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

  • Yingyao Hu
  • Yutaka Kayaba
  • Matt Shum

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.

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Paper provided by The Johns Hopkins University,Department of Economics in its series Economics Working Paper Archive with number 560.

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Date of creation: Jun 2010
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Handle: RePEc:jhu:papers:560
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