An Extended Reinforcement Algorithm for Estimation of Human Behaviour in Congestion Games
AbstractThe 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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Bibliographic InfoPaper provided by University of Bonn, Germany in its series Bonn Econ Discussion Papers with number bgse24_2004.
Date of creation: Dec 2004
Date of revision:
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congestion game; minority game; laboratory experiments; reinforcement algorithm; payoff sum model;
Find related papers by JEL classification:
- C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
- C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-01-16 (All new papers)
- NEP-CBE-2005-01-16 (Cognitive & Behavioural Economics)
- NEP-EVO-2005-01-16 (Evolutionary Economics)
- NEP-EXP-2005-01-16 (Experimental Economics)
- NEP-GTH-2005-01-16 (Game Theory)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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