Nonparametric Learning Rules from Bandit Experiments: The Eyes have it!
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.
Year of publication: |
2010-06
|
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Authors: | Hu, Yingyao ; Kayaba, Yutaka ; Shum, Matt |
Institutions: | Department of Economics, Johns Hopkins University |
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