Boundedly rational rule learning in a guessing game
Boundedly rational rule learning in a guessing game
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猜谜游戏中的有限理性规则学习
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发表时间：
1996
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通讯作者：
D. Stahl
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作者：
D. Stahl
摘要我们将联合收割机内格尔的有限理性参与者的“step- k“模型与“效果律”学习模型相结合。参与者开始倾向于使用第k步行为规则之一，随着时间的推移，参与者学习可用规则如何执行并切换到执行更好的规则。我们提供了这个动态过程的计量经济学规范，并适合内格尔的实验数据。我们发现，规则学习模型大大优于其他嵌套和非嵌套学习模型。我们发现了强有力的证据，不同的处置和拒绝贝叶斯规则学习模型。经济文献分类号：C70、C52、D83。
Abstract We combine Nagel's “step- k ” model of boundedly rational players with a “law of effect” learning model. Players begin with a disposition to use one of the step- k rules of behavior, and over time the players learn how the available rules perform and switch to better performing rules. We offer an econometric specification of this dynamic process and fit it to Nagel's experimental data. We find that the rule of learning model vastly outperforms other nested and nonnested learning models. We find strong evidence for diverse dispositions and reject the Bayesian rule-learning model. Journal of Economic Literature Classification Numbers: C70, C52, D83.