Attribute Conflict and Preference Uncertainty: The RandMAU Model
提出RandMAU模型，用随机权重解释多属性评价中的偏好不确定性，模拟显示该模型能预测属性冲突和极端性对偏好的影响，对研究决策行为的经济学者有参考价值。
This paper extends the behavioral results reported in Fischer et al. (2000) by developing a model addressing preference uncertainty in multiattribute evaluation. The model is motivated by two hypotheses regarding properties of multiattribute profiles that lead to greater preference uncertainty. Our attribute conflict hypothesis predicts that greater within-alternative conflict (discrepancy among the attributes of an alternative) leads to more preference uncertainty. Our attribute extremity hypothesis predicts that greater attribute extremity (very high or low attribute values) leads to less preference uncertainty. To provide a deeper explanation of attribute conflict and extremity effects, we develop RandMAU, a family of additive (RandAUF) and multiplicative (RandMUF) random weights multiattribute utility models. In RandMAU models, preference uncertainty is represented as random variation in both the weighting parameters governing trade-offs among attributes and the curvature parameters governing single-attribute evaluations. Simulation results show that RandMUF successfully predicts both the attribute conflict and attribute extremity effects exhibited by the experimental participants in Fischer et al. (2000). It also predicts an outcome value effect on error whose form depends on the shape of single-attribute functions and on the type of multiattribute combination rule.