经济Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria

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Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria

文献名称	Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria
文献作者	Erev, Ido Roth, Alvin E
作者所在单位	Harvard University
文献分类	已发表文献
学科一级分类	经济
学科二级分类	行为经济学
文献摘要	The authors examine learning in all experiments they could locate involving one hundred periods or more of games with a unique equilibrium in mixed strategies, and in a new experiment. They study both the ex post ('best fit') descriptive power of learning models, and their ex ante predictive power, by simulating each experiment using parameters estimated from the other experiments. Even a one-parameter reinforcement learning model robustly outperforms the equilibrium predictions. Predictive power is improved by adding 'forgetting' and 'experimentation,' or by allowing greater rationality as in probabilistic fictitious play. Implications for developing a low-rationality, cognitive game theory are discussed. Copyright 1998 by American Economic Association.
参考文献
关键字	game theory
发表所在刊物（或来源）	The American Economic Review Vol. 88, No. 4 (Sep., 1998), pp. 848-881
发表时间	1998-09-01
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