 Nonparametric learning rules from bandit experiments: The eyes have it!Yingyao Hu, Yutaka Kayaba and Matthew Shum () Games and Economic Behavior, 2013, vol. 81, issue C, 215-231 Abstract: How do people learn? We assess, in a model-free manner, subjectsʼ belief dynamics in a two-armed bandit learning experiment. A novel feature of our approach is to supplement the choice and reward data with subjectsʼ eye movements during the experiment to pin down estimates of subjectsʼ beliefs. Estimates show that subjects are more reluctant to “update down” following unsuccessful choices, than “update up” following successful choices. The profits from following the estimated learning and decision rules are smaller (by about 25% of average earnings by subjects in this experiment) than what would be obtained from a fully-rational Bayesian learning model, but comparable to the profits from alternative non-Bayesian learning models, including reinforcement learning and a simple “win-stay” choice heuristic. Keywords: Learning; Belief dynamics; Experiments; Eye tracking; Bayesian vs. non-Bayesian learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:81:y:2013:i:c:p:215-231 DOI: 10.1016/j.geb.2013.05.003 Access Statistics for this article Games and Economic Behavior is currently edited by E. Kalai More articles in Games and Economic Behavior from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.