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Evolutionary Learning in the Ultimatum Game

Author

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  • Thomas Riechmann

Abstract

The ultimatum game is (in)famous for its `anomalies': The outcomes of laboratory experiments are very different from the results generated by traditional game theory. This paper aims to find to what extent these discrepancies between theory and experiments can be explained by the effects of bounded rationality and learning dynamics. These are modeled by several agent based models and computer simulations of evolutionary learning by pure imitation as well as imitation and experiments. The main result of the analysis is surprisingly clear and robust: Proposers do not play a subgame perfect strategy but instead `learn' to make offers of about 20 to 25 % of the total amount to their opponents.

Suggested Citation

  • Thomas Riechmann, 2001. "Evolutionary Learning in the Ultimatum Game," Computing in Economics and Finance 2001 91, Society for Computational Economics.
    • Handle: RePEc:sce:scecf1:91
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    Cited by:

    1. Riechmann, Thomas, 2001. "Two Notes on Replication in Evolutionary Modelling," Hannover Economic Papers (HEP) dp-239, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

    More about this item

    Keywords

    ultimatum game; evolutionary dynamics; evolutionary algorithms;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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