In the second post, I'll discuss some more general arguments against Bayesian reasoning as an idealization of human reasoning. What role should "unknown unknowns" play in a bounded Bayesian reasoner? Is "Knightian uncertainty" a useful concept that is not captured by the Bayesian framework?

Typing this before reading because I want to "predict ahead of time": have you considered the arguments for shifting from classical Bayesianism to intuitionistic/constructive Bayesianism for reasons such as these? The long and short of it is that you can have probabilities which don't add normally (may add up to more or less than one) because you're also uncertain as to the space of possible events. There are Dutch Book arguments showing one should use such a probability model if one's bets "resolve" to a definite payout at some indeterminate time after they are made, which may be never.

Furthermore, every choice in life can be viewed as a bet about which available action will lead to the best outcome, and on this view, it is quite reasonable to expect that many bets will be "retracted" (e.g., the opportunity will pass).

Yeah, this would sound like the kind of situation where you use nonclassical Bayesianisms: when you're not actually sure about to what set of mutually-exclusive propositions you're assigning measure 1. When you have uncertainty over what can happen as well as what will happen, minimum expected utility and ambiguity aversion make sense (pick the worst possible event you're very confident can actually happen, and maximize utility for it, assuming that less-possible events will be better than that).