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

摘要

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.