Applications of Agent-based Models

Often I get asked the question along the lines of: “how are agent-based models are being used outside academia, especially in government and private industry?” So I thought it was about time I briefly write something about this.

Let me start with a question I ask my students when I first introduce agent-based modeling: “Have you ever seen an agent-based model before?” Often the answer is NO, but then I show them the following clip from MASSIVE (Multiple Agent Simulation System in Virtual Environment) where agent-based models are used in a variety of movies and TV shows. But apart from TV shows and movies where else have agent-based models been used?
There are two specific application domains where agent-based modeling has taken off. The first being pedestrian simulation for example, LegionSteps and EXODUS simulation platforms. The second is the area of traffic modeling for example, there are several microsimulation/agent-based model platforms such as PTV Visum, TransModeler and Paramics. Based on these companies websites they have clients in industry, government and academia.
If we move away from the areas discussed above, there is a lot of writing about the potential of agent-based modeling. For example, the Bank of England had a article entitled “Agent-based models: understanding the economy from the bottom up” which to quote from the summary:
“considers the strengths of agent-based modelling, which explains the behaviour of a system by simulating the behaviour of each individual ‘agent’ in it, and the ways that it can be used to help central banks understand the economy.”

Similar articles can be seen in the New York Times and the Guardian to name but a few. But where else have agent-based models been used? A sample (and definitely not an exhaustive list) of applications and references are provided below for interested readers:
  • Southwest Airlines used an agent-based model to improve how it handled cargo (Seibel and Thomas, 2000).
  • Eli Lilly used an agent-based model for drug development (Bonabeau, 2003a).
  • Pacific Gas and Electric: Used an agent based model to see how energy flows through the power grid (Bonabeau, 2003a).
  • Procter and Gamble used an agent-based model to understand its consumer markets (North et al., 2010) while Hewlett-Packard used an agent-based model to understand how hiring strategies effect corporate culture (Bonabeau, 2003b).
  • Macy’s have used agent-based models for store design (Bonabeau, 2003b).
  • NASDAQ used and agent based model to explore changes to Stock Market’s decimalization (Bonabeau, 2003b; Darley and Outkin, 2007).
  • Using a agent-based model to explore capacity and demand in theme parks (Bonabeau, 2000).
  • Traffic and pedestrian modeling (Helbing and Balietti, 2011).
  • Disease dynamics (e.g. Eubank et al., 2004).
  • Agent-based modeling has also been used for wild fire training, incident command and community outreach (Guerin and Carrera, 2010). For example SimTable was used in the  2016 Sand Fire in California. 
  • InSTREAM: Explores how river salmon populations react to changes (Railsback and Harvey, 2002).

While not a comprehensive list, it is hoped that these examples and links will be useful if someone asks the question I started this post with. If anyone else knows of any other real world applications of agent-based modeling please let me know (preferably with a link to a paper or website).
 
References

  • Bonabeau, E. (2000), ‘Business Applications of Social Agent-Based Simulation’, Advances in Complex Systems, 3(1-4): 451-461.
  • Bonabeau, E. (2003a), ‘Don’t Trust Your Gut’, Harvard Business Review, 81(5): 116-123.
  • Bonabeau, E. (2003b), ‘Predicting the Unpredictable’, Harvard Business Review, 80(3): 109-116.
  • Darley, V. and Outkin, A.V. (2007), NASDAQ Market Simulation: Insights on a Major Market from the Science of Complex Adaptive Systems, World Scientific Publishing, River Edge, NJ.
  • Eubank, S., Guclu, H., Kumar, A.V.S., Marathe, M.V., Srinivasan, A., Toroczkai, Z. and Wang, N. (2004), ‘Modelling Disease Outbreaks in Realistic Urban Social Networks’, Nature, 429: 180-184.
  • Guerin, S. and Carrera, F. (2010), ‘Sand on Fire: An Interactive Tangible 3D Platform for the Modeling and Management of Wildfires.’ WIT Transactions on Ecology and the Environment, 137: 57-68.
  • Helbing, D. and Balietti, S. (2011), How to do Agent-based Simulations in the Future: From Modeling Social Mechanisms to Emergent Phenomena and Interactive Systems Design, Santa Fe Institute, Working Paper 11-06-024, Santa Fe, NM.
  • North, M.J., Macal, C.M., Aubin, J.S., Thimmapuram, P., Bragen, M., Hahn, J., J., K., Brigham, N., Lacy, M.E. and Hampton, D. (2010), ‘Multiscale Agent-based Consumer Market Modeling’, Complexity, 15(5): 37-47.
  • Railsback, S.F. and Harvey, B.C. (2002), ‘Analysis of Habitat Selection Rules using an Individual-based Model’, Ecology, 83(7): 1817-1830.
  • Seibel, F. and Thomas, C. (2000), ‘Manifest Destiny: Adaptive Cargo Routing at Southwest Airlines’, Perspectives on Business Innovation, 4: 27-33.

