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Agent-Based Simulation of the Trust and Tracing Game for Supply Chains and Networks

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Abstract

This paper describes a multi-agent simulation model of the Trust And Tracing game. The Trust And Tracing game is a gaming simulation for human players, developed as a research tool for data collection on human behaviour in food supply chains with asymmetric information about food quality and food safety. Important issues in the game are opportunistic behaviour (deceit), trust and institutional arrangements for enforcing compliance. The goal is to improve the understanding of human decision making with respect to these issues. To this end multi-agent simulation can be applied to simulate the effect of models of individual decision making in partner selection, negotiation, deceit and trust on system behaviour. The combination of human gaming simulation and multi-agent simulation offers a basis for model refinement in a cycle of validation, experimentation, and formulation of new hypotheses. This paper describes a first round of model formulation and validation. The models presented are validated by a series of experiments performed by the implemented simulation system, of which the outcomes are compared on aggregated level to the outcomes of games played by humans. The experiments cover in a systematic way the important variations in parameter settings possible in the game and in the characteristics of the agents. The simulation results show the same tendencies of behaviour as the observed human games.

Suggested Citation

  • Dmytro Tykhonov & Catholijn Jonker & Sebastiaan Meijer & Tim Verwaart, 2008. "Agent-Based Simulation of the Trust and Tracing Game for Supply Chains and Networks," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(3), pages 1-1.
  • Handle: RePEc:jas:jasssj:2007-64-4
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    1. Philippe Mathieu & Bruno Beaufils & Olivier Brandouy, 2005. "Artificial Economics," Post-Print hal-00826572, HAL.
    2. Hanemann, W Michael, 1991. "Willingness to Pay and Willingness to Accept: How Much Can They Differ?," American Economic Review, American Economic Association, vol. 81(3), pages 635-647, June.
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    Cited by:

    1. Lucio Biggiero & Enrico Sevi, 2009. "Opportunism by cheating and its effects on industry profitability. The CIOPS model," Computational and Mathematical Organization Theory, Springer, vol. 15(3), pages 191-236, September.
    2. Utomo, Dhanan Sarwo & Onggo, Bhakti Stephan & Eldridge, Stephen, 2018. "Applications of agent-based modelling and simulation in the agri-food supply chains," European Journal of Operational Research, Elsevier, vol. 269(3), pages 794-805.
    3. Doloswala, Kalika Navin, 2014. "Eroding trust – An agent based model to explore how trust flows," Australasian marketing journal, Elsevier, vol. 22(1), pages 51-53.
    4. Irz, Xavier & Mazzocchi, Mario & Réquillart, Vincent & Soler, Louis-Georges, 2015. "Research in Food Economics: past trends and new challenges," Revue d'Etudes en Agriculture et Environnement, Editions NecPlus, vol. 96(01), pages 187-237, March.
    5. repec:eur:ejfejr:7 is not listed on IDEAS
    6. Gert Jan Hofstede & Catholijn M. Jonker & Tim Verwaart, 2012. "Cultural Differentiation of Negotiating Agents," Group Decision and Negotiation, Springer, vol. 21(1), pages 79-98, January.
    7. Bruce Edmonds, 2012. "Context in social simulation: why it can’t be wished away," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 5-21, March.
    8. Whan-Seon Kim, 2009. "Effects of a Trust Mechanism on Complex Adaptive Supply Networks: An Agent-Based Social Simulation Study," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(3), pages 1-4.
    9. Shu-Heng Chen & Bin-Tzong Chie & Tong Zhang, 2015. "Network-Based Trust Games: An Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(3), pages 1-5.
    10. Sara McPhee-Knowles, 2015. "Growing Food Safety from the Bottom Up: An Agent-Based Model of Food Safety Inspections," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-9.

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