This is a linkpost for Subjective Probabilities should be Sharp by Adam Elga, which was originally published in Philosophers' Imprint in May 2010. Here is an errata for it. Below is a summary from Claude Opus 4.8 High. Adam said "I took a quick look and at first glance I saw nothing wrong with the summary". I also think the summary is accurate based on my read of the article. I used the following prompt. "Hi. Make an in-depth summary of the paper "Subjective Probabilities should be Sharp", which I send attached".
I very much agree subjective probabilities should be sharp. So I am not concerned about the unawareness argument for "no impartial altruistic justification for preferring any action over another", which relies on unsharp probabilities.
The target debate
Elga takes aim at a popular view in formal epistemology about how to respond to weak or unspecific evidence. He opens with a contrast between three kinds of evidential situations. Sometimes evidence is sharp (you've watched a biased coin land heads 83% of thousands of tosses, so your credence should be ~83%). Sometimes it's sparse but with a clear upshot (you have almost no evidence about whether the number of humans born in 1984 was even, but symmetry still pushes you to ~50%). And sometimes it's sparse and unspecific—his memorable example is a stranger pulling objects from a bag (a regular tube of toothpaste, a live jellyfish, a travel-sized tube of toothpaste), where there's no obvious basis for any particular credence that the next object is toothpaste.
In that third kind of case, many philosophers find it natural to say your degree of belief shouldn't be any single precise number but rather should be indeterminate, vague, or interval-valued—represented by a range like [10%, 80%] (or, more powerfully, by a set of probability functions rather than one). Elga cites Joyce, Levi, Sturgeon, and Walley as defenders of this idea. Their shared claim is not that some computational or representational limitation stops you from forming a precise credence; it's that the evidence itself makes a precise credence unwarranted, no matter how idealized the agent is.
Elga distills the view into a deliberately cautious thesis:
- UNSHARP: It is consistent with perfect rationality to have unsharp degrees of belief.
His own position is the negation:
- SHARP: Perfect rationality requires sharp degrees of belief.
An important clarification
Before arguing, Elga separates SHARP from a stronger doctrine called Uniqueness (the idea that each body of evidence permits exactly one rational credence function). SHARP does not say there's only one permissible function per evidential situation; it allows that several functions might be permissible. It just insists that whichever one(s) you adopt must each be perfectly precise. So this is not a defense of a uniquely rational credence—only of precision.
The strategy: demand a decision rule
Elga's whole case rests on a challenge. On the standard story (expected utility theory), a rational agent's beliefs are a probability function, and she acts to maximize expected utility—and the paper simplifies by assuming utility is linear in dollars. Anyone who says rational agents can have unsharp credences owes a corresponding account of how unsharp probabilities constrain rational action. Elga argues that no acceptable such account exists. If there's no good answer to "how do unsharp credences guide choice?", then the view collapses.
The "great series of bets"
The engine of the argument is a sequential betting setup on some proposition H (say, that it rains tomorrow):
- Bet A: If H is true you lose $10; otherwise you win $15.
- Bet B: If H is true you win $15; otherwise you lose $10.
You're told the full setup in advance, Bet A is offered first and Bet B immediately after, and crucially your opinion about H won't change during the process (no new evidence, no reinterpretation, just the passage of time). The bets are mirror images, so accepting both guarantees a net $5 gain no matter how H turns out.
Elga grants you're not required to accept both (a very confident or very doubtful agent might prefer just one). But he insists on the key premise: a rational agent must accept at least one of the two bets, because rejecting both is dominated—it's worse than accepting both in every outcome, and you can see this in advance. This premise is easy for a sharp-credence theorist to honor. The rest of the paper argues that no version of the unsharp view can.
To set up the problem: suppose your credence is the wide interval P(H) = [10%, 80%]. A sharp agent evaluates Bet A by a clean threshold—accept if P(H) < 60%, reject if P(H) > 60%, optional at exactly 60%. (Bet A's expected value is positive precisely when P(H) is below 60%.) Your interval straddles 60%, and that's where the trouble starts.
