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Simulating Common Pool Resource Management Experiments with Adaptive Agents Employing Alternate Communication Routines

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This paper describes the development of a series of intelligent agent simulations based on data from previously documented common pool resource (CPR) experiments. These simulations are employed to examine the effects of different institutional configurations and individual behavioral characteristics on group level performance in a commons dilemma. Intelligent agents were created to represent the actions of individuals in a CPR experiment. The agents possess a collection of heuristics and utilize a form of adaptation by credit assignment in which they select the heuristic that appears to yield the highest return under the current circumstances. These simulations allow the analyst to specify the precise initial configuration of an institution and an individual's behavioral characteristics, so as to observe the interaction of the two and the group level outcomes that emerge as a result. Simulations explore settings in which there is no communication between agents, as well as the relative effects on overall group behavior of two different communication routines. The behavior of these simulations is compared with documented CPR experiments. Future directions in the development of the technology are outlined for natural resource management modeling applications.

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  • Peter Deadman & Edella Schlager & Randy Gimblett, 2000. "Simulating Common Pool Resource Management Experiments with Adaptive Agents Employing Alternate Communication Routines," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 3(2), pages 1-2.
    • Handle: RePEc:jas:jasssj:2000-2-1
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    Cited by:

    1. Giangiacomo Bravo, 2011. "Agents’ beliefs and the evolution of institutions for common-pool resource management," Rationality and Society, , vol. 23(1), pages 117-152, February.
    2. Lapp, Maya & Long, Colby, 2022. "A new approach to agent-based models of Community Resource Management based on the analysis of cheating, monitoring, and sanctioning," Ecological Modelling, Elsevier, vol. 468(C).
    3. Bustamante, Pedro & Gomez, Marcela & Murtazashvili, Ilia & Weiss, Martin, 2020. "Spectrum anarchy: why self-governance of the radio spectrum works better than we think," Journal of Institutional Economics, Cambridge University Press, vol. 16(6), pages 863-882, December.
    4. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    5. Rammel, Christian & Stagl, Sigrid & Wilfing, Harald, 2007. "Managing complex adaptive systems -- A co-evolutionary perspective on natural resource management," Ecological Economics, Elsevier, vol. 63(1), pages 9-21, June.
    6. Vallino, Elena, 2013. "The tragedy of the park: an agent-based model on endogenous and exogenous institutions for the management of a forest," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201316, University of Turin.
    7. Bertacchini, Enrico & Grazzini, Jakob & Vallino, Elena, 2013. "Emergence and Evolution of Property Rights: an Agent Based Perspective," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201340, University of Turin.

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