Abstract. We elaborate on Watson and Holmes' observation that misspecification is contextual: a model that is wrong can still be adequate in one prediction context, yet grossly inadequate in another. One can incorporate such phenomena by adopting a generalized posterior, in which the likelihood is multiplied by an exponentiated loss. We argue that Watson and Holmes' characterization of such generalized posteriors does not really explain their good practical performance, and we provide an alternative explanation which suggests a further extension of the method.It is a pleasure to comment on this stimulating paper about decision making under model misspecification. I was happy to see that it begins by pointing out that misspecification is contextual-a point that cannot be stressed enough, and that has also played a central part in my own work on Bayesian inconsistency under misspecification (Grünwald andLangford, 2007, Grünwald andVan Ommen, 2014). I will focus my comments on this aspect and on the developments in Section 4, which are the most closely related to my own work. While I think the paper's combined Bayesminimax approach has substantial merit for the case of "simple" loss functions of the form L a (θ ), involving model parameters and actions (as in the synthetic example in their Section 3.5), I am more skeptical of the application to losses of the form L a (θ, z) or L a (z) that involve data z as well, as in their Section 4.2. I do see the merit of the approaches described by the authors for such losses (indeed I have been advocating them myself), yet I do not see how their characterization can explain their practical success: the proposed formalism is rich enough to incorporate such approaches as special cases, but it does not really motivate them. Before elaborating on this in Section 3, below I first introduce data-dependent (DD from now on) losses and I then show how nicely they illustrate the contextuality of misspecification. I end by suggesting an extension to the paper's approach that may address my concerns.