Multi-state choices with aggregate feedback on unfamiliar alternatives

Philippe Jehiel () and Juni Singh
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Juni Singh: UCL - University College of London [London]

PSE-Ecole d'économie de Paris (Postprint) from HAL

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.

Keywords: Ambiguity; Bounded rationality; Experiment; Learning; Coarse feedback; Valuation equilibrium (search for similar items in EconPapers)
Date: 2021-11
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Citations: View citations in EconPapers (1)

Published in Games and Economic Behavior, 2021, 130, pp.1-24. ⟨10.1016/j.geb.2021.07.007⟩

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Journal Article: Multi-state choices with aggregate feedback on unfamiliar alternatives (2021)
Working Paper: Multi-state choices with aggregate feedback on unfamiliar alternatives (2021)
Working Paper: Multi-state choices with aggregate feedback on unfamiliar alternatives (2019)
Working Paper: Multi-state choices with aggregate feedback on unfamiliar alternatives (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:pseptp:halshs-03672197

DOI: 10.1016/j.geb.2021.07.007

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