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.
The argument I give against assigning precise credences is that it’s arbitrary — literally, you pick one precise credence over many others for no reason. To me, “you have no reason to do this thing” is a pretty darn strong argument. :) (ETA: I like the intuition pump in this very short post, if it helps.)
doesn’t say why this means we shouldn’t/can’t pick credences according to our best effort.
Why does “our best effort” need to be precise? Can you say more what exactly you mean? (If the intuition is that more precision = more information, I address that in the post.)
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.
I address this in the unawareness sequence. I recommend reading the table in my summary post — the row with “Even if our impact is dominated by consequences we’re unaware of...” — for the high-level idea, and the links therein for details.
especially when we’re not given an alternative
Isn’t this privileging the hypothesis? My claim is that we don’t have a positive argument in favor of doing what the precise EV approach recommends (or fuzzier “best guesses”, either). If our best defense of that approach is “what else is there?”, that seems rather damning.
literally, you pick one precise credence over many others for no reason
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.
Why does “our best effort” need to be precise?
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.
(from link) It doesn’t follow from “we don’t know the net direction of the consequences we’re unaware of” that we should regard the positives and negatives as precisely symmetric. One reason symmetry is implausible: If we become aware of a new possible consequence, this should update our beliefs about the others we’re unaware of, breaking the symmetry.
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.
My claim is that we don’t have a positive argument in favor of doing what the precise EV approach recommends
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
(Due to time constraints I expect I can only give brief replies/clarifications, going forward. I hope a full read of the sequence will suffice, though I realize it’s quite long, sorry!)
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. … I don’t see why we should do so with credences (which are of course usually non-foundational statements)
(See my last para for the “future lightcone” thing.)
I don’t understand your Munchausen trilemma argument yet. You say credences are “of course usually non-foundational”. Agreed! That’s exactly why I think our choices of credences require deeper justification. (Whereas foundational things, like Huemer’s “seemings”, don’t.[1])
forecasting 0.1234567% chance of rain if the extra precision was actually decision-relevant
The extra precision might be “decision-relevant” in the sense that: if you were justified in a credence of 0.1234567% + 0.0000001%, you should choose A, and if you were justified in a credence of 0.1234567% − 0.0000001%, you should choose B. But the whole question is why we’d be justified in the former vs. the latter, epistemically. (“I need to make a choice” isn’t a justification for any particular option you choose.)
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.
That’s not what I’m saying, sorry — I’m denying we should give equal credence. Please see my reply to a similar comment here, and section 3.2.1 and 4.1.1 of the sequence (you might need to CTRL+F some terms defined earlier in the sequence). If it’s still unclear, I’m happy to try to explain further if you could point to particular passages that need clarification.
The precise EV approach is well evidenced in short-term decision-making
I don’t know what exactly this means. If you mean “we seem to be justified in using precise EVs in short term decision making”:
I think our beliefs shouldn’t be precise in basically any real-world case, not just beliefs about the far future. (Sec 2.2)
So I think what’s going on is simply that short term decisions aren’t sensitive to the imprecision in the beliefs we’re actually justified in having. The principled difference from the far future case is that in the latter, our decisions are sensitive to the imprecision.
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.
Hi Arepo, thanks for sharing your cruxes here.
The argument I give against assigning precise credences is that it’s arbitrary — literally, you pick one precise credence over many others for no reason. To me, “you have no reason to do this thing” is a pretty darn strong argument. :) (ETA: I like the intuition pump in this very short post, if it helps.)
Why does “our best effort” need to be precise? Can you say more what exactly you mean? (If the intuition is that more precision = more information, I address that in the post.)
I address this in the unawareness sequence. I recommend reading the table in my summary post — the row with “Even if our impact is dominated by consequences we’re unaware of...” — for the high-level idea, and the links therein for details.
Isn’t this privileging the hypothesis? My claim is that we don’t have a positive argument in favor of doing what the precise EV approach recommends (or fuzzier “best guesses”, either). If our best defense of that approach is “what else is there?”, that seems rather damning.
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
(Due to time constraints I expect I can only give brief replies/clarifications, going forward. I hope a full read of the sequence will suffice, though I realize it’s quite long, sorry!)
(See my last para for the “future lightcone” thing.)
I don’t understand your Munchausen trilemma argument yet. You say credences are “of course usually non-foundational”. Agreed! That’s exactly why I think our choices of credences require deeper justification. (Whereas foundational things, like Huemer’s “seemings”, don’t.[1])
The extra precision might be “decision-relevant” in the sense that: if you were justified in a credence of 0.1234567% + 0.0000001%, you should choose A, and if you were justified in a credence of 0.1234567% − 0.0000001%, you should choose B. But the whole question is why we’d be justified in the former vs. the latter, epistemically. (“I need to make a choice” isn’t a justification for any particular option you choose.)
That’s not what I’m saying, sorry — I’m denying we should give equal credence. Please see my reply to a similar comment here, and section 3.2.1 and 4.1.1 of the sequence (you might need to CTRL+F some terms defined earlier in the sequence). If it’s still unclear, I’m happy to try to explain further if you could point to particular passages that need clarification.
I don’t know what exactly this means. If you mean “we seem to be justified in using precise EVs in short term decision making”:
I think our beliefs shouldn’t be precise in basically any real-world case, not just beliefs about the far future. (Sec 2.2)
So I think what’s going on is simply that short term decisions aren’t sensitive to the imprecision in the beliefs we’re actually justified in having. The principled difference from the far future case is that in the latter, our decisions are sensitive to the imprecision.
That is, they don’t require deeper justification prima facie. They’re still defeasible.