I don’t know how to challenge the premises because the key premise seems to be an assertion that I don’t find convincing.
In ‘Should you go with your best guess?‘, which appears to be the primary argument against the idea of ‘deterministic’ Bayesian credences, DiGiovanni repeatedly signposts that he’s going to give an argument against them… but I can’t see anything that constitutes one.
In the section ‘Background on degrees of belief and what makes them rational’, he talks about how we don’t get to find out which beliefs outperform others, but doesn’t say why this means we shouldn’t/can’t pick credences according to our best effort. It also doesn’t say why, if we can measure short term value, we shouldn’t use that as a justification for our decisionmaking process and assume EV from events that we don’t think we can assess is 0.
In the section ‘Motivating example’, he gives an example vignette, at the end of which we’re given that ‘this feels so arbitrary’. But it doesn’t seem like ‘feeling arbitrary’ is a reason not to do something—especially when we’re not given an alternative (or at least, no other decision process that seems less or equally arbitrary).
So my response is just to say ‘using credences still seems fine, if occasionally emotionally uncomfortable (maybe using distributions is sometimes empirically better, and if so I support it)’ - in which case I don’t see a problem in need of solving.
At some foundational level, a credence has no deeper reason than ‘some neurons fired that way’. But you don’t need to restrict yourself to concerns about the whole future lightcone to run into this problem—at the foundational level this is true of every statement.
There are various ways one might respond to this challenge, but if we don’t view it as insurmountable elsewhere, I don’t see why we should do so with credences (which are of course usually non-foundational statements). And if we do, it undermines e.g. any argument about unawareness.
It only needs to be as precise as is necessary for decision-making. I will probably never need to forecast rain to 8 decimal places. But if you’re saying forecasting rain as ‘less than .5’ is ok, but that forecasting 0.1234567% chance of rain if the extra precision was actually decision-relevant, would be un-ok/impossible/qualitatively different, then I disagree.
If you ‘become aware’ of something, you’ve gained information and should update your priors accordingly. That doesn’t move me away from being happy to treat genuine unknowns as EV-0. Your counterpoint seems to be that in some cases that feel sort-of- equal (and about which, in the cases you describe we actually have a lot of information), we might be inclined to give equal credence. But it seems to me correct to say ‘if you have meaningful knowledge of two possible outcomes, and the weight you assign to them is decision-relevant, giving them equal credence is a mistake’, which fixes this purported problem without radically undermining our epistemology.
The precise EV approach is well evidenced in short-term decision-making, so the positive argument is that there isn’t any principled difference between short and long-term decision-making