Permissive rules are too permissive
The first and most natural family of rules: since your interval spans the 60% threshold, neither accepting nor rejecting Bet A is mandatory—the bet is optional. By the same reasoning Bet B is optional too. But then it's permissible to reject both. Elga says this is plainly absurd: a money-loving agent who knowingly walks away from a guaranteed $5 has departed from perfect rationality. He notes that a wide swath of decision rules in the literature (Levi, Walley, Good, Seidenfeld, Gärdenfors–Sahlin, Gilboa–Schmeidler) deliver exactly this verdict, and so are unacceptable as accounts of ideal rationality. (He's careful to flag that some of these authors—e.g. Gilboa–Schmeidler—may only be theorizing about non-ideal agents, in which case his critique doesn't touch them; and that Levi explicitly embraces the reject-both consequence.)
Strict rules are too strict
At the opposite extreme is the midpoint rule: evaluate bets using the midpoint of your interval, so [10%, 80%] behaves like a precise 45%. This does yield the correct verdict (you'd never reject both bets, since no sharp agent does). But Elga argues it's self-defeating for the unsharp camp. The original motivation for unsharp credences was that the evidence fails to "nail down" any exact probability. Yet the midpoint rule lets the evidence nail down a completely precise pattern of betting odds: an H-ticket worth $100 if true gets valued at exactly $45.000…. If it's fishy for rationality to require an exact credence of 45%, it's equally fishy to require valuing the ticket at exactly $45. So strict rules buy the right behavior only by smuggling precise constraints back in—which removes any reason to have rejected precise credences in the first place. The midpoint rule (and its strict relatives) thus robs unsharpness of its point.
What's needed, and the three "global" attempts
So the unsharp theorist needs a rule that is strict enough to forbid rejecting both bets, yet permissive enough to leave a range of options open when, say, Bet B is offered alone—and that motivates both verdicts naturally. Elga sees only three candidate strategies, all "global" in that they assess choices in light of other (past or future) choices.
1. NARROW — Acting sharpens your interval. Reject Bet A and your P(H) narrows (e.g. to [60%, 80%]), which then disposes you to accept Bet B; in general your intervals contract so as to block predictably inferior sequences. This delivers the right strictness/permissiveness mix. Elga's objection: it forces a rational agent to change her opinion without any change in relevant evidence. His illustration: if your credence about rain is unsharp enough that wearing either a rain-poncho or a non-water-resistant suede jacket is permissible, NARROW says choosing the poncho makes you confident it'll rain and choosing the jacket makes you confident it won't—even though your choice of jacket carries no evidence about the weather (you have no rain-sensing powers, and your clothing doesn't control the sky). Rationality never requires revising an opinion when relevant evidence is unchanged, so NARROW fails.
2. PLAN — When you act, you simultaneously form a plan binding your later choices to cohere with it (reject Bet A → plan to accept Bet B → follow through), but without changing any beliefs. Elga refutes this with the case of Sally, who cares only about money and has a highly unsharp credence about rain. Compare two scenarios: in the first she rejected Bet A and is now offered Bet B; in the second she's offered Bet B alone. PLAN permits rejecting Bet B in the second but not the first. Yet the monetary consequences of accepting and of rejecting Bet B are identical across the two scenarios, and her beliefs are identical, and money is all she cares about—so the situations are alike in every respect she cares about. Rationality can't impose different requirements on choices that are identical in all relevant respects. To the rejoinder "but rejecting Bet B would break her plan," Elga replies that either plan-breaking is something Sally finds costly (contradicting the stipulation that it's costless for her), or it isn't—in which case "Don't break plans!" is as groundless a constraint as "Don't break mirrors!" He flags but sets aside the resolute-choice tradition (Gauthier, McClennen) that would defend plan-following.
3. SEQUENCE — Sequences of actions can be assessed for rationality independently of their parts: each of "reject A" and "reject B" can be individually permissible while the sequence "reject-A-then-reject-B" is impermissible. Elga turns the same Sally argument on it. SEQUENCE makes rejecting Bet B fine when no Bet A preceded it but irrational when it would complete the bad sequence—so it, too, imposes different requirements across two situations Sally can see are identical in everything she cares about. Hence SEQUENCE fails.
Conclusion
Every candidate account of how unsharp probabilities guide action falls into one of the traps: permissive rules wrongly license rejecting both bets; strict rules pin down precise betting odds and thereby destroy the motivation for unsharpness; and the global rules (NARROW, PLAN, SEQUENCE) either demand belief change without evidence change or treat choice-identical situations differently. Since there's no good answer to how unsharp credences constrain rational action, UNSHARP is false—and so perfect rationality requires perfectly sharp probabilities.
