We review recent advances in the field of decision making under uncertainty or ambiguity. We start with a presentation of the general approach to a decision problem under uncertainty, as well as the 'standard' Bayesian treatment and issues with this treatment. We present more general approaches (Choquet expected utility, maximin expected utility, smooth ambiguity and so forth) that have been developed in the literature under the name of models of ambiguity sensitive preferences. We draw a distinction between fully subjective models and models incorporating explicitly some information. We review definitions and characterizations of ambiguity aversion in these models. We mention the challenges posed by some of the models presented. We end with a review of part of the experimental literature and applications of these models to economic settings.

The aim of the paper is to propose a preferences representation model under risk where risk perception can be past experience dependent. A first step consists in considering a one period decision problem where individual preferences are no more defined only on decisions but on pairs (decision, past experience). The obtained criterion is used in the construction of a dynamic choice model under risk. The paper ends with an illustrative example concerning insurance demand. It appears that our model allows to explain modifications in the insurance demand behavior over time observed on the insurance markets for catastrophic risk and difficult to justify with standard models.

This article deals with optimal insurance contracts in the framework of imprecise probabilities and adverse selection. Agents differ not only in the objective risk they face but also in the perception of risk. In monopoly, a range of configurations that VNM preferences preclude appears: a pooling contract may be optimal, incomplete coverage may be offered to high risks, low risks may be better covered. Copyright Springer-Verlag Berlin/Heidelberg 2004Imprecise probabilities, Insurance markets, Adverse selection.,

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