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Erscheinungsjahr: 
2010
Schriftenreihe/Nr.: 
Working Paper No. 560
Verlag: 
The Johns Hopkins University, Department of Economics, Baltimore, MD
Zusammenfassung: 
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.
Schlagwörter: 
Learning
experiments
eye-tracking
Bayesian vs. non-Bayesian learning
nonparametric estimation
JEL: 
D83
C91
C14
Dokumentart: 
Working Paper

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