I don't think this is a strong argument. There are other cases where you should make commitments that you would later be inclined to break, like Parfit's hitchhiker, and St. Petersburg lotteries with unbounded utility functions. The latter is an argument that unbounded utility functions are irrational, based on similar logic.
Furthermore, "imposes different requirements across two situations Sally can see are identical in everything she cares about". What if I do care about the differences? Or, is this any worse than picking numbers to ensure precision for no better reason than that they occured to you? Because that's what it takes to produce arbitrarily precise probabilities if you fix what information is available to you in realistic settings.
Also, here's another way someone with unsharp probabilities might handle this situation. In summary, I should accept bet A at the start to rule out the possibility of picking a dominated sequence:
If I compare 1 and 2 statewise, then 1 > 2 with some probability, and 1 and 2 are incomparable otherwise. In other words, either 1 beats 2, or I have no decisive reasons favouring either and I can ignore those cases. So I decide on the cases where 1 beats 2 and accept bet A at the start.
Hi Michael.
Why does Parfit's hitchhiker pose a problem? I would think my chance of survival is equal to my chance of keeping the commitment. So I would simply aim to commit as much as possible if I wanted to maximise my chances of survival. I understand the dilemma is that it would make sense for me to break the committment after I was driven to town, but my decision and thoughts in the town would be constrained from my chat with the driver in the desert. If the driver predicted I was 90 % likely to keep the commitment, and their predictions were calibrated, I would be 90 % likely to keep the commitment, and my thoughts would have to be compatible with this? If the driver predicted I was certain to keep the commitment, I would not consider breaking it in town? Otherwise, the predictions of the driver would not be accurate, which violates the set up of the thought experiment? Here is the description of the thought experiment for readers' context.
The St. Petersburg paradox involves an infinite expected payoff, and I reject infinite worlds.
Adam's argument holds as long as, given 2 sharp states of the world A and B, A is better, worse, or as good as B? In Sally's case, her money is the only thing that matters. For realistic cases, many other factors will contribute to the value of a sharp world state.
I understand one should accept bet A based on that strategy. However, unsharp probabilities are supposed to allow for accepting or rejecting A?
Because the same kind of solution is available to someone with unsharp probabilities in Elga's scenario, if you're treating them fairly.
It doesn't require an infinite world, only that you can't be 100% confident in any finite upper bound on your impact that you specify, and that there are infinitely many ways that the world could be (due largely to not full certainty about physics).
(But also 0% to infinite worlds seems epistemically immodest, doesn't treat the evidence on each side fairly, and is poorly argued, imo. But I don’t want to rehash this.)
Why can't the fact that she'd pick a dominated sequence or regret it if she rejects both bets matter to her after rejecting bet A?
They don't have to in every case. If it was A in isolation, and no other decisions, then yes, both rejecting and accepting should be permissible. But that's not the case presented to us.
Solution to which problem? I am not sure what is supposed to be problematic. As far as I understand, one should just commit as much as possible to maximise the chances of survival.
I agree there is a probability above 0 of (counterfactual) impact being larger than X for any X. So I think impact can be arbitrarily large. However, I do not think it can be infinite. The function f(x) = x can take an arbitrarily large value, but not an infinite value (its range is the set of real numbers). The function g(x) = 1/x can take an arbitrary small value, but not a value of exactly 0 (its range is the set of real numbers besides 0).
It is very counterintuitive that could matter for Sally for reasons that do not have to do with money.
That if you use backward induction on acting rationally at each step, you will be worse off. You will predict later that you'll change your mind, unless you can force your future self to honor a commitment (or plan) you'd no longer want to keep when it actually comes time to honor it.EDIT: my bad, the problem is that if you don't use commitments, you could be worse off. Using backward induction in the Sally argument actually works fine, doesn't leave you (or Sally) worse off and doesn't require any commitment.
St Petersburg doesn't require any state to have infinite value. Its value is (canonically) 2^n with probability 1/2^n for each n at least 1. Always finite actual value, but infinite expected value.
I followed up here.
