I've been trying to scurry academic fields for discussions of how agents optimally reduce their expected error for various estimands (parameters to estimate). This seems like a really natural thing to me (the main reason why we choose some ways of predictions over others), but the literature seems kind of thin from what I can tell.
The main areas I've found have been Statistical Learning Theory and Bayesian Decision / Estimation Theory. However, Statistical Learning Theory seems to be pretty tied to Machine Learning, and Bayesian Decision / Estimation Theory seem pretty small.
Preposterior analyses like expected value of information / expected value of sample information seem quite relevant as well, though that literature seems a bit disconnected from the above two mentioned.
(Separately, I feel like preposterior analyses should be a much more common phrase. I hadn't actually heard about it until recently, but the idea and field is quite natural.)