Attribute conflict and preference uncertainty: The RandMAU model

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 o f multiattribute profiles that lead to greater preference uncertainty. Our attribute conflict hypothesis predicts that greater within-alternative conf lict (discrepancy among the attributes of an alternative) leads to more pre ference uncertainty. Our attribute extremity hypothesis predicts that great er attribute extremity (very high or low attribute values) leads to less pr eference 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 variatio n in both the weighting parameters governing trade-offs among attributes an d the curvature parameters governing single-attribute evaluations. Simulati on results show that RandMUF successfully predicts both the attribute confl ict and attribute extremity effects exhibited by the experimental participa nts in Fischer et al. (2000). It also predicts an outcome value effect on e rror whose form depends on the shape of single-attribute functions and on t he type of multiattribute combination rule.