The expected value of the St. Petersburg lottery is 1 + 1 + ... = +inf. It involves finite terms, but infinitely many terms. I meant to relate f(x) = x in my comment to the expected value of the St. Petersburg lottery. If this involved an arbitrarily large number of terms, its expected value would be arbitrarily large, but not infinite.
I don't understand what you're objecting to. Are you saying all probability distributions should only take finitely many possible values? If so, do you have an upper bound on what the total value should be over all of the future and possible existence, that you're 100% confident in, under your course of action if where you do nothing? (It doesn't have to be the least upper bound / tight.)
I object to relying on infinities (not arbitrarily large finities) to guide decisions because they do not explain more empirical evidence than arbitrarily large finities.
I do not have an upper bound for counterfactual impact, but this does not mean it can be infinite. A normal distribution does not have a maximum value. It can take an arbitrarily large value. However, it cannot take an infinite value. Its range is the set of real numbers.
But do they deserve to be privileged at the exclusion of the infinite EV distributions if and when the latter are at least as consistent with the evidence? Why? Shouldn't you use some principle of indifference or symmetry here?
If there's no finite upper bound on what the value the specific probability distribution can take, how can you be 100% confident it is not a probabilistic mixture with a distribution with infinite or undefined EV (but finite for every actual value), like 0.0001% probability to it being drawn from something like a St. Petersburg lottery?
I like the principle of indifference. However, I think infinities are unfalsifiable in principle. In this case, does it make sense for me to attribute probabilities to them? If I did, they would be just metaphysical priors that can never be updated by any evidence.
I also believe 2 actions can have infinite expected cost-effectiveness considering all future effects, and still be comparable. Imagine actions A and B have expected cost-effectiveness of CE_A(t) and CE_B(t) considering effects in the next t years (after a given date), and that CE_A and CE_B tend to positive infinity as t tends to infinity. If the ratio R(t) = CE_A(t)/CE_B(t) tends to:
I am sceptical of the 1st and last cases in practice. However, in any of the above cases, there would be a clear answer about which action has the highest expected cost-effectiveness considering all future effects.
There would be a dilemma if the expected cost-effectiveness growing faster kept oscillating forever between positive and negative forever. For example, if CE_A(t) = t, and CE_B(t) = t^2*sin(t), R(t) = 1/(t*sin(t)), which tends to 0 as t goes to infinity, but keeps oscillating between an infinitesimally positive and negative value. However, in the real world, one could reasonably assume actions grow similarly fast sufficiently far into the future when there would be exactly the same evidence about the effects of A and B? In this case, R always tends to a finite single number as t goes to infinity.
St Petersburg-like lotteries, defined in terms of your Bayesian credences, don't require assigning positive probability to any possible infinities out there in the world.
I'll leave the rest in a footnote, because it's not that relevant to the point I've been making so don't plan to go further with it, but I already wrote it and it may be of interest to you.[1]
Makes sense. I cannot imagine any evidence that would update me. However, if I did, there would be falsifiability.
The way I see it, the probability of Earth having a radius larger than X tends to 0 as X goes to infinity. So I would say the probability of Earth having an infinite radius is exactly 0.
Do we really have any evidence that the universe is globally flat? From Wikipedia's page on the shape of the universe:
We have evidence that the universe is close to flat. However, there are infinitely many values arbitrary close to exactly 0. So applying some sort of principle of indifference results in a probability of exactly 0 of curvature being exactly 0 (or any other sharp value)?
As an aside, there have been many cases where quantities in physics were assumed to be 0, but then turned out to be just small values, like the mass of neutrinos. It often makes sense to round a quantity to 0 for simplicity, but a sufficiently small value would explain exactly the same empirical evidence.
What do you think about the St Petersburg problem now?
I think your arguments re infinities conflict with Occam's razor, and the principle of indifference should be applied across models within the same complexity (or submodels), otherwise you will assign 0 or too little credence to simpler models that are special cases, e.g. parameter value=0 or effectively eliminating some type of feature. There are infinitely many ways the universe could be more complex, and arbitrarily more complex, than you'd guess.
A flat universe effectively has one fewer parameter and is simpler. So it shouldn't get 0 credence. Among flat universes, the bounded/finite ones also have extra parameters for the boundaries or shape of the universe (compared to something that looks like R^3), so the infinite one shouldn't get 0 credence.
