How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

1 Citation (Scopus)

Abstract

In collective decision-making (CDM) a group of experts with a shared set of values and a common goal must combine their knowledge to make a collectively optimal decision. Whereas existing research on CDM primarily focuses on making binary decisions, we focus here on CDM applied to solving contextual multi-armed bandit (CMAB) problems, where the goal is to exploit contextual information to select the best arm among a set. To address the limiting assumptions of prior work, we introduce confidence estimates and propose a novel approach to deciding with expert advice which can take advantage of these estimates. We further show that, when confidence estimates are imperfect, the proposed approach is more robust than the classical confidence-weighted majority vote.
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
Pages125-138
Number of pages14
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

Fingerprint Dive into the research topics of 'How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems'. Together they form a unique fingerprint.

Cite this