Un cadre axiomatique commun pour la théorie de l’utilité espérée et la théorie de la décision qualitative A common axiomatic framework for Subjective Expected Utility and Qualitative Decision Theory

In the field of Artificial Intelligence many models for decision making under uncertainty have been proposed that deviate from the traditional models used in Decision Theory, i.e. the Subjective Expected Utility (SEU) model and its many variants. These models, forming what is usually called "Qualitative Decision Theory", aim at obtaining simple decision rules that can be implemented by efficient algorithms while based on inputs that are less rich than what is required in traditional models. The purpose of this paper is to present an axiomatic framework for decision under uncertainty that contains as particular cases both types of models. It is based on a model for conjoint measurement tolerating intransitive and/or incomplete preferences. The use of this axiomatic framework allows us to pinpoint the similarities and the differences between these two types of models.