Accession Number : ADA132759

Title :   A Comparison of Importance Weights for Multiattribute Utility Analysis Derived from Holistic, Indifference, Direct Subjective and Rank Order Judgments.

Descriptive Note : Research rept.,

Corporate Author : UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES SOCIAL SCIENCE RESEARCH INST

Personal Author(s) : John,Richard S ; Edwards,Ward ; Collins,Linda

PDF Url : ADA132759

Report Date : Jun 1980

Pagination or Media Count : 46

Abstract : Research done in the 1960's and early 1970's suggested that although statistical weights and subjective weights show some correspondence in regression-like situations, subjective weights tend to be too flat by comparison; statistical weights usually show that some attributes are quite important, while others are hardly important at all. More recent discussions of this literature, however, have pointed out a number of methodological problems with much of the early research, and have reached a more optimistic conclusion with respect to subjective weights. Several experiments support the more recent interpretation. The present study compared weight estimation procedures for additive, riskless, four-attribute value functions with linear single-attribute values. Self-explicated (subjective) weights were assessed from direct subjective and rank order estimates of attribute importance; observer-derived weights were determined both from indifference judgments (axiomatic approach) and from holistic evaluations (statistical approach) of alternatives. Assessed weights were compared to a true weight vector used to generate feedback during pre-assessment learning trials (constructed with zero inter-attribute correlations). Although self-explicated weights tended to be flatter than observer-derived weights, resulting composites correlated equally well with true composites. Only slight differences were found in ordinal correspondence between true and assessed weights.

Descriptors :   *Decision theory, *Weighting functions, Probability, Rank order statistics

Subject Categories : Psychology
      Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE