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The Optimal Level of Generosity in a Selfish, Uncertain Environment

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  • Per Molander

Abstract

The tit-for-tat strategy in iterated prisoners' dilemma games is examined in the presence of disturbances. It is shown that performance deteriorates drastically at arbitrarily low noise levels. The situation may be remedied by introducing some unconditional generosity. This may be done without exposing oneself to the risk of exploitation by the other party.

Suggested Citation

  • Per Molander, 1985. "The Optimal Level of Generosity in a Selfish, Uncertain Environment," Journal of Conflict Resolution, Peace Science Society (International), vol. 29(4), pages 611-618, December.
    • Handle: RePEc:sae:jocore:v:29:y:1985:i:4:p:611-618
    DOI: 10.1177/0022002785029004004
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    Cited by:

    1. Matus Halas, 2018. "Balancing Against Threats In Interactions Determined By Distance And Overall Gains," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 1-22, August.
    2. repec:cup:judgdm:v:4:y:2009:i:5:p:363-384 is not listed on IDEAS
    3. Yoella Bereby-Meyer & Alvin E. Roth, 2006. "The Speed of Learning in Noisy Games: Partial Reinforcement and the Sustainability of Cooperation," American Economic Review, American Economic Association, vol. 96(4), pages 1029-1042, September.
    4. Matsushima Hitoshi, 2020. "Behavioral Theory of Repeated Prisoner’s Dilemma: Generous Tit-For-Tat Strategy," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 20(1), pages 1-11, January.
    5. Brams, Steven J. & Kilgour, D. Marc, 2012. "Inducible Games: Using Tit-for-Tat to Stabilize Outcomes," MPRA Paper 41773, University Library of Munich, Germany.
    6. Jack Hirshleifer & Juan Carlos Martinez Coll, 1992. "Selection, Mutation, and the Preservation of Diversity in Evolutionary Games," UCLA Economics Working Papers 648, UCLA Department of Economics.
    7. Ding, Hong & Zhang, Geng-shun & Wang, Shi-hao & Li, Juan & Wang, Zhen, 2019. "Q-learning boosts the evolution of cooperation in structured population by involving extortion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    8. Kayaba, Yutaka & Matsushima, Hitoshi & Toyama, Tomohisa, 2020. "Accuracy and retaliation in repeated games with imperfect private monitoring: Experiments," Games and Economic Behavior, Elsevier, vol. 120(C), pages 193-208.
    9. 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.
    10. Jonathan Bendor & Piotr Swistak, 1998. "Evolutionary Equilibria: Characterization Theorems and Their Implications," Theory and Decision, Springer, vol. 45(2), pages 99-159, October.
    11. Kristian Lindgren, 1996. "Evolutionary Dynamics in Game-Theoretic Models," Working Papers 96-06-043, Santa Fe Institute.
    12. Howard Kunreuther & Gabriel Silvasi & Eric T. Bradlow & Dylan Small, 2009. "Bayesian analysis of deterministic and stochastic prisoner's dilemma games," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 4(5), pages 363-384, August.
    13. Hitoshi Matsushima, 2019. "Behavioral Theory of Repeated Prisoner’s Dilemma: Generous Tit-For-Tat Strategy (Forthcoming in the B. E. Journal of Theoretical Economics)," CARF F-Series CARF-F-452, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    14. Anders Eriksson & Kristian Lindgren, 2001. "Evolution of Strategies in Repeated Stochastic Games," Working Papers 01-04-023, Santa Fe Institute.
    15. Yutaka Kayaba & Hitoshi Matsushima & Tomohisa Toyama, 2019. "Accuracy and Retaliation in Repeated Games with Imperfect Private Monitoring: Experiments (Revised version of CARF-F-433)," CARF F-Series CARF-F-466, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.

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