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What Do Agent-Based and Equation-Based Modelling Tell Us About Social Conventions: The Clash Between ABM and EBM in a Congestion Game Framework

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Abstract

In this work simulation-based and analytical results on the emergence steady states in traffic-like interactions are presented and discussed. The objective of the paper is twofold: i) investigating the role of social conventions in coordination problem situations, and more specifically in congestion games; ii) comparing simulation-based and analytical results to figure out what these methodologies can tell us on the subject matter. Our main issue is that Agent-Based Modelling (ABM) and the Equation-Based Modelling (EBM) are not alternative, but in some circumstances complementary, and suggest some features distinguishing these two ways of modeling that go beyond the practical considerations provided by Parunak H.V.D., Robert Savit and Rick L. Riolo. Our model is based on the interaction of strategies of heterogeneous agents who have to cross a junction. In each junction there are only four inputs, each of which is passable only in the direction of the intersection and can be occupied only by an agent one at a time. The results generated by ABM simulations provide structured data for developing the analytical model through which generalizing the simulation results and make predictions. ABM simulations are artifacts that generate empirical data on the basis of the variables, properties, local rules and critical factors the modeler decides to implement into the model; in this way simulations allow generating controlled data, useful to test the theory and reduce the complexity, while EBM allows to close them, making thus possible to falsify them.

Suggested Citation

  • Federico Cecconi & Marco Campenni & Giulia Andrighetto & Rosaria Conte, 2010. "What Do Agent-Based and Equation-Based Modelling Tell Us About Social Conventions: The Clash Between ABM and EBM in a Congestion Game Framework," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(1), pages 1-6.
  • Handle: RePEc:jas:jasssj:2009-22-3
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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, vol. 23(1), pages 94-121, March.
    2. Matthew Oremland & Reinhard Laubenbacher, 2014. "Using difference equations to find optimal tax structures on the SugarScape," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 9(2), pages 233-253, October.
    3. Claudius Gräbner & Catherine S. E. Bale & Bernardo Alves Furtado & Brais Alvarez-Pereira & James E. Gentile & Heath Henderson & Francesca Lipari, 2019. "Getting the Best of Both Worlds? Developing Complementary Equation-Based and Agent-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 763-782, February.
    4. Geoffrey Hodgson & Kainan Huang, 2012. "Evolutionary game theory and evolutionary economics: are they different species?," Journal of Evolutionary Economics, Springer, vol. 22(2), pages 345-366, April.

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