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.
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In this paper the method of the Analytic Hierarchy Process (AHP) is described. At the beginning the general assumptions of the method are characterized and discussed. These are related to assumptions held within General Systems Theory. Then the problems of pairwise comparisons of elements, with its use of a specific scale, as well as the resulting reciprocal matrix are presented. There are many ways of estimating the eigenvectors of this matrix. These eigenvectors reflect weights of preferences. Despite the fact that we are able to evaluate the consistency of judgements the problem of acceptable weights still remains. Therefore, by way of an illustration, the method for the sensitivity analysis of preferences is also discussed in the paper.
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Dostęp do pełnego tekstu na zewnętrznej witrynie WWW
In this paper the method of the Analytic Hierarchy Process (AHP) is described. At the beginning the general assumptions of the method are characterized and discussed. These are related to assumptions held within General Systems Theory. Then the problems of pairwise comparisons of elements, with its use of a specific scale, as well as the resulting reciprocal matrix are presented. There are many ways of estimating the eigenvectors of this matrix. These eigenvectors reflect weights of preferences. Despite the fact that we are able to evaluate the consistency of judgements the problem of acceptable weights still remains. Therefore, by way of an illustration, the method for the sensitivity analysis of preferences is also discussed in the paper.
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