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

Axel Abels; Tom Lenaerts; Vito Trianni; Ann Nowé

doi:10.1007/978-3-030-63007-2_9

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

Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

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 language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	113-124
Number of pages	12
ISBN (Print)	9783030630065
DOIs	https://doi.org/10.1007/978-3-030-63007-2_9
Publication status	Published - Nov 2020
Event	International Conference on Computational Collective Intelligence - Da Nang, Viet Nam Duration: 30 Nov 2020 → 3 Dec 2020 Conference number: 12 https://iccci.pwr.edu.pl/2020/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12496 LNAI

Conference

Conference	International Conference on Computational Collective Intelligence
Abbreviated title	ICCCI
Country/Territory	Viet Nam
City	Da Nang
Period	30/11/20 → 3/12/20
Internet address	https://iccci.pwr.edu.pl/2020/

Keywords

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

Access to Document

10.1007/978-3-030-63007-2_9

Cite this

Abels, A., Lenaerts, T., Trianni, V., & Nowé, A. (2020). Collective Decision-Making as a Contextual Multi-armed Bandit Problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 113-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12496 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63007-2_9

Abels, Axel ; Lenaerts, Tom ; Trianni, Vito et al. / Collective Decision-Making as a Contextual Multi-armed Bandit Problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, 2020. pp. 113-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Abels, A , Lenaerts, T, Trianni, V & Nowé, A 2020, Collective Decision-Making as a Contextual Multi-armed Bandit Problem. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12496 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 113-124, International Conference on Computational Collective Intelligence, Da Nang, Viet Nam, 30/11/20. https://doi.org/10.1007/978-3-030-63007-2_9

Collective Decision-Making as a Contextual Multi-armed Bandit Problem. / Abels, Axel ; Lenaerts, Tom; Trianni, Vito et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, 2020. p. 113-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12496 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

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AU - Abels, Axel

AU - Lenaerts, Tom

AU - Trianni, Vito

AU - Nowé, Ann

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PY - 2020/11

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AB - 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.

KW - Collective decision-making

KW - Confidence

KW - Contextual bandits

KW - Deciding with expert advice

KW - Noise

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Science and Business Media Deutschland GmbH

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Y2 - 30 November 2020 through 3 December 2020

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Abels A , Lenaerts T, Trianni V, Nowé A. Collective Decision-Making as a Contextual Multi-armed Bandit Problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH. 2020. p. 113-124. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-63007-2_9

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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

VLAAI1: Flanders Artificial Intelligence Research program (FAIR) – second cycle

EUAR39: H2020: TAILOR : Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization

Cite this

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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

VLAAI1: Flanders Artificial Intelligence Research program (FAIR) – second cycle

EUAR39: H2020: TAILOR : Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization

Cite this