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Multi-state choices with aggregate feedback on unfamiliar alternatives
Games and Economic Behavior ( IF 1.265 ) Pub Date : 2021-07-22 , DOI: 10.1016/j.geb.2021.07.007
Philippe Jehiel 1, 2 , Juni Singh 1
Affiliation  

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.



中文翻译:

多状态选择以及对不熟悉的替代方案的综合反馈

本文研究了一个多状态二元选择实验,其中在每个状态中,一个选择具有众所周知的后果,而另一个选择具有未知的后果。受试者反复从过去的选择中收到关于不熟悉的选择的后果的反馈,但这种反馈是在状态上汇总的。改变不同状态下各种备选方案的收益,使我们能够测试是否对不熟悉的备选方案进行了折扣,以及基于相似性的强化学习模型是否更好地解释了受试者对反馈的使用(本着估值均衡的精神,Jehiel 和 Samet, 2007)或贝叶斯学习模型的一些变体。

更新日期:2021-08-09
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