Spiliopoulos, Leonidas (2008): Humans versus computer algorithms in repeated mixed strategy games.
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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 within-sub jects heterogeneity, but that there exists comparatively little between-subjects heterogeneity since players seemed to follow very similar strategies against each algorithm. Simple heuristics, such as win-stay/lose-shift, 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 win-stay/lose-shift 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|
|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
|Depositing User:||Leonidas Spiliopoulos|
|Date Deposited:||12. Jan 2008 06:01|
|Last Modified:||12. Feb 2013 12:21|
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