(There probably are many ways for the universe to be infinite spatially in its shape, too, but those are more complex than R^3.)
EDIT:
Note that I never suggested to assign 0 probability to anything. I think this leads to further examples to illustrate my point:
Would you assign exactly 0 probability to photons having exactly 0 mass?
Exactly 0 probability to there being no additional fundamental force, because it could just be vanishingly weak? And shouldn't this get you to infinitely many fundamental forces? For any finite set of fundamental forces, you could posit another one and just say it's very weak.
Exactly 0 probability to there not being ghosts, because their effects could just be very small or rare?
I think consistently applying your arguments suggest you should assign 0 probability in these cases, and so your models blow up in complexity and you become too credulous, contrary to Occam's razor.
Thanks for pushing me to think about this more. I had only looked into your post a few days after you published it around 3 years ago, but just had a look again. I agree the money pump you described there does not require prospect which could have an infinite value. It only requires prospects with infinite expected value as you have been saying.
I think there is exactly 0 empirical evidence for distributions with infinite expected value for the same reasons I believe there is exactly 0 empirical evidence for infinities. As far as I can tell, exactly 100 % of the empirical evidence that could ever be gathered in principle could be exactly 100 % explained by distributions with finite expected value. Do you agree? I agree distributions should not have a maximum because one cannot be exactly 100 % confident there are not higher values. However, a lack of maximum does not imply infinite expected value.
This proves too much? One could argue there is a probability above exactly 0 of any given quantity being a probabilistic mixture involving a distribution with infinite or undefined expected value. In this case, all distributions would have an infinite or undefined expected value? For me this is a bitter bullet to bite than fully rejecting distributions with infinite or undefined expected value.
I would apply the principle of indifference to models which explain the same empirical evidence. If a curvature of 0 had a probability above 0, and the values of the curvature just above 0 followed a continuous distribution, the curvature of 0 would be infinitely more likely than a positive curvature arbitrarily close to 0. This is very counterintuitive to me because the curvatures would have an arbitrarily close explanatory power. I would rather concede all universe models are wrong with probability 1 while acknowledging simpler ones are more useful for further scientific progress all else equal.
Yes. I think there will always be infinitely many values arbitrarily close to 0 which explain exactly the same empirical evidence as a value of 0.
Yes.Edit after Michael's comment just below. I would assign a probability of exactly 0 to any physical law because there are arbitrarily many physical laws arbitrarily close to any physical law. So I would also assign a probability of exactly 0 to any set of physical laws, including the set of laws involving any given number of fundamental forces.Yes, but the effects of the ghosts would have to be sufficiently small or rare to be unfalsifiable.I assume the existence of ghosts is falsifiable under some typical definitions. Likewise for some defitions of God. Edit after Michael's comment just below. I would not assign a probability of exactly 1 to something falsifiable.Do you see any undesirable implications of believing in ghosts which have exactly 0 measurable effects on the world? I think this is effectively the same as not believing in such ghosts.
I think you should just expect this, and the answer is not to deny the possibility of St Petersberg lotteries in objective quantities, but to figure out a good way to deal with them (e.g. ignore small enough probabilities, use a bounded utility function, use commitments, use bracketing of some form), or accept that they raise difficult normative problems.
Are you saying you believe in the existence with 100% credence in anything (such as ghosts) that is not ruled out by current evidence, as long as its effects couid be arbitrarily small and are so far indistinguishable from its nonexistence? Or must it also have no important normative implications (under classical utilitarianism?)?
Ghosts could be conscious, experience pleasure and suffering and care about what you do. Some could be vengeful and want harm to fall upon those that have caused them harm in their lives (whether or not they enact it themselves). Others could want to see the happiness of loved ones. Others could want their descendants to live up to their expectations (e.g. in education, work, family, religious adherence), and not care much about their happiness. A large share could be horrified by modern secularism. It could be that every human that dies becomes a ghost indefinitely.
Why do you think I should expect all distributions to have infinite or undefined expected value instead of rejecting such distributions?
I corrected my answers in my past comment. You can see what I crossed out, and wrote after "Edit after Michael's comment just below".
For the "ghosts [I mentioned in my last comment] which have exactly 0 measurable effects on the world", the benefit and harm they could cause would be sufficiently small to be practically negligible.