    Continue reading »

    Applications of Agent-based Models

    Often I get asked the question along the lines of: “how are agent-based models are being used outside academia, especially in government and private industry?” So I thought it was about time I briefly write something about this.

    Let me start with a question I ask my students when I first introduce agent-based modeling: “Have you ever seen an agent-based model before?” Often the answer is NO, but then I show them the following clip from MASSIVE (Multiple Agent Simulation System in Virtual Environment) where agent-based models are used in a variety of movies and TV shows. But apart from TV shows and movies where else have agent-based models been used?
    There are two specific application domains where agent-based modeling has taken off. The first being pedestrian simulation for example, LegionSteps and EXODUS simulation platforms. The second is the area of traffic modeling for example, there are several microsimulation/agent-based model platforms such as PTV Visum, TransModeler and Paramics. Based on these companies websites they have clients in industry, government and academia.
    If we move away from the areas discussed above, there is a lot of writing about the potential of agent-based modeling. For example, the Bank of England had a article entitled “Agent-based models: understanding the economy from the bottom up” which to quote from the summary:
    “considers the strengths of agent-based modelling, which explains the behaviour of a system by simulating the behaviour of each individual ‘agent’ in it, and the ways that it can be used to help central banks understand the economy.”

    Similar articles can be seen in the New York Times and the Guardian to name but a few. But where else have agent-based models been used? A sample (and definitely not an exhaustive list) of applications and references are provided below for interested readers:
    • Southwest Airlines used an agent-based model to improve how it handled cargo (Seibel and Thomas, 2000).
    • Eli Lilly used an agent-based model for drug development (Bonabeau, 2003a).
    • Pacific Gas and Electric: Used an agent based model to see how energy flows through the power grid (Bonabeau, 2003a).
    • Procter and Gamble used an agent-based model to understand its consumer markets (North et al., 2010) while Hewlett-Packard used an agent-based model to understand how hiring strategies effect corporate culture (Bonabeau, 2003b).
    • Macy’s have used agent-based models for store design (Bonabeau, 2003b).
    • NASDAQ used and agent based model to explore changes to Stock Market’s decimalization (Bonabeau, 2003b; Darley and Outkin, 2007).
    • Using a agent-based model to explore capacity and demand in theme parks (Bonabeau, 2000).
    • Traffic and pedestrian modeling (Helbing and Balietti, 2011).
    • Disease dynamics (e.g. Eubank et al., 2004).
    • Agent-based modeling has also been used for wild fire training, incident command and community outreach (Guerin and Carrera, 2010). For example SimTable was used in the  2016 Sand Fire in California. 
    • InSTREAM: Explores how river salmon populations react to changes (Railsback and Harvey, 2002).

    While not a comprehensive list, it is hoped that these examples and links will be useful if someone asks the question I started this post with. If anyone else knows of any other real world applications of agent-based modeling please let me know (preferably with a link to a paper or website).
     
