Boundedly Rational Rule Learning in a Guessing Game

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. © 1996 Academic Press, Inc.