mechanism design & auctions
                • Privacy, safety & security
                • Public policy & regulatory issues
                • Resource management, environmental practices & policy
                • Social emergence & evolution of institutions
                • Social media and volunteered information
                • Social networks and their dynamics
              • Tools and methods for development of simulation models 
                • Advanced distributed computing
                • Agent ontologies
                • Agent-embodied Artificial Intelligence
                • Model replication, verification & validation
                • Participatory & Human-in-the-Loop simulations
                • Simulation software & programming computational frameworks
              • Techniques for visualizing, interpreting and analyzing simulation outputs
                • Coupling simulations and optimization methods
                • Data analysis software for simulations
                • Experiment design and data farming for simulations
                • Simulation metamodels
                • Statistical & data mining techniques for simulated data

              Submission

              • Full paper – in the length of 10 to 12 pages, which should be comprehensive and consists of detailed presentation of theory, methodology and simulation results
              • Extended abstract – in the length of 3 to 4 pages, which presents the current topic in progress with enough detail to ensure proper evaluation
              • Poster – should present the current work in progress

               Contributions can be submitted to general session or the following special tracks:

              • Adaptive behavior , social interactions and global environmental change: an agent-based perspective (chairs: T. Filatova, G. Polhill, R. van Duinen)
              • Applications of computational social science in conflict and sensitive contexts (chair: A. Geller)
              • Business applications of computational social science (chair: M. Łatek)
              • Heterogeneity and interaction in macroeconomic modeling (chair: G. Koloch)
              • Social simulation of science processes (chair: F. Squazzoni)
              • Statistical analysis of simulation models (chair: B. Kamiński)
              • Using qualitative rules to inform behavioral rules (chair: B. Edmonds)

              Important dates

              • Registration opens: 1 January 2013
              • Paper submission closes: 1 March 2013
              • Notification of acceptance: 15 April 2013
              • Final manuscript due: 15 May 2013

              Paper publication

              • Accepted full papers will be published by Springer in the conference proceedings series “Advances in Intelligent Systems and Computing”, http://www.springer.com/series/11156 (indexed by ISI Proceedings, DBLP, Ulrich’s, EI-Compendex, SCOPUS, Zentralblatt Math, MetaPress, Springerlink).
              • Authors of selected full papers or extended abstracts will be invited to submit their extended version for special issue of Central European Journal of Economic Modelling and Econometrics, http://cejeme.org/ (indexed by IndexCopernicus, IC Value in 2011: 4.85 and RePEc).
              • Full paper abstracts, extended abstracts and poster summaries will be presented on conference website.

              Local organizing committee contact: contact@essa2013.org
              Conference website: http://www.essa2013.org

              Continue reading »
              Uncategorized

              Research Update

              Posted on by
              With the semester now well underway, I have been reflecting on some of the recent work we have been doing at George Mason University. This is currently taking two strands, the first being agent-based modeling and the second being deriving information from social media. Hopefully by the end of the semester, these two strands will be merged together.
              One of the models we are working on is the movement of people across national boarders. Below is a visualization of our work looking at the movment of people across the US/Mexico border which a specific focus on Arizona.
              Moreover, we have continued to work diseases and refugee camps. We are scaling up the model to represent the entire population of the Dadaab refugee camps along with verifying the model and exploring the spatial characteristics of the model (i.e the spread of cholera). If anyone will be at the annual North American Meetings of the Regional Science Association International in Ottowa  Canada feel free to come and listen to our presentation. The movie directly below shows the spread of cholera in one camp, while the second movie shows how cholera can be spread throughout the camps by people becoming infected and moving between the different camps.

              Some of this work has been feated in UPMagazine and Trajectory Magazine:

              Metcalfe, M. (2012), The Bounds of Rationality, UP Magazine, May, Issue 5: 40-43.

              Quinn, K. (2012), Visualizing the Invisible:GMU Pioneers a New Approach to Harvesting GEOINT, Trajectory Magazine, Fall, 11-12.

              Continue reading »
              ABM, social media, Social network analysis

              Research Update

              Posted on by
              With the semester now well underway, I have been reflecting on some of the recent work we have been doing at George Mason University. This is currently taking two strands, the first being agent-based modeling and the second being deriving information from social media. Hopefully by the end of the semester, these two strands will be merged together.
              One of the models we are working on is the movement of people across national boarders. Below is a visualization of our work looking at the movment of people across the US/Mexico border which a specific focus on Arizona.

              Moreover, we have continued to work diseases and refugee camps. We are scaling up the model to represent the entire population of the Dadaab refugee camps along with verifying the model and exploring the spatial characteristics of the model (i.e the spread of cholera). If anyone will be at the annual North American Meetings of the Regional Science Association International in Ottowa  Canada feel free to come and listen to our presentation. The movie directly below shows the spread of cholera in one camp, while the second movie shows how cholera can be spread throughout the camps by people becoming infected and moving between the different camps.



              Some of this work has been feated in UPMagazine and Trajectory Magazine:

              Metcalfe, M. (2012), The Bounds of Rationality, UP Magazine, May, Issue 5: 40-43.

              Quinn, K. (2012), Visualizing the Invisible:GMU Pioneers a New Approach to Harvesting GEOINT, Trajectory Magazine, Fall, 11-12.

              Continue reading »
              ABM, social media, Social network analysis
              1 … 7 8 9 10 11 … 13