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Title:Nonparametric learning rules from bandit experiments: The eyes have it! PDF Logo
Authors:Hu, Yingyao
Kayaba, Yutaka
Shum, Matt
Issue Date:2010
Series/Report no.:Working papers // the Johns Hopkins University, Department of Economics 560
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.
Subjects:Learning
experiments
eye-tracking
Bayesian vs. non-Bayesian learning
nonparametric estimation
JEL:D83
C91
C14
Document Type:Working Paper
Appears in Collections:Working Papers, Department of Economics, The Johns Hopkins University

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