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Effective Choice in the Prisoner's Dilemma

Author

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  • Robert Axelrod

    (Institute of Public Policy Studies University of Michigan)

Abstract

This is a “primer†on how to play the iterated Prisoner's Dilemma game effectively. Existing research approaches offer the participant limited help in understanding how to cope effectively with such interactions. To gain a deeper understanding of how to be effective in such a partially competitive and partially cooperative environment, a computer tournament was conducted for the iterated Prisoner's Dilemma. Decision rules were submitted by entrants who were recruited primarily from experts in game theory from a variety of disciplines: psychology, political science, economics, sociology, and mathematics. The results of the tournament demonstrate that there are subtle reasons for an individualistic pragmatist to cooperate as long as the other side does, to be somewhat for-giving, and to be optimistic about the other side's responsiveness.

Suggested Citation

  • Robert Axelrod, 1980. "Effective Choice in the Prisoner's Dilemma," Journal of Conflict Resolution, Peace Science Society (International), vol. 24(1), pages 3-25, March.
    • Handle: RePEc:sae:jocore:v:24:y:1980:i:1:p:3-25
    DOI: 10.1177/002200278002400101
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    References listed on IDEAS

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    4. Pedro Ribeiro de Andrade & Antonio Miguel Vieira Monteiro & Gilberto Câmara & Sandra Sandri, 2009. "Games on Cellular Spaces: How Mobility Affects Equilibrium," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-5.
    5. Tarik Tazdaït & Alejandro Caparros & Jean-Chrsitophe Péreau, 2008. "Mutual Aid: An Indirect Evolution Analysis," Working Papers halshs-00275386, HAL.
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    7. Jager, W. & Janssen, M. A. & De Vries, H. J. M. & De Greef, J. & Vlek, C. A. J., 2000. "Behaviour in commons dilemmas: Homo economicus and Homo psychologicus in an ecological-economic model," Ecological Economics, Elsevier, vol. 35(3), pages 357-379, December.
    8. Lawrence C. Reardon, 2011. "Ideational Learning and the Paradox of Chinese Catholic Reconciliation," Journal of Current Chinese Affairs - China aktuell, Institute of Asian Studies, GIGA German Institute of Global and Area Studies, Hamburg, vol. 40(2), pages 43-70.
    9. Travers, Henry & Clements, Tom & Keane, Aidan & Milner-Gulland, E.J., 2011. "Incentives for cooperation: The effects of institutional controls on common pool resource extraction in Cambodia," Ecological Economics, Elsevier, vol. 71(C), pages 151-161.
    10. Romero, Julian & Rosokha, Yaroslav, 2018. "Constructing strategies in the indefinitely repeated prisoner’s dilemma game," European Economic Review, Elsevier, vol. 104(C), pages 185-219.
    11. Lucas Kruitwagen & Kaveh Madani & Ben Caldecott & Mark H. W. Workman, 2017. "Game theory and corporate governance: conditions for effective stewardship of companies exposed to climate change risks," Journal of Sustainable Finance & Investment, Taylor & Francis Journals, vol. 7(1), pages 14-36, January.
    12. Jager, Wander & Janssen, Marco A., 2001. "Diffusion processes in demographic transitions: a prospect on using multi agent simulation to explore the role of cognitive strategies and social interactions," Research Report 01B40, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    13. Todd Guilfoos & Andreas Pape, 2016. "Predicting human cooperation in the Prisoner’s Dilemma using case-based decision theory," Theory and Decision, Springer, vol. 80(1), pages 1-32, January.
    14. Dridi, Slimane & Lehmann, Laurent, 2014. "On learning dynamics underlying the evolution of learning rules," Theoretical Population Biology, Elsevier, vol. 91(C), pages 20-36.
    15. Alejandro Lee-Penagos, 2016. "Learning to Coordinate: Co-Evolution and Correlated Equilibrium," Discussion Papers 2016-11, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
    16. Schimit, P.H.T., 2016. "Evolutionary aspects of spatial Prisoner’s Dilemma in a population modeled by continuous probabilistic cellular automata and genetic algorithm," Applied Mathematics and Computation, Elsevier, vol. 290(C), pages 178-188.
    17. Zhang, Huanren, 2018. "Errors can increase cooperation in finite populations," Games and Economic Behavior, Elsevier, vol. 107(C), pages 203-219.
    18. Jui-Chung Allen Li & Yeh-Chen Chen, 2012. "Personality, Affects, and Forgiving Behavior in Games," Working Papers WR-952, RAND Corporation.
    19. Murnighan, J. Keith & Wang, Long, 2016. "The social world as an experimental game," Organizational Behavior and Human Decision Processes, Elsevier, vol. 136(C), pages 80-94.
    20. Menezes, Flavio Marques, 1992. "Repeated Games Played by Finite Automata: A Survey," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 12(1), April.
    21. Etro, Federico, 2017. "Research in economics and game theory. A 70th anniversary," Research in Economics, Elsevier, vol. 71(1), pages 1-7.

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