In what applications do we prefer Model Selection over Model Averaging? I'm wondering in what applications or scenarios (or in trying to answer what kind of questions), the researcher would prefer using Model Selection (such as AIC or BIC) over Model Averaging (such as Bayesian model averaging)?  One example: if my question at hand is what's the most appropriate model, then I must use Model Selection.  Another (not sure) example: I guess  if we want less bias, Model Selection is also preferred.       It would be great if you have other examples like these. Paper or book recommendations are also welcome.

Single models are much easier to interpret than ensembles. They are therefore often more easily accepted by non-technical users, and are easier to troubleshoot (e.g., when a prediction is way off).
Averaged models are much harder to apply null hypothesis significance testing to. This is a very hard criterion in many domains where publishability hinges on p values.
