Přehled o publikaci
2024
Robustness of priority deriving methods for pairwise comparison matrices against rank reversal: A probabilistic approach
GÓRECKI, Jan; David BARTL and Jaroslav RAMÍKBasic information
Original name
Robustness of priority deriving methods for pairwise comparison matrices against rank reversal: A probabilistic approach
Authors
GÓRECKI, Jan; David BARTL and Jaroslav RAMÍK
Edition
Annals of Operations Research, NETHERLANDS, Springer, 2024, 0254-5330
Other information
Language
English
Type of outcome
Article in a journal
Country of publisher
Switzerland
Confidentiality degree
is not subject to a state or trade secret
References:
Marked to be transferred to RIV
Yes
RIV identification code
RIV/47813059:19520/24:A0000367
Organization
Obchodně podnikatelská fakulta v Karviné – Slezská univerzita v Opavě – Repository
UT WoS
EID Scopus
Keywords in English
Decision analysis; Pairwise comparison matrix; Priority vector; Random perturbations; Rank reversal
Links
GA21-03085S, research and development project.
Changed: 29/8/2024 00:50, Bc. Ivana Glabazňová
Abstract
In the original language
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.
