A Bayesian priorization procedure for AHP-group decision making

Abstract

This paper proposes a new priorization procedure for Analytic Hierarchy Process Group Decision Making (AHP-GDM). Unlike the methods normally employed in AHP-GDM, this process does not require intermediate filters for the actors' initial judgements. The procedure is based on a Bayesian analysis of the problem and in general, it provides more efficient estimates than the techniques conventionally applied in the literature for AHP-GDM: aggregation of individual judgements (AIJ) and aggregation of individual priorities (AIP). The proposed procedure naturally extends to the analysis of incomplete and/or imprecise pairwise comparison matrices, enhancing realism, practicality and scope. The methodology has been illustrated by the analysis of a case study. © 2006 Elsevier B.V. All rights reserved.