What is ambiguity?

The concept of Ambiguity designates those situations where the information available to the decision maker is insufficient to form a probabilistic view of the world. Thus, it has provided the motivation for departing from the Subjective Expected Utility (SEU) paradigm. Yet, the formalization of the concept is missing. This is a grave omission as it leaves non-expected utility models hanging on a shaky ground. In particular, it leaves unanswered basic questions such as: (1) Does Ambiguity exist?; (2) If so, which situations should be labeled as ‘ambiguous’?; (3) Why should one depart from Subjective Expected Utility (SEU) in the presence of Ambiguity?; and (4) If so, what kind of behavior should emerge in the presence of Ambiguity? The present paper fills these gaps. Specifically, it identifies those information structures that are incompatible with SEU theory, and shows that their mathematical properties are the formal counterpart of the intuitive idea of insufficient information. These are used to give a formal definition of Ambiguity and, consequently, to distinguish between ambiguous and unambiguous situations. Finally, the paper shows that behavior not conforming to SEU theory must emerge in correspondence of insufficient information and identifies the class of non-EU models that emerge in the face of Ambiguity. The paper also proposes a new comparative definition of Ambiguity, and discusses its relation with some of the existing literature.