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Feedback Information on Cumulative Payoff in a Bandit Experiment: Meaningful Learning in Weighted Voting | IEEE Conference Publication | IEEE Xplore

Feedback Information on Cumulative Payoff in a Bandit Experiment: Meaningful Learning in Weighted Voting


Abstract:

In a two-armed bandit experiment with the contextual information on weighted voting, we investigated whether subjects who had experienced a binary choice problem for many...Show More

Abstract:

In a two-armed bandit experiment with the contextual information on weighted voting, we investigated whether subjects who had experienced a binary choice problem for many periods increased the number of choosing the answer which would give a higher expected payoff when they were faced with a similar but different binary choice problem in the subsequent periods (or meaningfully learned the correct answer). Receiving both cumulative payoff and current payoffs as the feedback information, subjects learned the correct answers of three binary choice problems we examined, but for any binary choice problem they did not meaningfully learn it from their experience in a similar but different one. Compared with the previous study where subjects received only current payoffs as the feedback information, the additional feedback information on cumulative payoff might induce subjects to learn the correct answers but would not promote their meaningful learning of the latent feature of the contextual information in this experiment.
Date of Conference: 17-20 December 2022
Date Added to IEEE Xplore: 26 January 2023
ISBN Information:
Conference Location: Osaka, Japan

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