Any quantitative model (e.g., in financial risk management) must rely on modeling assumptions and is thus prone to model risk. In ?The Joint Impact of F Divergences and Reference Models on the Contents of Uncertainty Sets,? the authors reassess the ?robustness? approach to model risk. In this approach, model risk is defined in a nonparametric way. Calculations under a reference model are contrasted against worst case scenarios over all alternative models within a maximal ?divergence? from the reference model. The choices of the reference model and the divergence measure jointly shape the uncertainty set?and thus, the perceived severity of model risk. The authors argue that there is no single divergence measure that is suitable for all reference models. Instead, when choosing a divergence measure, properties of the reference model should be taken into account. This concerns in particular assumptions on tail risk made under the reference model.