Judgmental consistency and consensus in stochastic multicriteria decision making

Authors:
Eugene D. Hahn
Affiliations:
Department of Information and Decision Sciences, Salisbury University, Salisbury, MD 21801, United States
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 4
Cited 1

The reliability of subjective probabilities obtained through decomposition

Management Science
Comparison of weighting judgments in multiattribute utility measurement

Management Science
Information concepts and pairwise comparison matrices

Information Processing Letters
Analysis of Correlated Expert Judgments from Extended Pairwise Comparisons

Decision Analysis

A decision support system for animated film selection based on a multi-criteria aggregation of referees' ordinal preferences

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.06

Visualization

Abstract

Stochastic multicriteria decision making methods are a class of multicriteria decision making methods in which judgments are not taken to be certain. Recently, this class of models has generated increasing interest in the literature. We unify the notions of judgmental consistency and intra-group consensus under a framework of preference homogeneity in the context of this class of models. We propose new methods and measures for examining departures from consistency by using the Kullback-Leibler divergence. We also propose a new method, the jackknifed Kullback-Leibler divergence, that characterizes the extent to which a group member exhibits preference consensus with the remaining members of the group.