Jean-Marc Tallon

This paper examines efficient allocations in economies where consumers exhibit heterogeneous smooth ambiguity preferences and face model uncertainty with a common set of identifiable models. Aggregate endowment is ambiguous. We characterize economies where the representative consumer is of the smooth ambiguity type and derive efficient sharing rules. Heterogeneous ambiguity aversion leads to sharing rules that systematically differ from those in vNM-economies. The representative consumer’s ambiguity aversion differs from that of the typical consumer; this leads to more compelling asset-pricing predictions. We focus on point-identified models but show that our insights extend to partially-identified models.

This paper offers a novel perspective on the -maxmin model, taking its components as originating from distinct selves within the decision maker. Drawing from the notion of multiple selves prevalent in inter-temporal decision-making contexts, we present an aggregation approach where each self possesses its own preference relation. Contrary to existing interpretations, these selves are not merely a means to interpret the decision maker’s overall utility function but are considered as primitives. Through consistency requirements, we derive an -maxmin representation as an outcome of a convex combination of the preferences of two distinct selves. We first explore a setting involving objective information and then move on to a fully subjective derivation.

This paper offers a novel perspective on the α-maxmin model, taking its components as originating from distinct selves within the decision maker. Drawing from the notion of multiple selves prevalent in inter-temporal decision-making contexts, we present an aggregation approach where each self possesses its own preference relation. Contrary to existing interpretations, these selves are not merely a means to interpret the decision maker’s overall utility function but are considered as primitives. Through consistency requirements, we derive an α-maxmin representation as an outcome of a convex combination of the preferences of two distinct selves. We first explore a setting involving objective information and then move on to a fully subjective derivation.

Many popular internet platforms use so-called collaborative filtering systems to give personalized recommendations to their users, based on other users who provided similar ratings for some items. We propose a novel approach to such recommendation systems by viewing a recommendation as a way to extend an agent’s expressed preferences, which are typically incomplete, through some aggregate of other agents’ expressed preferences. These extension and aggregation requirements are expressed by an Acceptance and a Pareto principle, respectively. We characterize the recommendation systems satisfying these two principles and contrast them with collaborative filtering systems, which typically violate the Pareto principle.