In this paper we introduce four new learning models: impulse balance learning, impulse matching learning, action-sampling learning, and payoff-sampling learning. With this models and together with the models of self- tuning EWA learning and reinforcement learning, we conduct simulations over 12 different 2×2 games and compare the results with experimental data obtained by Selten & Chmura (2008). Our results are two-fold: While the simulations, especially those with action-sampling learning and impulse matching learning successfully replicate the experimental data on the aggregate, they fail in describing the individual behavior. A simple inertia rule beats the learning models in describing individuals behavior.
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Paper provided by University of Bonn, Germany in its series Bonn Econ Discussion Papers with number
bgse18_2008.
Length: 34 Date of creation: Dec 2008 Date of revision: Handle: RePEc:bon:bonedp:bgse18_2008
Contact details of provider: Postal: Bonn Graduate School of Economics, University of Bonn, Adenauerallee 24 - 26, 53113 Bonn, Germany Fax: +49 228 73 9221 Web page: http://www.bgse.uni-bonn.de/index.php?id=494
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Find related papers by JEL classification: C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
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