There is also Hitchens's razor.
Below is how Claude thinks Adam (the author of the article) would object to your comments. The objections make sense to me. Any reactions?
1. "What if I do care about the differences?"
Michael's most direct hit: Elga's Sally argument needs the two B-situations to be identical in everything she cares about, and Michael asks why the agent can't just care about whether she's about to complete a dominated sequence — "why can't the fact that she'd pick a dominated sequence or regret it if she rejects both bets matter to her after rejecting bet A?"
Elga has a ready answer, and it's the one he actually gives in the paper against the parallel "but rejecting B would break her plan" rejoinder. It splits into a dilemma:
Either this caring is a genuine, independent source of value for Sally — in which case the case has been changed, not answered. Elga's Sally is stipulated to care only about money, with reconsideration costless. If you smuggle in a taste for sequence-completion or an aversion to regret, you're no longer discussing Elga's agent; you're conceding that a purely money-motivated unsharp agent is stuck, and rescuing a different agent who has been given an extra terminal value precisely engineered to patch the hole. That's ad hoc: the value exists only to deliver the verdict UNSHARP needs.
Or the caring is not an independent value but just tracks "this would be irrational" — in which case it's viciously circular. "I disprefer rejecting B because rejecting B here is irrational" cannot be what makes it irrational; the account owes us a prior reason, and this isn't one. Elga's "Don't break plans!"-is-like-"Don't break mirrors!" point applies verbatim: either breaking the sequence is independently costly (then say so, and it's a different case) or it isn't (then "avoid completing dominated sequences" is a bare, unmotivated constraint dressed up as a preference).
The regret variant is especially weak. Regret is backward-looking; at the B-node the money consequences of accept-B and reject-B are fixed and identical across the two situations. If anticipated regret genuinely moves her, it's doing so as a real (dis)utility — back to horn one, the case is changed. Vasco's reply on the forum ("it is very counterintuitive that this could matter for Sally for reasons that don't have to do with money") is exactly Elga's point, just stated flatly.
2. Michael's "treat them fairly" / Parfit's-hitchhiker parity argument
This is Michael's best move, and it's really DiGiovanni's commitment point [made here] sharpened into a parity charge: there are cases everyone agrees call for binding commitments you'll later be inclined to break — Parfit's hitchhiker, St. Petersburg with unbounded utility — so the same "commit and rule out the bad branch" solution should be available to the unsharp agent, if you're treating her fairly. And he uses this to answer Vasco's "but unsharp probabilities are supposed to allow rejecting A": "They don't have to in every case. If it were A in isolation, both would be permissible. But that's not the case presented to us."
Elga would grant the parity and then deny it helps — for two reasons.
First, notice what Michael has conceded. He now says the unsharp agent is required to accept A (to zero out the chance of the dominated branch). But that is Elga's whole thesis about this case: rationality forces a determinate verdict at the A-node. The disagreement was never "can she avoid NEITHER?" — of course she can. It's whether the unsharp credence leaves A genuinely optional. Michael answers "no, not here," which means the interval straddling 60% is not translating into optionality on A. So the imprecision is doing no work at the node where it was supposed to; the commitment (or the statewise argument, see below) is doing all of it. That's confirmation of Elga's challenge — "how do unsharp credences constrain action?" — with the answer "they don't; something bolted on top does."
Second, the Parfit's-hitchhiker analogy cuts the wrong way for him. In the hitchhiker case the commitment is valuable because the two situations genuinely differ in a consequence the agent cares about: keep-the-commitment vs break-it have different payoffs (you live vs you die, or the predictor's reading changes your prospects). That's exactly what legitimizes binding there. In Sally's case Elga has stipulated the two B-situations don't differ in any consequence she cares about. So the disanalogy is precisely the feature that makes hitchhiker-style commitment rational: where binding pays, it pays because of a real downstream difference; strip that difference out (as Sally's stipulation does) and the rationale for binding evaporates. Michael can restore the rationale only by putting a real difference back in — which is move 1's first horn again, changing the case.
Vasco's exchange on the hitchhiker actually pins this down: he points out that if you just "commit as much as possible," your chance of survival tracks your commitment probability and there's no residual puzzle. Michael's reply — "the same solution is available to the unsharp agent if you treat them fairly" — is true but double-edged: yes, the resolute solution is available, and invoking it is the concession that local unsharp verdicts had to be overridden.
