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Regret-based budgeted decision rules under severe uncertainty

Nakharutai, Nawapon; Destercke, Sébastien; Troffaes, Matthias C. M.

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Authors

Nawapon Nakharutai

Sébastien Destercke



Abstract

One way to make decisions under uncertainty is to select an optimal option from a possible range of options, by maximizing the expected utilities derived from a probability model. However, under severe uncertainty, identifying precise probabilities is hard. For this reason, imprecise probability models uncertainty through convex sets of probabilities, and considers decision rules that can return multiple options to reflect insufficient information. Many well-founded decision rules have been studied in the past, but none of those standard rules are able to control the number of returned alternatives. This can be a problem for large decision problems, due to the cognitive burden decision makers have to face when presented with a large number of alternatives. Our contribution proposes regret-based ideas to construct new decision rules which return a bounded number of options, where the limit on the number of options is set in advance by the decision maker as an expression of their cognitive limitation. We also study their consistency and numerical behaviour.

Citation

Nakharutai, N., Destercke, S., & Troffaes, M. C. M. (2024). Regret-based budgeted decision rules under severe uncertainty. Information Sciences, 665, Article 120361. https://doi.org/10.1016/j.ins.2024.120361

Journal Article Type Article
Acceptance Date Feb 21, 2024
Online Publication Date Feb 27, 2024
Publication Date 2024-04
Deposit Date Mar 1, 2024
Publicly Available Date Mar 6, 2024
Journal Information Sciences
Print ISSN 0020-0255
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 665
Article Number 120361
DOI https://doi.org/10.1016/j.ins.2024.120361
Public URL https://durham-repository.worktribe.com/output/2292046

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