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Comparative Analysis of Agent-Based Social Simulations: GeoSim and FEARLUS Models

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In this paper we compare models of two different kinds of processes in multi-agent-based social simulations (MABSS): military conflict within a states-system (GeoSim), and land use and ownership change (FEARLUS). This is a kind of model-to-model comparison which is novel within Multi-Agent Based Simulation research, although well-known within mathematics, physics and biology: comparing objects (in this case MABSS) drawn from distinct research domains, in order to draw out their structural similarities and differences. This can facilitate research in both domains, by allowing the use of findings from each to illuminate the other. Based on the similarities between FEARLUS and GeoSim, we conclude by identifying a new class of MABSS models based on territorial resource allocation processes occurring on a 2-dimensional space (which we define as the “TRAP2†class). The existence of the cross-domain TRAP2 class of models in turn suggests that MABSS researchers should look for other members of the class, sharing some of the properties or dynamics common to the GeoSim and FEARLUS models compared in this study: a systematic comparison of a set of related models from a range of apparently distinct domains should generate insights into both MABSS modeling, and the domains concerned.

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  • Claudio Cioffi-Revilla & Nicholas M. Gotts, 2003. "Comparative Analysis of Agent-Based Social Simulations: GeoSim and FEARLUS Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 6(4), pages 1-10.
  • Handle: RePEc:jas:jasssj:2003-31-1
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    File URL: http://jasss.soc.surrey.ac.uk/6/4/10.html
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    Citations

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    Cited by:

    1. Uri Wilensky & William Rand, 2007. "Making Models Match: Replicating an Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(4), pages 1-2.
    2. Riccardo Boero & Flaminio Squazzoni, 2005. "Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-6.
    3. Juliette Rouchier & Emily Tanimura, 2016. "Learning with Communication Barriers Due to Overconfidence. What a "Model-To-Model Analysis" Can Add to the Understanding of a Problem," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(2), pages 1-7.
    4. J. Gareth Polhill & Dawn C. Parker & Daniel Brown & Volker Grimm, 2008. "Using the ODD Protocol for Describing Three Agent-Based Social Simulation Models of Land-Use Change," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(2), pages 1-3.
    5. Juliette Rouchier & Claudio Cioffi-Revilla & J. Gareth Polhill & Keiki Takadama, 2008. "Progress in Model-To-Model Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(2), pages 1-8.
    6. Martin Neumann, 2007. "Complexity of social stability: a model-to-model analysis of Yugoslavia's decline," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 5(2), pages 92-111.

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