Multi-state choices with aggregate feedback on unfamiliar alternatives

• 成果类型: Article
• 署名作者: Jehiel, Philippe; Singh, Juni
• 署名单位: Paris School of Economics; University of London; University College London
• 刊物名称: GAMES AND ECONOMIC BEHAVIOR
• ISSN/ISSBN: 0899-8256
• DOI: 10.1016/j.geb.2021.07.007
• 发表日期: 2021
• 页码: 1-24
• 关键词: Ambiguity; bounded rationality; experiment; learning; Coarse feedback; Valuation equilibrium

摘要

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. (C) 2021 The Author(s). Published by Elsevier Inc.