Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Management (Business Administration)

First Advisor

Dan B. Rinks


Multiattribute utility analysis is a set of formal procedures designed to assist a decision maker in resolving problems requiring tradeoffs among competing objectives. The approach elicits a multiattribute utility function (MAUF) intended to represent the decision maker's preferences for multivariate alternatives. While different MAUF assessment technologies are available, few studies have been conducted that compare assessment techniques. The purpose of this research was to investigate the predictive ability of four MAUF elicitation procedures. Keeney-Raiffa (KR), holistic orthogonal parameter estimation (HOPE), simple multiattribute rating technique (SMART), and mathematical programming elicited MAUFs were compared in terms of (1) ordinal preference predictions, (2) first preference predictions, and (3) ability to capture tradeoffs between competing objectives. Since actual decision tasks do not offer a "correct" solution, it is difficult to compare the performances of alternative assessment procedures in a real-world setting. Consequently, a hypothetical decision making environment was established to determine a procedure's performance in the presence of elicitation errors of known type and magnitude. The inputs necessary to implement each procedure were provided by an artificial decision maker. While behavioral influences on a technique's performance were not considered, each experimental elicitation contained one or more of the following assessment errors: noisy respondent inputs; incorrectly specified functional forms; and incompletely defined attribute sets. Validity was determined by comparing MAUF predictions to the artificial subject's known preference constructs. The four procedures were not uniformly robust within or across the elicitation scenarios considered. KR encoded models typically provided the most consistently accurate predictions. HOPE first preference predictions were sensitive to the level of noise contained in the subject's responses. SMART and mathematical programming performances were generally inferior to those of KR and HOPE. SMART offered reasonable overall preference orderings, but was less effective at predicting the subject's most desired alternative. Predictions offered by mathematical programming MAUFs were relatively unstable, at times yielding high proportions of utility loss from incorrect first preferences. This research provides an initial set of guidelines to assist a decision analyst in choosing among competing MAUF elicitation techniques.