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

Kazuhito Ogawa, Naoki Watanabe

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
編集者Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3295-3299
ページ数5
ISBN(電子版)9781665480451
DOI
出版ステータスPublished - 2022
外部発表はい
イベント2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan
継続期間: 2022 12月 172022 12月 20

出版物シリーズ

名前Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference2022 IEEE International Conference on Big Data, Big Data 2022
国/地域Japan
CityOsaka
Period22/12/1722/12/20

ASJC Scopus subject areas

  • モデリングとシミュレーション
  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 情報システムおよび情報管理
  • 安全性、リスク、信頼性、品質管理
  • 制御と最適化

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