Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We explore decision-making under uncertainty using a framework that decomposes uncertainty into three distinct layers: (1) risk, which entails inherent randomness within a given probability model; (2) model ambiguity, which entails uncertainty about the probability model to be used; and (3) model misspecification, which entails uncertainty about the presence of the correct probability model among the set of models considered. Using a new experimental design, we isolate and measure attitudes toward each layer separately. We conduct our experiment on three different subject pools and document the existence of a behavioral distinction between the three layers. In addition to providing new insights into the underlying processes behind ambiguity aversion, we provide the first empirical evidence of the role of model misspecification in decision-making under uncertainty.