A Comparative Evaluation of Multiattribute Consumer Preference Models
比较了多种消费者偏好模型（如加性、乘性、联合分析、效用模型）在确定性和不确定性决策下的预测准确性，发现统计估计优于代数解法，效用模型在不确定性决策中表现更好。
In this paper the theory and estimation procedures for several consumer preference models are discussed. Predictive accuracy in the form of internal consistency of these models is compared in an empirical application. Consumer decision situations are classified into two classes: decisions under certainty and decisions under uncertainty. For each of the two classes of decision situations two modeling strategies have been used: statistical estimation and algebraic solution. An additive conjoint, an additive and a multiplicative measurable value, and an additive and a multiplicative utility model are considered. Our main finding is that the statistical estimation procedures outperform their algebraic counterparts on the criterion of predictive accuracy. The utility model provides better predictions for decisions under uncertainty than the widely used conjoint models. The relationship between models for decisions under certainty and decisions under uncertainty is discussed. It is shown how a conjoint or a measurable value function model can be transformed into a utility model with minimum additional information from the subjects. A concept of relative risk attitude is proposed to segment consumers by the degree of their risk aversion or risk seeking propensities.