We introduce and characterize axiomatically a general model of static choice under uncertainty, which is possibly the weakest model in which a separation of cardinal utility and a representation of beliefs is achieved. Most of the popular non-expected utility models in the literature are special...

We show that range convexity of beliefs, a 'technical' condition that appears naturally in axiomatizations of preferences in a Savage-like framework, imposes some unexpected restrictions when modelling ambiguity averse preferences. That is, when it is added to a mild condition, range convexity...

We propose and axiomatize a new model of preferences that achieves a separation between ambiguity, identified as a characteristic of the decision maker's subjective information, and ambiguity attitude, a characteristic of the decision maker's tastes.

We study the properties of ultramodular functions, a class of functions that generalizes scalar convexity and that naturally arises in some economic and statistical applications.

This paper introduces a subcalculus for general set functions and uses this framework to study the core of TU games. After stating a linearity theorem, we establish several theorems that characterize mea- sure games having finite-dimensional cores. This is a very tractable class of games...

We introduce a general model of static choice under uncertainty, arguably the weakest model achieving a separation of cardinal utility and a unique representation of beliefs. Most of the non-expected utility models existing in the literature are special cases of it. Such separation is motivated...

The theory of subjective expected utility (SEU) has been extended in many recent works, allowing ambiguity to matter for choice. However, a fully satisfactory and general notion of ambiguity aversion, analogous to risk aversion for SEU, is still missing. Using a new preference model which...

We propose and characterize a model of preferences over acts such that the decision maker prefers act f to act g if and only if <openface>E</openface>_μφ( <openface>E</openface>_πu&cir;f) &ges; <openface>E</openface>_μφ( <openface>E</openface>_πu&cir;g), where <openface>E</openface> is the expectation operator, u is a von Neumann-Morgenstern utility function, φis an increasing transformation, and μis a...