Robustness of priority deriving methods for pairwise comparison matrices against rank reversal: a probabilistic approach

Jan Górecki (), David Bartl () and Jaroslav Ramík ()
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Jan Górecki: Silesian University in Opava
David Bartl: Silesian University in Opava
Jaroslav Ramík: Silesian University in Opava

Annals of Operations Research, 2024, vol. 333, issue 1, No 9, 249-273

Abstract: Abstract This work aims to answer the natural question of how probable it is that a given method produces rank reversal in a priority vector (PV) if a decision maker (DM) introduces perturbations to the pairwise comparison matrix (PCM) under concern. We focus primarily on the concept of robustness against rank reversal, independent of specific methods, and provide an in-depth statistical insight into the application of the Monte Carlo (MC) approach in this context. This concept is applied to three selected methods, with a special emphasis on scenarios where a method may not provide outputs for all possible PCMs. All results presented in this work are replicable using our open-source implementation.

Keywords: Decision analysis; Pairwise comparison matrix; Priority vector; Random perturbations; Rank reversal (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-023-05753-0

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