Abstract We explore decision-making under uncertainty using a framework that decomposes into three distinct layers: (1) risk, which entails inherent randomness within given probability model; (2) model ambiguity, about the to be used; and (3) misspecification, presence of correct among set models considered. Using new experimental design, we isolate measure attitudes toward each layer separatel...