3. The statewise / maximality argument for accepting A
Michael's most technical contribution (in the top comment) is a way for the unsharp agent to derive "accept A first" without any of NARROW/PLAN/SEQUENCE: comparing "accept A now" (call it 1) against "reject A and hope to accept B" (2), he says 1 statewise-beats 2 with some probability and they're incomparable otherwise — so under maximality 1 is permissible and he'll take it, killing the dominated branch.
Elga's objection: look at what's actually being compared. Option 2 as Michael frames it is "reject A and if I can't guarantee I'll accept B, risk the dominated sequence." To get 1 to dominate 2, he has to treat 2 as carrying a live risk of ending in NEITHER — i.e. he has to already be modeling his own future B-node choice as possibly landing on reject-B. But that's the entire question. If the agent could guarantee she'll accept B after rejecting A (which is just the commitment), then 2 = B-only, which does not dominate 1 = A-only (they're incomparable, as their EVs cross at 60%), and the argument for being required to accept A collapses. So the statewise argument works only on the assumption that she cannot bind her future self — in which case Elga simply agrees the sequence is a problem and asks what makes each local rejection rational — or it works by importing the commitment, in which case the imprecise credence is again idle and we're at move 2's concession [see here]. Either way it doesn't vindicate UNSHARP; it either restates the problem or resolves it by non-credal means.
There's also a subtler point. Maximality, applied node-by-node, is precisely the permissive rule Elga says is too permissive: at the B-node in isolation it licenses reject-B. Michael's statewise argument applies maximality to the ex-ante policy comparison instead. Switching the object of maximization from acts to policies is, once more, the SEQUENCE/PLAN move — so Elga files it there and runs Sally. Michael's is the most resourceful version because he's derived the ex-ante verdict from a dominance relation rather than asserting a plan-norm, but the structural commitment (evaluate policies, not nodes) is identical, and it's that commitment Sally targets.
4. The "arbitrary precision" tu quoque
Michael's jab — isn't requiring sharpness "any worse than picking numbers to ensure precision for no better reason than that they occurred to you"? — is a real objection to SHARP, but Elga would note it's an objection to the plausibility/motivation of sharpness, not to the bet argument. And SHARP has a specific shield here: recall it explicitly does not entail Uniqueness. Elga isn't claiming the evidence picks out one number 45.000%; he allows a range of sharp functions to be permissible responses to the toothpaste evidence. So "you're forcing a spuriously exact number" misfires — SHARP permits you to adopt any of many precise credences; it just denies that your state can itself be spread out. The charge of false precision is aimed at Uniqueness, which Elga has already disowned. What SHARP does insist is that whatever you land on functions as a sharp probability for the purpose of guiding action — and the bet argument is what supports that, independently of how you chose the number.
The bottom line on Michael
Michael is the only one of the three [Anthony, Evans, and you] who attacks the load-bearing premise directly ("what if she cares about the difference?") rather than trying to route around it, and he's right that Elga's argument stands or falls on the stipulation that the two situations are identical in all respects the agent values. But Elga's reply is stable: every way of making the difference "matter" either (i) reintroduces a genuine downstream (dis)utility — which changes Sally's case and concedes that the money-only unsharp agent is stuck — or (ii) makes the mattering parasitic on "it would be irrational," which is circular. The Parfit's-hitchhiker parity and the statewise argument both turn out to require the commitment capacity, and invoking it is precisely the admission Elga wants: that unsharp credences, left to constrain action on their own, deliver the wrong verdict and must be overridden by a resolute policy that behaves like a determinate disposition.
So against all three of your interlocutors the dialectic funnels to the same joint: is a rational ideal agent to be assessed choice-by-choice (Elga) or entitled to bind herself and be assessed over policies (DiGiovanni's commitment, Evan's four-option reframe, Michael's statewise/parity argument)? Michael states the crux most honestly — he'll happily say the unsharp agent is required to accept A here — and that very concession is what Elga reads as victory: the imprecision has stopped doing the one thing it was introduced to do.
Taking bet A doesn’t require any commitment. My argument just uses backward induction (+ignoring statewise incomparability), which you should generally use in sequential choice situations, or else you'll be worse off in many situations, even with sharp probabilities.
