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.

Model averaging is more practical In applications where you don’t have resources to select a model and no need to explain the results in detail. Model selections is much more than comparing AICs, because if that’s all you do to select the models then don’t bother, just average.
A proper selection is a very time consuming expensive process. You can get a PhD working for months to come up with a parsimonious specification, that’s hundreds of thousands $$$, plus ongoing maintenance and opportunity cost of time to market off your product. There are situations when this can be afforded. An example is financial risk management where the cost of error can be existential to the firm, plus you have the regulators scrutinizing the models who also want you to explain the results to minute details. A counter example is marketing and sales, where instead of meticulous model selection you maybe better off with data mining and experimentation.
