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Eigenvector Priority Function Causes Strong Rank Reversal in Group Decision Making

Pérez J., Mokotoff E.

Fundamenta Informaticae

2016

Vol. 144, nr 3/4

255--261

This paper shows an example of strong rank reversal in group decision making. Decision makers have preferences expressed through a reciprocal paired comparison matrix. Every one of them applies the eigenvector priority function to her paired comparison matrix to obtain her individual priority vector and then a group priority vector is computed by any of the following two procedures: a) Averaging the already computed individual priority vectors, and b) Averaging the entries of the comparison matrices to obtain a group comparison matrix, and applying to it the eigenvector priority function. Strong rank reversal means that there is one alternative that has the highest priority for every decision maker, and consequently the highest priority in the averaged priority vector obtained by procedure (a), but loses such highest priority when procedure (b) is applied.