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

Hu, Yingyao and Kayaba, Yutaka and Shum, Matthew (2010) Nonparametric Learning Rules from Bandit Experiments: The Eyes have it! Social Science Working Paper, 1326. California Institute of Technology , Pasadena, CA. (Unpublished)

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How do people learn? We assess, in a distribution-free manner, subjects’ learning and choice rules in dynamic two-armed bandit 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.

Item Type:Report or Paper (Working Paper)
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URLURL TypeDescription ItemPublished Version
Shum, Matthew0000-0002-6262-915X
Additional Information:We are indebted to Antonio Rangel for his encouragement and for the funding and use of facilities in his lab. We thank Dan Ackerberg, Peter Bossaerts, Colin Camerer, Andrew Ching, Cary Frydman, Ian Krajbich, Pietro Ortoleva, and participants in presentations at U. Arizona, Caltech, UCLA, U. Washington and Choice Symposium 2010 (Key Largo) for comments and suggestions. Published in Games and Economic Behavior, 81, 215-231.
Group:Social Science Working Papers
Subject Keywords:learning, experiments, eye-tracking, Bayesian vs. non-Bayesian learning, nonparametric estimation
Series Name:Social Science Working Paper
Issue or Number:1326
Classification Code:JEL: D83, C91, C14
Record Number:CaltechAUTHORS:20170726-145343662
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:79442
Deposited By: Jacquelyn Bussone
Deposited On:07 Aug 2017 21:49
Last Modified:03 Oct 2019 18:20

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