Multi-state choices with aggregate feedback on unfamiliar alternatives

Philippe Jehiel () and Juni Singh

Games and Economic Behavior, 2021, vol. 130, issue C, 1-24

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)
JEL-codes: C12 C91 D81 D83 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: 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:eee:gamebe:v:130:y:2021:i:c:p:1-24

DOI: 10.1016/j.geb.2021.07.007

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