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.
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Notes
- 1.
Neither EXP4 (and derivatives) nor meta-CMAB make explicit use of the base contexts to solve the problem.
- 2.
Code available at https://github.com/axelabels/CDM.
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Acknowledgment
This publication benefits from the support of the French Community of Belgium in the context of a FRIA grant, and by the FuturICT2.0 (www.futurict2.eu) project funded by the FLAG-ERA Joint Transnational call (JTC) 2016.
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Abels, A., Lenaerts, T., Trianni, V., Nowé, A. (2020). Collective Decision-Making as a Contextual Multi-armed Bandit Problem. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_9
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