Spiliopoulos, Leonidas (2008): Humans versus computer algorithms in repeated mixed strategy games.

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Abstract
This paper is concerned with the modeling of strategic change in humans’ behavior when facing diﬀerent types of opponents. In order to implement this eﬃciently a mixed experimental setup was used where subjects played a game with a unique mixed strategy Nash equilibrium for 100 rounds against 3 preprogrammed computer algorithms (CAs) designed to exploit diﬀerent modes of play. In this context, substituting human opponents with computer algorithms designed to exploit commonly occurring human behavior increases the experimental control of the researcher allowing for more powerful statistical tests. The results indicate that subjects signiﬁcantly change their behavior conditional on the type of CA opponent, exhibiting withinsub jects heterogeneity, but that there exists comparatively little betweensubjects heterogeneity since players seemed to follow very similar strategies against each algorithm. Simple heuristics, such as winstay/loseshift, were found to model subjects and make out of sample predictions as well as, if not better than, more complicated models such as individually estimated EWA learning models which suﬀered from overﬁtting. Subjects modiﬁed their strategies in the direction of better response as calculated from CA simulations of various learning models, albeit not perfectly. Examples include the observation that subjects randomized more eﬀectively as the pattern recognition depth of the CAs increased, and the drastic reduction in the use of the winstay/loseshift heuristic when facing a CA designed to exploit this behavior.
Item Type:  MPRA Paper 

Institution:  University of Sydney 
Original Title:  Humans versus computer algorithms in repeated mixed strategy games 
Language:  English 
Keywords:  Behavioral game theory; Learning; Experimental economics; Simulations; Experience weighted attraction learning; Simulations; Repeated games; Mixed Strategy Nash equilibria; Economics and psychology 
Subjects:  C  Mathematical and Quantitative Methods > C9  Design of Experiments C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling C  Mathematical and Quantitative Methods > C7  Game Theory and Bargaining Theory > C70  General C  Mathematical and Quantitative Methods > C7  Game Theory and Bargaining Theory > C73  Stochastic and Dynamic Games ; Evolutionary Games ; Repeated Games C  Mathematical and Quantitative Methods > C7  Game Theory and Bargaining Theory > C72  Noncooperative Games C  Mathematical and Quantitative Methods > C9  Design of Experiments > C91  Laboratory, Individual Behavior 
Item ID:  6672 
Depositing User:  Leonidas Spiliopoulos 
Date Deposited:  12. Jan 2008 06:01 
Last Modified:  12. Feb 2013 12:21 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/6672 