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Learning by trial and error   总被引:2,自引:0,他引:2  
A person learns by trial and error if he occasionally tries out new strategies, rejecting choices that are erroneous in the sense that they do not lead to higher payoffs. In a game, however, strategies can become erroneous due to a change of behavior by someone else. We introduce a learning rule in which behavior is conditional on whether a player experiences an error of the first or second type. This rule, called interactive trial and error learning, implements Nash equilibrium behavior in any game with generic payoffs and at least one pure Nash equilibrium.  相似文献
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This paper uses the experimental method to investigate behavior in a coordination game when the information available to subjects is limited to their feasible choices and their experienced payoffs. In the experiment subjects converge to an absorbing state at rates that are orders of magnitude faster than reinforcement learning algorithms, but slower than under complete information. This state is very close to a mutual best response outcome. All cohorts converged to the market statistic predicted by the interior equilibrium regardless of the information conditions or the stability conditions. Eric Battalio programmed the graphical user interface. The National Science Foundation and Texas Advanced Research Program provided financial support. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the Texas Advanced Research Program.  相似文献
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