Three Layers of Uncertainty

In a new paper published on the Journal of the European Economic Association, Ilke Aydogan, Loïc Berger, Valentina Bosetti and Ning Liu 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;
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, the authors isolate and measure attitudes toward each layer separately. They 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, the authors provide the first empirical evidence of the role of model misspecification in decision-making under uncertainty.