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.
Abels, A, Lenaerts, T, Trianni, V & Nowé, A 2020, How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems. 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. 125-138, International Conference on Computational Collective Intelligence, Da Nang, Viet Nam, 30/11/20. https://doi.org/10.1007/978-3-030-63007-2_10
Abels, A., Lenaerts, T., Trianni, V., & Nowé, A. (2020). How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 125-138). (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_10
@inproceedings{2ad548e49da048ef8e75c1cce3e3320d,
title = "How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems",
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.",
keywords = "Collective decision-making, Confidence, Contextual bandits, Deciding with expert advice, Noise",
author = "Axel Abels and Tom Lenaerts and Vito Trianni and Ann Now{\'e}",
year = "2020",
month = nov,
doi = "10.1007/978-3-030-63007-2_10",
language = "English",
isbn = "9783030630065",
series = "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 = "125--138",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
note = "International Conference on Computational Collective Intelligence, ICCCI ; Conference date: 30-11-2020 Through 03-12-2020",
url = "https://iccci.pwr.edu.pl/2020/",
}