This is somewhat ill-defined, but: Why is Wald's decision theory not universally recognized as the foundation of statistics? I gather (or maybe I infer) that it was formulated to put frequentist and Bayesian methods (or any other kind of methods) into a common framework, so that they could be compared in a quantitative way for a wide range of problems.
But I gather that this has not really worked out, in that there are some settings where 'respectable' modern statisticians would still use estimators that are known to be inadmissible in a Waldian sense (like using the usual estimate of the vector mean in three or more dimensions). Why is this? Because loss functions are typically only an approximate stand-in for the true loss function in real problems, which is hard to specify?
I realize this question is rather vague, and I appreciate your patience.