    References

    • Bonabeau, E. (2000), ‘Business Applications of Social Agent-Based Simulation’, Advances in Complex Systems, 3(1-4): 451-461.
    • Bonabeau, E. (2003a), ‘Don’t Trust Your Gut’, Harvard Business Review, 81(5): 116-123.
    • Bonabeau, E. (2003b), ‘Predicting the Unpredictable’, Harvard Business Review, 80(3): 109-116.
    • Darley, V. and Outkin, A.V. (2007), NASDAQ Market Simulation: Insights on a Major Market from the Science of Complex Adaptive Systems, World Scientific Publishing, River Edge, NJ.
    • Eubank, S., Guclu, H., Kumar, A.V.S., Marathe, M.V., Srinivasan, A., Toroczkai, Z. and Wang, N. (2004), ‘Modelling Disease Outbreaks in Realistic Urban Social Networks’, Nature, 429: 180-184.
    • Guerin, S. and Carrera, F. (2010), ‘Sand on Fire: An Interactive Tangible 3D Platform for the Modeling and Management of Wildfires.’ WIT Transactions on Ecology and the Environment, 137: 57-68.
    • Helbing, D. and Balietti, S. (2011), How to do Agent-based Simulations in the Future: From Modeling Social Mechanisms to Emergent Phenomena and Interactive Systems Design, Santa Fe Institute, Working Paper 11-06-024, Santa Fe, NM.
    • North, M.J., Macal, C.M., Aubin, J.S., Thimmapuram, P., Bragen, M., Hahn, J., J., K., Brigham, N., Lacy, M.E. and Hampton, D. (2010), ‘Multiscale Agent-based Consumer Market Modeling’, Complexity, 15(5): 37-47.
    • Railsback, S.F. and Harvey, B.C. (2002), ‘Analysis of Habitat Selection Rules using an Individual-based Model’, Ecology, 83(7): 1817-1830.
    • Seibel, F. and Thomas, C. (2000), ‘Manifest Destiny: Adaptive Cargo Routing at Southwest Airlines’, Perspectives on Business Innovation, 4: 27-33.

      Continue reading »

      The study of slums as social and physical constructs: challenges and emerging research opportunities

      Conceptual model for integrating social
      and physical constructs to monitor,
      analyze and model slums.

      Continuing our research on slums, we have just had a paper published in the journal Regional Studies, Regional Science entitled “The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities“. In this open access publication we review past lines of research with respect to studying slums which often focus on one of three constructs: (1) exploring the socio-economic and policy issues; (2) exploring the physical characteristics; and, lastly, (3) those modelling slums. We argue that while such lines of inquiry have proved invaluable with respect to studying slums, there is a need for  a  more  holistic  approach  for  studying  slums  to truly understand  them at the local, national and regional scales. Below you can read the abstract of our paper:

      “Over 1 billion people currently live in slums, with the number of slum dwellers only expected to grow in the coming decades. The vast majority of slums are located in and around urban centres in the less economically developed countries, which are also experiencing greater rates of urbanization compared with more developed countries. This rapid rate of urbanization is cause for significant concern given that many of these countries often lack the ability to provide the infrastructure (e.g., roads and affordable housing) and basic services (e.g., water and sanitation) to provide adequately for the increasing influx of people into cities. While research on slums has been ongoing, such work has mainly focused on one of three constructs: exploring the socio-economic and policy issues; exploring the physical characteristics; and, lastly, those modelling slums. This paper reviews these lines of research and argues that while each is valuable, there is a need for a more holistic approach for studying slums to truly understand them. By synthesizing the social and physical constructs, this paper provides a more holistic synthesis of the problem, which can potentially lead to a deeper understanding and, consequently, better approaches for tackling the challenge of slums at the local, national and regional scales.”

      Keywords: Slums; informal settlements; socio-economic; remote sensing; crowdsourced information; modelling.

      Framework for studying and understanding slums.

