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Multi-state choices with aggregate feedback on unfamiliar alternatives

Jehiel, P; Singh, J; (2021) Multi-state choices with aggregate feedback on unfamiliar alternatives. Games and Economic Behavior , 130 pp. 1-24. 10.1016/j.geb.2021.07.007. Green open access

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Abstract

This paper studies a multi-state binary choice experiment in which in each state, one alternative has well understood consequences whereas the other alternative has unknown consequences. Subjects repeatedly receive feedback from past choices about the consequences of unfamiliar alternatives but this feedback is aggregated over states. Varying the payoffs attached to the various alternatives in various states allows us to test whether unfamiliar alternatives are discounted and whether subjects' use of feedback is better explained by similarity-based reinforcement learning models (in the spirit of the valuation equilibrium, Jehiel and Samet, 2007) or by some variant of Bayesian learning model. Our experimental data suggest that there is no discount attached to the unfamiliar alternatives and that similarity-based reinforcement learning models have a better explanatory power than their Bayesian counterparts.

Type: Article
Title: Multi-state choices with aggregate feedback on unfamiliar alternatives
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.geb.2021.07.007
Publisher version: https://doi.org/10.1016/j.geb.2021.07.007
Language: English
Additional information: © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10133688
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