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Substantial efforts have been made in developing various Decision Modelingformalisms, both from industry and academia. A challenging problem is that ofexpressing decision knowledge in the context of incomplete knowledge. In suchcontexts, decisions depend on what is known or not known. We argue that none ofthe existing formalisms for modeling decisions are capable of correctlycapturing the epistemic nature of such decisions, inevitably causing issues insituations of uncertainty. This paper presents a new language for modelingdecisions with incomplete knowledge. It combines three principles:stratification, autoepistemic logic, and definitions. A knowledge base in thislanguage is a hierarchy of epistemic theories, where each component theory mayepistemically reason on the knowledge in lower theories, and decisions are madeusing definitions with epistemic conditions.

An epistemic logic for modeling decisions in the context of incomplete knowledge