Aggregation (Composition) Schema For Eigenvector Scaling Of Criteria Priorities In Hierarchical Structures

Abstract

An eigenvector approach to scaling of choice alternatives based upon multiple attributes evaluated on a relative basis by paired comparisons is reviewed. It is shown that neither compensatory aggregation (composition) nor noncompensatory models may be consistent or appropriate for an individual's utility. An alternative approach, where the individual (judge) evaluates all attributes simultaneously, and thereby, performs a subjective aggregation (composition) across all attributes simultaneously, is proposed. Eigenvector scaling may then be used to "prioritize" choice alternatives based upon aggregated pairwise comparisons.