Preferences with Multiple Forecasts

When a collective decision maker presents a menu of uncertain prospects to her group members, each member's choice depends on their predictions about payoff-relevant states. In reality, however, these members hold different predictions; more precisely, they have different prior beliefs about states and predictions about the information they will receive. In this paper, we develop an axiomatic framework to examine collective decision making under such disagreements. First, we characterize two classes of representations: Bewley multiple learning (BML) representations, which are unanimity rules among predictions, and justifiable multiple learning (JML) representations, where a single prediction has veto power. Furthermore, we characterize a general class of representations called hierarchical multiple learning representations, which includes BML and JML representations as special cases. Finally, motivated by the fact that these representations violate completeness or intransitivity due to multiple predictions, we propose a rationalization procedure for constructing complete and transitive preferences from them.