On Confident Outrankings with Uncertain Criteria Significance Weights

Raymond Bisdorff

Chapter Chapter 18 in Algorithmic Decision Making with Python Resources, 2022, pp 251-260 from Springer

Abstract: Abstract When modelling preferences following the outranking approach, the signs of the majority margins do sharply distribute validation and invalidation of pairwise outranking situations. How can we be confident in the resulting outranking digraph, when we acknowledge the usual imprecise knowledge of criteria significance weights coupled with small majority margins? In this chapter we propose to link the qualifying significance majority with a required α%-confidence level. We model therefore the significance weights as random variables following more or less widespread distributions around an average significance value that corresponds to the given deterministic weight. As the bipolar-valued random credibility of an outranking statement hence results from the simple sum of positive or negative independent random variables, we may apply the Central Limit Theorem (CLT) for computing the bipolar likelihood that the expected majority margin will indeed be positive and, respectively, negative.

Date: 2022
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DOI: 10.1007/978-3-030-90928-4_18

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