Automatic Criteria Weight Generation for Multi-criteria Decision Making Under Uncertainty

Danielson, M. & Ekenberg, L. ORCID: (2020). Automatic Criteria Weight Generation for Multi-criteria Decision Making Under Uncertainty. In: Innovation for Systems Information and Decision. Eds. de Almeida, A.T. & Morais, D.C., pp. 1-14 Cham, Switzerland: Springer. ISBN 978-3-030-64398-0 10.1007/978-3-030-64399-7_1.

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Real-life decision situations almost invariably involve large uncertainties. In particular, there are several difficulties connected with the elicitation of probabilities, utilities, and criteria weights. In this article, we explore and test a robust multi-criteria weight generating method covering a broad set of decision situations, but which still is reasonably simple to use. We cover an important class of methods for criteria weight elicitation and propose the use of a reinterpretation of an efficient family (rank exponent) of methods for modelling and evaluating multi-criteria decision problems under uncertainty. We find that the rank exponent (RX) family generates the most efficient and robust weighs and works very well under different assumptions. Furthermore, it is stable under varying assumptions regarding the decision-makers’ mindset and internal modelling. We also provide an example to show how the algorithm can be used in a decision-making context. It is exemplified with a problem of selecting strategies for combatting COVID-19.

Item Type: Book Section
Uncontrolled Keywords: Multi-criteria decision analysis; Uncertain reasoning; Criteria weights; Criteria ranking; Rank order; Automatic weight generation; COVID-19
Research Programs: Risk & Resilience (RISK)
Depositing User: Luke Kirwan
Date Deposited: 30 Nov 2020 08:39
Last Modified: 27 Aug 2021 17:33

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