Collective Decision-Making as a Contextual Multi-armed Bandit Problem

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Abstract

Collective decision-making (CDM) processes – wherein the knowledge of a group of individuals with a common goal must be combined to make optimal decisions – can be formalized within the framework of the deciding with expert advice setting. Traditional approaches to tackle this problem focus on finding appropriate weights for the individuals in the group. In contrast, we propose here meta-CMAB, a meta approach that learns a mapping from expert advice to expected outcomes. In summary, our work reveals that, when trying to make the best choice in a problem with multiple alternatives, meta-CMAB assures that the collective knowledge of experts leads to the best outcome without the need for accurate confidence estimates.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Science and Business Media Deutschland GmbH
Pages113-124
Number of pages12
ISBN (Print)9783030630065
DOIs
Publication statusPublished - Nov 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12496 LNAI

Keywords

  • Collective decision-making
  • Confidence
  • Contextual bandits
  • Deciding with expert advice
  • Noise

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