      We hope you enjoy this paper and we wound be interested in receiving any feedback.
      Full Reference:

      Mahabir, R., Crooks, A.T., Croitoru, A. and Agouris, P. (2016), “The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities”, Regional Studies, Regional Science, 3(1): 737-757. (pdf)

      Continue reading »

      The study of slums as social and physical constructs: challenges and emerging research opportunities

      Conceptual model for integrating social
      and physical constructs to monitor,
      analyze and model slums.

      Continuing our research on slums, we have just had a paper published in the journal Regional Studies, Regional Science entitled “The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities“. In this open access publication we review past lines of research with respect to studying slums which often focus on one of three constructs: (1) exploring the socio-economic and policy issues; (2) exploring the physical characteristics; and, lastly, (3) those modelling slums. We argue that while such lines of inquiry have proved invaluable with respect to studying slums, there is a need for  a  more  holistic  approach  for  studying  slums  to truly understand  them at the local, national and regional scales. Below you can read the abstract of our paper:

      “Over 1 billion people currently live in slums, with the number of slum dwellers only expected to grow in the coming decades. The vast majority of slums are located in and around urban centres in the less economically developed countries, which are also experiencing greater rates of urbanization compared with more developed countries. This rapid rate of urbanization is cause for significant concern given that many of these countries often lack the ability to provide the infrastructure (e.g., roads and affordable housing) and basic services (e.g., water and sanitation) to provide adequately for the increasing influx of people into cities. While research on slums has been ongoing, such work has mainly focused on one of three constructs: exploring the socio-economic and policy issues; exploring the physical characteristics; and, lastly, those modelling slums. This paper reviews these lines of research and argues that while each is valuable, there is a need for a more holistic approach for studying slums to truly understand them. By synthesizing the social and physical constructs, this paper provides a more holistic synthesis of the problem, which can potentially lead to a deeper understanding and, consequently, better approaches for tackling the challenge of slums at the local, national and regional scales.”

      Keywords: Slums; informal settlements; socio-economic; remote sensing; crowdsourced information; modelling.

      Framework for studying and understanding slums.

      We hope you enjoy this paper and we wound be interested in receiving any feedback.
      Full Reference:

      Mahabir, R., Crooks, A.T., Croitoru, A. and Agouris, P. (2016), “The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities”, Regional Studies, Regional Science, 3(1): 737-757. (pdf)

      Continue reading »

      Call For Papers: Rethinking the ABCs

      Readers of the blog might be interested in a workshop being organized by Daniel Brown, Eun-Kyeong Kim, Liliana Perez, and Raja Sengupta entitled:

      Rethinking the ABCs: Agent-Based Models and Complexity Science in the age of Big Data, CyberGIS, and Sensor networks

      September 27th, 2016 in Montreal, Canada

      To quote from the call:

      “A broad scope of concepts and methodologies from complexity science – including Agent-Based Models, Cellular Automata, network theory, chaos theory, and scaling relations – has contributed to a better understanding of spatial/temporal dynamics of complex geographic patterns and process.

      Recent advances in computational technologies such as Big Data, Cloud Computing and CyberGIS platforms, and Sensor Networks (i.e. the Internet of Things) provides both new opportunities and raises new challenges for ABM and complexity theory research within GIScience. Challenges include parameterization of complex models with volumes of georeferenced data being generated, scale model applications to realistic simulations over broader geographic extents, explore the challenges in their deployment across large networks to take advantage of increased computational power, and validate their output using real-time data, as well as measure the impact of the simulation on knowledge, information and decision-making both locally and globally via the world wide web.

      The scope of this workshop is to explore novel complexity science approaches to dynamic geographic phenomena and their applications, addressing challenges and enriching research methodologies in geography in a Big Data Era.”

