An alternate method of deriving priorities and related inferences for group decision making in analytic hierarchy process

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3 Citations (Scopus)

Abstract

Some work has been carried out in the past on statistically deriving priorities in Analytic Hierarchy Process (AHP). In AHP, the aggregated worths of the alternatives, when compared with respect to several criteria, are estimated in a hierarchical comparisons model introduced by Saaty. In this setup, statistical models are used for Saaty’s method of scaling in paired comparisons experiments in any level of the hierarchy. At the end, the final priority weights of the alternatives and related inferences are developed with appropriate statistical methods. Existing statistical methods in the literature assume independence of the entries of the paired comparison matrix. However, these entries are highly dependent among themselves. In this article, we propose a statistical method that allows for the dependence among the entries of the pairwise comparisons matrix. The proposed method is then illustrated with a numerical example.

Original languageEnglish (US)
Pages (from-to)279-287
Number of pages9
JournalJournal of Multi-Criteria Decision Analysis
Volume18
Issue number5-6
DOIs
StatePublished - Jan 1 2011

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Inference
Analytic hierarchy process
Group decision making
Statistical methods
Paired comparisons
Statistical model
Scaling
Experiment
Model comparison
Pairwise comparisons

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Strategy and Management

Cite this

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title = "An alternate method of deriving priorities and related inferences for group decision making in analytic hierarchy process",
abstract = "Some work has been carried out in the past on statistically deriving priorities in Analytic Hierarchy Process (AHP). In AHP, the aggregated worths of the alternatives, when compared with respect to several criteria, are estimated in a hierarchical comparisons model introduced by Saaty. In this setup, statistical models are used for Saaty’s method of scaling in paired comparisons experiments in any level of the hierarchy. At the end, the final priority weights of the alternatives and related inferences are developed with appropriate statistical methods. Existing statistical methods in the literature assume independence of the entries of the paired comparison matrix. However, these entries are highly dependent among themselves. In this article, we propose a statistical method that allows for the dependence among the entries of the pairwise comparisons matrix. The proposed method is then illustrated with a numerical example.",
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