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

Kazuhito Ogawa, Naoki Watanabe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
EditorsShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3295-3299
Number of pages5
ISBN (Electronic)9781665480451
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan
Duration: 2022 Dec 172022 Dec 20

Publication series

NameProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference2022 IEEE International Conference on Big Data, Big Data 2022
Country/TerritoryJapan
CityOsaka
Period22/12/1722/12/20

Keywords

  • bandit experiment
  • feedback information
  • latent feature
  • meaningful learning
  • weighted voting

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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