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Bayesian analysis of deterministic and stochastic prisoner's dilemma games

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  • Howard Kunreuther
  • Gabriel Silvasi
  • Eric T. Bradlow
  • Dylan Small
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    Abstract

    This paper compares the behavior of individuals playing a classic two-person deterministic prisoner's dilemma (PD) game with choice data obtained from repeated interdependent security prisoner's dilemma games with varying probabilities of loss and the ability to learn (or not learn) about the actions of one's counterpart, an area of recent interest in experimental economics. This novel data set, from a series of controlled laboratory experiments, is analyzed using Bayesian hierarchical methods, the first application of such methods in this research domain. We find that individuals are much more likely to be cooperative when payoffs are deterministic than when the outcomes are probabilistic. A key factor explaining this difference is that subjects in a stochastic PD game respond not just to what their counterparts did but also to whether or not they suffered a loss. These findings are interpreted in the context of behavioral theories of commitment, altruism and reciprocity. The work provides a linkage between Bayesian statistics, experimental economics, and consumer psychology.

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    Bibliographic Info

    Article provided by Society for Judgment and Decision Making in its journal Judgment and Decision Making.

    Volume (Year): 4 (2009)
    Issue (Month): 5 (August)
    Pages: 363-384

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    Handle: RePEc:jdm:journl:v:4:y:2009:i:5:p:363-384

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    Related research

    Keywords: prisoner's dilemma; uncertainty; bayesian analysis; controlled experiments.;

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    Cited by:
    1. Asim Ansari & Ricardo Montoya & Oded Netzer, 2012. "Dynamic learning in behavioral games: A hidden Markov mixture of experts approach," Quantitative Marketing and Economics, Springer, Springer, vol. 10(4), pages 475-503, December.
    2. Ozan Aksoy & Jeroen Weesie, 2013. "Hierarchical Bayesian Analysis of Biased Beliefs and Distributional Other-Regarding Preferences," Games, MDPI, Open Access Journal, MDPI, Open Access Journal, vol. 4(1), pages 66-88, February.

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