2011International Journal of the Analytic Hierarchy ProcessOpen access

PRIORITY EIGENVECTORS IN ANALYTIC HIERARCHY/NETWORK PROCESSES WITH OUTER DEPENDENCE BETWEEN ALTERNATIVES AND CRITERIA

Stan Lipovetsky

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Abstract

This work continues consideration of the relations between Analytic Hierarchy and Network processes. It shows that in the case of a simple network with outer dependence between alternatives and criteria, the priority vectors can be constructed not only via the powered supermatrix but also by the eigenvectors of the supermatrix. The relationship to the AHP least squares approach and other methods of priority estimation are considered as well. An ANP matrix of local eigenvectors includes priorities for all the compared items in the whole network. Here we interpret the complex AHP/ANP connections and show clearly how they result in priority estimations useful for applied decision making. http://dx.doi.org/10.13033/ijahp.v3i2.123

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This work continues consideration of the relations between Analytic Hierarchy and Network processes. It shows that in the case of a simple network with outer dependence between alternatives and criteria, the priority vectors can be constructed not only via the powered supermatrix but also by the eigenvectors of the supermatrix. The relationship to the AHP least squares approach and other methods of priority estimation are considered as well. An ANP matrix of local eigenvectors includes priorities for all the compared items in the whole network. Here we interpret the complex AHP/ANP connections and show clearly how they result in priority estimations useful for applied decision making. http://dx.doi.org/10.13033/ijahp.v3i2.123

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Available abstract

This work continues consideration of the relations between Analytic Hierarchy and Network processes. It shows that in the case of a simple network with outer dependence between alternatives and criteria, the priority vectors can be constructed not only via the powered supermatrix but also by the eigenvectors of the supermatrix. The relationship to the AHP least squares approach and other methods of priority estimation are considered as well. An ANP matrix of local eigenvectors includes priorities for all the compared items in the whole network. Here we interpret the complex AHP/ANP connections and show clearly how they result in priority estimations useful for applied decision making. http://dx.doi.org/10.13033/ijahp.v3i2.123

Key concepts: Supermatrix, Hierarchy, Analytic hierarchy process, Span (engineering), Margin (machine learning), Computer science, Matrix (chemical analysis), Eigenvalues and eigenvectors

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