This is a deeply confused set of arguments.
There's no fundamental ontological difference between "give me your likelihood ratios between drawing cards [a and b] as the next draw from this deck of cards" and "give me your likelihood ratios between finding and not finding a continent in an unexplored part of the world". While what we ultimately care about is the territory, which is fixed, we only have our imperfect map of the world with which to model it. There is nothing inherently "probabilistic" about a shuffled deck of cards - the cards have a specific configuration; the uncertainy is only present in the observer(s). Predicting the next draw from a deck of cards is subject to "out of context" model violations the same way that any other prediction over future world states is.
The proposed escape hatch seems to be to dodge the question, but of course in reality you are not agnostic over all possible future events that haven't been ruled out by being "bad explanations". You can say, "well, don't make predictions with numbers on them", but by acting in the world you are making predictions with numbers on them all the time! Bayesian epistemology doesn't claim to be perfect, it claims to be the least inaccurate way of making those predictions and updating on new evidence. Now, there's a separate question of how bounded reasoners (i.e. humans) can best accomplish their goals, and "explicitly run Bayes to decide whether to get out of bed" probably isn't it. But superforecasters are an existence proof that explicit Bayesian calculations are still extremely useful even in highly uncertain domains.
Mechanistic explanations are great but even having a mechanistic explanation doesn't let you say P(x) = 1. There can be enormous differences in reasonable mitigations to take between P(x) = 0.99 and 0.999999, depending on context.
There's no fundamental ontological difference between "give me your likelihood ratios between drawing cards [a and b] as the next draw from this deck of cards" and "give me your likelihood ratios between finding and not finding a continent in an unexplored part of the world". While what we ultimately care about is the territory, which is fixed, we only have our imperfect map of the world with which to model it. There is nothing inherently "probabilistic" about a shuffled deck of cards - the cards have a specific configuration; the uncertainy is only present in the observer(s). Predicting the next draw from a deck of cards is subject to "out of context" model violations the same way that any other prediction over future world states is.
The proposed escape hatch seems to be to dodge the question, but of course in reality you are not agnostic over all possible future events that haven't been ruled out by being "bad explanations". You can say, "well, don't make predictions with numbers on them", but by acting in the world you are making predictions with numbers on them all the time! Bayesian epistemology doesn't claim to be perfect, it claims to be the least inaccurate way of making those predictions and updating on new evidence. Now, there's a separate question of how bounded reasoners (i.e. humans) can best accomplish their goals, and "explicitly run Bayes to decide whether to get out of bed" probably isn't it. But superforecasters are an existence proof that explicit Bayesian calculations are still extremely useful even in highly uncertain domains.
Mechanistic explanations are great but even having a mechanistic explanation doesn't let you say P(x) = 1. There can be enormous differences in reasonable mitigations to take between P(x) = 0.99 and 0.999999, depending on context.