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Boundedly Rational Rule Learning in a Guessing Game

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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-krules 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 LiteratureClassification Numbers: C70, C52, D83.

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Partial funding of this research was provided by grant SBR-9308914 from the National Science Foundation. The author thanks Rosemarie Nagel for permission to use her experimental data for this study and for many stimulating conversations, Xioahua Lu for substantial programming and research assistance, and Paul Wilson for statistical consultation. The author is also indebted to Daniel Friedman for many helpful suggestions on earlier drafts. All errors and omissions are the author's sole responsibility. E-mail: [email protected].

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