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A Spatial Agent-Based Model of N-Person Prisoner's Dilemma Cooperation in a Socio-Geographic Community

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Abstract

The purpose of this paper is to present a spatial agent-based model of N-person prisoner's dilemma that is designed to simulate the collective communication and cooperation within a socio-geographic community. Based on a tight coupling of REPAST and a vector Geographic Information System, the model simulates the emergence of cooperation from the mobility behaviors and interaction strategies of citizen agents. To approximate human behavior, the agents are set as stochastic learning automata with Pavlovian personalities and attitudes. A review of the theory of the standard prisoner's dilemma, the iterated prisoner's dilemma, and the N-person prisoner's dilemma is given as well as an overview of the generic architecture of the agent-based model. The capabilities of the spatial N-person prisoner's dilemma component are demonstrated with several scenario simulation runs for varied initial cooperation percentages and mobility dynamics. Experimental results revealed that agent mobility and context preservation bring qualitatively different effects to the evolution of cooperative behavior in an analyzed spatial environment.

Suggested Citation

  • Conrad Power, 2009. "A Spatial Agent-Based Model of N-Person Prisoner's Dilemma Cooperation in a Socio-Geographic Community," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-8.
    • Handle: RePEc:jas:jasssj:2008-30-2
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    1. Rezaei, Golriz & Kirley, Michael, 2012. "Dynamic social networks facilitate cooperation in the N-player Prisoner’s Dilemma," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6199-6211.
    2. Sutee Anantsuksomsri & Nij Tontisirin, 2016. "A spatial agent-based model of a congestion game: evolutionary game theory in space," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 57(2), pages 371-391, November.
    3. Marcel Ausloos & Herbert Dawid & Ugo Merlone, 2015. "Spatial Interactions in Agent-Based Modeling," Dynamic Modeling and Econometrics in Economics and Finance, in: Pasquale Commendatore & Saime Kayam & Ingrid Kubin (ed.), Complexity and Geographical Economics, edition 127, pages 353-377, Springer.
    4. Aloysius Suratin & Suyud Warno Utomo & Dwi Nowo Martono & Kosuke Mizuno, 2023. "Indonesia’s Renewable Natural Resource Management in the Low-Carbon Transition: A Conundrum in Changing Trajectories," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    5. Benjamin Patrick Evans & Kirill Glavatskiy & Michael S. Harré & Mikhail Prokopenko, 2023. "The impact of social influence in Australian real estate: market forecasting with a spatial agent-based model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(1), pages 5-57, January.
    6. Olszewski, Robert & Pałka, Piotr & Wendland, Agnieszka & Majdzińska, Karolina, 2021. "Application of cooperative game theory in a spatial context: An example of the application of the community-led local development instrument for the decision support system of biogas plants constructi," Land Use Policy, Elsevier, vol. 108(C).
    7. Peng Lu & Xiaoping Zheng, 2015. "Social Stratification and Cooperative Behavior in Spatial Prisoners' Dilemma Games," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-16, July.

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