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An Extended Reinforcement Algorithm for Estimation of Human Behaviour in Congestion Games

  • Thorsten Chmura
  • Thomas Pitz

The paper reports simulations applied on two similar congestion games: the first is the classical minority game. The second one is a asymmetric variation of the minority game with linear payoff functions. For each game simulation results based on an extended reinforcement algorithm are compared with real experimental statistics. It is shown that the extension of the reinforcement model is essential for fitting the experimental data and estimating the players behaviour.

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File URL: http://www.wiwi.uni-bonn.de/bgsepapers/bonedp/bgse24_2004.pdf
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Paper provided by University of Bonn, Germany in its series Bonn Econ Discussion Papers with number bgse24_2004.

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Length: 19
Date of creation: Dec 2004
Date of revision:
Handle: RePEc:bon:bonedp:bgse24_2004
Contact details of provider: Postal: Bonn Graduate School of Economics, University of Bonn, Adenauerallee 24 - 26, 53113 Bonn, Germany
Fax: +49 228 73 6884
Web page: http://www.bgse.uni-bonn.de

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  1. A. Roth & I. Er’ev, 2010. "Learning in Extensive Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Run," Levine's Working Paper Archive 387, David K. Levine.
  2. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
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