It allows unsharpness. Having unsharp probabilities does not require sequential decisions to be made independently.
The argument against unsharp probabilities is defeated. We just have to treat them in certain ways. The summary of the paper here missed one way we could treat them, and claimed too much against another (if we accept commitments or resolute choice in other cases).
Here is a video I found useful that explains how to use backward induction. Below is Claude's reply to your comment after some iteration between us.
Thanks Michael — the backward-induction framing is the strongest version of the reply, and I want to grant what it gets right before saying where I think it's still exposed.
It does defuse three things at once. It needs no commitment (you predict the future Bet B choice and fold it back, rather than binding yourself), it needs no complete ordering (it runs on statewise dominance, so the Bet B node can stay genuinely unsharp), and it isn't ad hoc (backward induction is the standard discipline for sequential choice). So this isn't PLAN in disguise. Fair enough.
But I think the argument turns on a step that quietly does more than "just backward induction." Here is the full tree, with payoffs written as (if H / if not‑H). Bet A pays −10/+15 and Bet B pays +15/−10, so the four leaves are BOTH +5/+5, A-only −10/+15, B-only +15/−10, and NEITHER 0/0:
Notice both Bet B nodes are under-determined: at each, neither action statewise-dominates the other (BOTH vs A-only cross; B-only vs NEITHER cross). That is exactly the optionality unsharpness is meant to preserve, so dominance-pruning removes nothing at a Bet B node. To get a verdict on Bet A, backward induction has to fold each Bet B node back into a single continuation value — and the value of the reject-A branch depends entirely on which of its two (equally maximal) leaves you assume you'll pick.
Crucially, the accept-A node is also under-determined — it can land on BOTH or on A-only. So to compare the two root actions I have to fix a policy over both identical Bet B nodes. There are only three consistent options:
The only statewise-dominance relation anywhere in the tree is BOTH ≻ NEITHER. In particular A-only vs NEITHER crosses — A-only is worse than NEITHER in the H-state (−10 < 0) — so accepting A does not statewise-dominate rejecting A. Under either consistent policy (always-accept or always-reject), both root actions stay admissible and there's no dominance reason to prefer accepting A. And note that under "always accept B," NEITHER is never reached on either branch, so there's nothing for accepting-A to protect against in the first place.
The recommendation to accept A appears only under the third policy — the one that accepts B after accept-A but rejects B after reject-A. That is what produces the BOTH-vs-NEITHER pairing that makes accepting A look dominant. But that policy isn't backward induction resolving each node on its merits; it's a rule that makes your Bet B choice depend on whether Bet A preceded it, handing down different verdicts at two Bet B nodes that (for a money-only agent) are identical in every respect she cares about. That is precisely the SEQUENCE/PLAN pattern Elga's Sally case is built to reject.
Put differently: the recommendation to accept A materialises only when you assume you'll reject B specifically on the reject-A branch — i.e. you distrust your future self on one branch but not the other. That asymmetric self-distrust is either the sophisticated-chooser reading (treat your own future permitted choice as a hazard to steer around) or the differential treatment of identical nodes. Both are exactly the concessions at issue: if you're rationally required to prevent your future self from exercising reject-B, then reject-B was never really optional — which is just SHARP's verdict reached the long way.
So a sharper version of my earlier question: your derivation of "accept A" resolves the accept-A continuation to BOTH and the reject-A continuation to NEITHER. What consistent policy over the two identical Bet B nodes yields that pair? If "always accept B," reject-A gives B-only and the dominance is gone. If "always reject B," accept-A gives A-only and the dominance is gone. The only policy that yields it treats the two Bet B nodes differently — which is the thing an imprecise theorist owes an account of, and which Sally says you can't have.
(One aside on "you'd use backward induction even with sharp probabilities, or be worse off": agreed, but with sharp credences backward induction never has to override a node's verdict — it agrees with local EV-maximisation, and the cases where skipping it hurts are cases of myopia, not override. This is the unique setting where the rule must reverse a choice the agent's own decision rule calls permissible. That asymmetry is the tell.)
Claude is dumb (at least without further critique and verification, and usually with), and your prompt basically put it on the task of defending the position, not actually assessing the arguments fairly. So it turned up bad arguments.
I doubt the author would respond this badly.