      More information about the workshop can be found at https://sites.psu.edu/bigcomplexitygisci/

      Continue reading »

      Call For Papers: Rethinking the ABCs

      Readers of the blog might be interested in a workshop being organized by Daniel Brown, Eun-Kyeong Kim, Liliana Perez, and Raja Sengupta entitled:

      Rethinking the ABCs: Agent-Based Models and Complexity Science in the age of Big Data, CyberGIS, and Sensor networks

      September 27th, 2016 in Montreal, Canada

      To quote from the call:

      “A broad scope of concepts and methodologies from complexity science – including Agent-Based Models, Cellular Automata, network theory, chaos theory, and scaling relations – has contributed to a better understanding of spatial/temporal dynamics of complex geographic patterns and process.

      Recent advances in computational technologies such as Big Data, Cloud Computing and CyberGIS platforms, and Sensor Networks (i.e. the Internet of Things) provides both new opportunities and raises new challenges for ABM and complexity theory research within GIScience. Challenges include parameterization of complex models with volumes of georeferenced data being generated, scale model applications to realistic simulations over broader geographic extents, explore the challenges in their deployment across large networks to take advantage of increased computational power, and validate their output using real-time data, as well as measure the impact of the simulation on knowledge, information and decision-making both locally and globally via the world wide web.

      The scope of this workshop is to explore novel complexity science approaches to dynamic geographic phenomena and their applications, addressing challenges and enriching research methodologies in geography in a Big Data Era.”

      More information about the workshop can be found at https://sites.psu.edu/bigcomplexitygisci/

      Continue reading »

      Modeling the outbreak, spread, and containment of tuberculosis

      It seems my interest into disease models is growing. While the development of the cholera model is still underway, over the summer I have had been working with a very talented high school student looking at the outbreak, spread and containment of tuberculosis (TB). Why might you ask? TB is a global problem with 1.8 billion people having a TB Infection, 8.8 million people infected with the TB disease, and around 1.5 million annual deaths. It is the second most common form of death from an infectious disease with the majority of cases in developing countries.

      So we have been developing a model that explores how TB might manifest itself, spread within an urban setting and the potential to contain the disease. We have chosen as our test case the Kibera slum within Nairobi, Kenya. Agents in this model represent the residents of the Kibera slum. They are mobile and goal-orientated, seeking to fulfill one goal before moving on to the next. Goals are determined based on the agent’s characteristics (age, sex, etc.) as well as their needs (water, food, health etc.). The exact location they choose to go to is also affected by the distance. When agents interact with one another, they can be infected with TB. Infection is determined upon the amount of bacilli absorbed by agents and their immune response. The transition from infection to disease for HIV positive patients is also dependent on the patient’s CD4 cell count.  What you see below is a poster we presented at Krasnow Institute Retreat.

      To give a sense of the dynamics of the model, the movie below shows agents moving around the slum and how their health status changes as time progresses.

      Continue reading »

      Modeling the outbreak, spread, and containment of tuberculosis

      It seems my interest into disease models is growing. While the development of the cholera model is still underway, over the summer I have had been working with a very talented high school student looking at the outbreak, spread and containment of tuberculosis (TB). Why might you ask? TB is a global problem with 1.8 billion people having a TB Infection, 8.8 million people infected with the TB disease, and around 1.5 million annual deaths. It is the second most common form of death from an infectious disease with the majority of cases in developing countries.

      So we have been developing a model that explores how TB might manifest itself, spread within an urban setting and the potential to contain the disease. We have chosen as our test case the Kibera slum within Nairobi, Kenya. Agents in this model represent the residents of the Kibera slum. They are mobile and goal-orientated, seeking to fulfill one goal before moving on to the next. Goals are determined based on the agent’s characteristics (age, sex, etc.) as well as their needs (water, food, health etc.). The exact location they choose to go to is also affected by the distance. When agents interact with one another, they can be infected with TB. Infection is determined upon the amount of bacilli absorbed by agents and their immune response. The transition from infection to disease for HIV positive patients is also dependent on the patient’s CD4 cell count.  What you see below is a poster we presented at Krasnow Institute Retreat.

      To give a sense of the dynamics of the model, the movie below shows agents moving around the slum and how their health status changes as time progresses.

      Continue reading »

      Modeling Human Behavior

      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.
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