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.

We consider nancial markets with heterogeneously ambiguous assets and heterogeneously ambiguity averse investors. Investors’ preferences, a version of the smooth ambiguity model, are a parsimonious extension of the standard mean-variance framework. We consider, in a uni ed setting, portfolio choice, and trade upon arrival of public information, and show, in both cases, there are systematic departures from the predictions of standard theory. These departures are of signi cance as they occur in the direction of empirical regularities that belie the standard theory. In particular, our theory speaks to several puzzling phenomena in a uni ed fashion: the asset allocation puzzle, the observation that earnings announcements are often followed by signi cant trading volume with small price change, and that increases in uncertainty are positively associated with increased trading activity and portfolio rebalancing toward safer assets by individual (retail) investors

We investigate consequences of ambiguity on efficient allocations in an exchange economy. Ambiguity is embodied in the model uncertainty perceived by the consumers: they are unsure what would be the appropriate probability measure to apply to evaluate consumption and keep in consideration a set P of alternative probabilistic laws. Consumers are heterogeneously ambiguity averse with smooth

Nous passons en revue les implications en termes d’allocation du risque des modèles de décision développés par David Schmeidler. Nous revenons sur le résultat d’inertie des portefeuilles de Dow et Werlang [1992] et discutons de l’extension du résultat dans un cadre d’équilibre. Nous procédons ensuite à une revue des propriétés d’équilibre (indétermination, non révélation d’information) liées à ces modèles. Nous exposons ensuite les propriétés d’optimalité et concluons avec une discussion de la littérature expérimentale sur le sujet.

Nous passons en revue les implications en termes d’allocation du risque des modèles de décision développés par David Schmeidler. Nous revenons sur le résultat d’inertie des portfeuilles de Dow et Werlang (1992) et discutons de l’extension du résultat dans un cadre d’équilibre. Nous procédons ensuite à une revue des propriétés d’équilibre (indétermination, non révélation d’information) liées à ces modèles. Nous exposons ensuite les propriétés d’optimalité et concluons avec une discussion de la littérature expérimentale sur le sujet..

Many popular internet platforms give personalized recommendations to their users, based on other users who have 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 preferences to items she has not yet rated, by means of some aggregate of other agents’ preferences. These extension and aggregation requirements are expressed by an Acceptance and a Pareto principle, respectively. We show through examples that so-called collaborative filtering systems used by popular platforms typically violate the Pareto principle. We then develop a formal model within which we identify the recommendation systems satisfying the above two principles. A central feature of this model is the use of incomplete preference relations to handle agents who have not rated all items.