Evolutionary Learning in the Ultimatum Game
AbstractThe 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.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 91.
Date of creation: 01 Apr 2001
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Web page: http://www.econometricsociety.org/conference/SCE2001/SCE2001.html
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ultimatum game; evolutionary dynamics; evolutionary algorithms;
Find related papers by 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, and Information
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- Riechmann, Thomas, 2001. "Two Notes on Replication in Evolutionary Modelling," Diskussionspapiere der Wirtschaftswissenschaftlichen FakultÃÂ¤t der Leibniz UniversitÃÂ¤t Hannover dp-239, Leibniz UniversitÃ¤t Hannover, Wirtschaftswissenschaftliche FakultÃ¤t.
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