Nonparametric Learning Rules from Bandit Experiments: The Eyes have it!

Yingyao Hu, Yutaka Kayaba and Matthew Shum (mshum@caltech.edu)

Economics Working Paper Archive from The Johns Hopkins University,Department of Economics

Abstract: How do people learn? We assess, in a distribution-free manner, subjects?learning and choice rules in dynamic two-armed bandit (probabilistic reversal learning) experiments. To aid in identification and estimation, we use auxiliary measures of subjects?beliefs, in the form of their eye-movements during the experiment. Our estimated choice probabilities and learning rules have some distinctive features; notably that subjects tend to update in a non-smooth manner following choices made in accordance with current beliefs. Moreover, the beliefs implied by our nonparametric learning rules are closer to those from a (non-Bayesian) reinforcement learning model, than a Bayesian learning model.

Date: 2010-06
New Economics Papers: this item is included in nep-exp
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Citations: View citations in EconPapers (1)

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Journal Article: Nonparametric learning rules from bandit experiments: The eyes have it! (2013)
Working Paper: Nonparametric learning rules from bandit experiments: the eyes have it! (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:jhu:papers:560

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