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.

(This is a very rough thought still, please take it with a grain of salt)
Averaging is a viable strategy against random uncertainty, i.e. variance. OTOH, if the dominating problem of the situation is bias, a selection approach may work.
In practice, the situation may be even more blurred by the additional random uncertainty/variance that stems from judging the "health" of the model with only a limited number of independent cases (for the data I work with, number of idependent cases is often << number of rows in the data).
I'd therefore make the arguement the other way round compared to your example (and rather go in the specification direction Frank Harrell brought to your attention): selection (for low bias) has the prerequisite that variance uncertainty must be low.
