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Spatial Behavior in Groups: an Agent-Based Approach

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Abstract

We present an agent-based model with the aim of studying how macro-level dynamics of spatial distances among interacting individuals in a closed space emerge from micro-level dyadic and local interactions. Our agents moved on a lattice (referred to as a room) using a model implemented in a computer program called P-Space in order to minimize their dissatisfaction, defined as a function of the discrepancy between the real distance and the ideal, or desired, distance between agents. Ideal distances evolved in accordance with the agent's personal and social space, which changed throughout the dynamics of the interactions among the agents. In the first set of simulations we studied the effects of the parameters of the function that generated ideal distances, and in a second set we explored how group macro-level behavior depended on model parameters and other variables. We learned that certain parameter values yielded consistent patterns in the agents' personal and social spaces, which in turn led to avoidance and approaching behaviors in the agents. We also found that the spatial behavior of the group of agents as a whole was influenced by the values of the model parameters, as well as by other variables such as the number of agents. Our work demonstrates that the bottom-up approach is a useful way of explaining macro-level spatial behavior. The proposed model is also shown to be a powerful tool for simulating the spatial behavior of groups of interacting individuals.

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  • Francesc S. Beltran & Laura Salas & Vicenç Quera, 2006. "Spatial Behavior in Groups: an Agent-Based Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(3), pages 1-5.
  • Handle: RePEc:jas:jasssj:2005-76-2
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    File URL: http://jasss.soc.surrey.ac.uk/9/3/5/5.pdf
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    1. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    2. Guillaume Deffuant & Frederic Amblard & Gérard Weisbuch, 2002. "How Can Extremism Prevail? a Study Based on the Relative Agreement Interaction Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1-1.
    3. Amblard, Frédéric & Deffuant, Guillaume, 2004. "The role of network topology on extremism propagation with the relative agreement opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 343(C), pages 725-738.
    4. Weisbuch, Gérard & Deffuant, Guillaume & Amblard, Frédéric, 2005. "Persuasion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 555-575.
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    1. repec:eee:thpobi:v:82:y:2012:i:1:p:48-58 is not listed on IDEAS
    2. Pedro Ribeiro de Andrade & Antonio Miguel Vieira Monteiro & Gilberto Câmara & Sandra Sandri, 2009. "Games on Cellular Spaces: How Mobility Affects Equilibrium," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, pages 1-5.

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