Boundedly Rational Rule Learning in a Guessing Game
作者： Dale O. Stahl
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摘要: Abstract We combine Nagel's “step- k ” model of boundedly rational players with a “law effect” learning model. Players begin disposition to use one the step- rules behavior, and over time learn how available perform switch better performing rules. offer an econometric specification this dynamic process fit it experimental data. find that rule vastly outperforms other nested nonnested models. strong evidence for diverse dispositions reject Bayesian rule-learning Journal Economic Literature Classification Numbers: C70, C52, D83.
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