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An Agent-Based Model to Explore Game Setting Effects on Attitude Change During a Role Playing Game Session

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Abstract

Role playing games (RPGs) can be used as participatory simulation methods for environmental management. However, researchers in the field need to be aware of the influence of the game settings on participants' behavioural patterns and attitudes, before fine tuning the design and use of their games. We developed an agent-based model (CauxAttitude) to assess the framing induced by the conditions of implementation of a specific game, named CauxOpération, on possible changes in participants' attitudes. We designed CauxAttitude on the basis of social psychology theories that describe relations between attitudes and behaviours, as well as on observations of CauxOpération sessions. In this paper, we describe how the model behaved according to variations in the initialization of the parameters, our aim being to explore the effects of subjective choices concerning model design and implementation. The results of our simulations enabled us to identify effects of game settings we explored, including the choice of the population of participants or of the number of participants made by the game designer. Our results also revealed the underlying mechanisms that explain the effects of game settings. These provide clues to the game designer on how to manage them.

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  • Emmanuel Dubois & Olivier Barreteau & Véronique Souchère, 2013. "An Agent-Based Model to Explore Game Setting Effects on Attitude Change During a Role Playing Game Session," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(1), pages 1-2.
  • Handle: RePEc:jas:jasssj:2011-88-2
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    File URL: http://jasss.soc.surrey.ac.uk/16/1/2/2.pdf
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    1. Matteo Richiardi & Roberto Leombruni & Nicole J. Saam & Michele Sonnessa, 2006. "A Common Protocol for Agent-Based Social Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, pages 1-15.
    2. Ulrich Frank & Klaus G. Troitzsch, 2005. "Epistemological Perspectives on Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-7.
    3. Janssen, Marco & de Vries, Bert, 1998. "The battle of perspectives: a multi-agent model with adaptive responses to climate change," Ecological Economics, Elsevier, vol. 26(1), pages 43-65, July.
    4. Hanneman & Anthony Patrick, 1997. "On the Uses of Computer-Assisted Simulation Modeling in the Social Sciences," Sociological Research Online, Sociological Research Online, vol. 2(2), pages 1-5.
    5. Karolina Safarzyńska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, pages 329-373.
    6. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 195-226, October.
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    Cited by:

    1. Davide Secchi & Raffaello Seri, 2017. "Controlling for false negatives in agent-based models: a review of power analysis in organizational research," Computational and Mathematical Organization Theory, Springer, pages 94-121.

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