Some related things that come to mind:
Challenges to Bayesian Confirmation Theory outlines some conceptual potential issues arising from the use of explicit probabilities in a Bayesian framework.
Gerd Gigerenzer likes to claim that “fast and frugal” heuristics often just perform better than more formal, quantitative models. These claims can be linked to the bias-variance tradeoff and extreme priors.
The optimizer’s curse can be generalized to the satisficer’s curse. This generalization doesn’t obviously seem to differentially affect explicit probabilities though.
Thanks for these links. I know a little about the satisficer’s curse, and share the view that “This generalization doesn’t obviously seem to differentially affect explicit probabilities though.” Hopefully I’ll have time to look into the other two things you mention at some point.
(My kneejerk reaction to “”fast and frugal” heuristics often just perform better than more formal, quantitative models” is that if it’s predictable that a heuristic would result in more accurate answers, even if we imagine we could have unlimited time for computations or whatever, then that fact, and ideally whatever causes it, can just be incorporated into the explicit model. But that’s just a kneejerk reaction. And in any case, if he’s just saying that in practice heuristics are often better, then I totally agree.)