Three Layers of Uncertainty

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 separately. conduct our experiment on different subject pools document, existence behavioral distinction between layers. In addition providing insights underlying processes behind ambiguity aversion, provide first empirical evidence role misspecification in uncertainty.