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Relationship between priority ratios disturbances and priority estimation errors

Starczewski T.

Journal of Applied Mathematics and Computational Mechanics

2016

Vol. 15, nr 3

143--154

This article is devoted to some problems connected with multicriteria decision analysis. We consider the relationship between the pairwise comparison matrix (PCM) and a priority vector (PV) obtained on the basis of this matrix. The PCM elements are the decision makers’ judgments about priority ratios i.e. the ratios of weights contained in the PV. It is known, that in the case of consistent matrix, we can obtain the exact value of related PV. However, the real-world practice shows that the decision maker does not create a perfectly consistent PCM, and thus usually in such a matrix the judgment’s errors occur. In our paper we use Monte Carlo simulation to study the relationship between various possible distributions of these errors and the distributions of the errors in estimates of the true PV. In these simulation we apply some initial families distribution and some different parameters. We obtain interesting results which show very slight influence families distribution on final PV errors. Our paper show that much bigger influence on simulation result have adopted parameters than selection distribution family.

Incomplete Pairwise Comparison Matrices and Weighting Methods

Csató L., Rónyai L.

Fundamenta Informaticae

2016

Vol. 144, nr 3/4

309--320

A special class of preferences, given by a directed acyclic graph, is considered. They are represented by incomplete pairwise comparison matrices as only partial information is available: for some pairs no comparison is given in the graph. A weighting method satisfies the linear order preservation property if it always results in a ranking such that an alternative directly preferred to another does not have a lower rank. We study whether two procedures, the Eigenvector Method and the Logarithmic Least Squares Method meet this axiom. Both weighting methods break linear order preservation, moreover, the ranking according to the Eigenvector Method depends on the incomplete pairwise comparison representation chosen.