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  4. Minimax decision rules for planning under uncertainty: drawbacks and remedies
 
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Minimax decision rules for planning under uncertainty: drawbacks and remedies
File(s)
1-s2.0-S0377221723004095-main (1).pdf (1.19 MB)
Published version
Author(s)
Anderson, Edward
Zachary, Stan
Type
Journal Article
Abstract
It is common to use minimax rules to make planning decisions when there is great uncertainty about what may happen in the future. Using minimax rules avoids the need to determine probabilities for each future scenario, which is an attractive feature in many public sector settings. However there are potential problems in the application of a minimax approach. In this paper our aim is to give guidance for planners considering a minimax approach, including minimax regret which is one popular version of this. We give an analysis of the behaviour of minimax rules in the case with a finite set of possible future scenarios. Minimax rules will have sensitivity to the choice of a small number of scenarios. When regret-based rules are used there are also problems arising since the independence of irrelevant alternatives property fails, which can lead to opportunities to game the process. We analyse these phenomena considering cases where the decision variables are chosen from a convex set in Rⁿ, as well as cases with a finite set of decision choices. We show that the drawbacks of minimax regret hold even when restrictions are placed on the problem setup, and we show how working with a structured set of scenarios can ameliorate the difficulty of having a final decision depend on the characteristics of just a handful of extreme scenarios.
Date Issued
2023-12-01
Date Acceptance
2023-05-24
Citation
European Journal of Operational Research, 2023, 311 (2), pp.789-800
URI
http://hdl.handle.net/10044/1/104656
URL
https://www.sciencedirect.com/science/article/pii/S0377221723004095
DOI
https://www.dx.doi.org/10.1016/j.ejor.2023.05.030
ISSN
0377-2217
Publisher
Elsevier
Start Page
789
End Page
800
Journal / Book Title
European Journal of Operational Research
Volume
311
Issue
2
Copyright Statement
© 2023 Published by Elsevier B.V. Copyright © 2023 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
License URL
https://creativecommons.org/licenses/by/4.0/
Identifier
https://www.sciencedirect.com/science/article/pii/S0377221723004095
Publication Status
Published
Date Publish Online
2023-05-28
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