Bayesian epistemology says that rational agents have credences that align with the probability calculus. A common objection to this is that this is actually really *really* demanding. But we don’t have to say that rationality is about having perfectly calibrated credences that match the probability calculus to an arbitrary number of decimal points. Instead we want to say something like “Look, this is just our idealized model of perfectly rational reasoning. We understand that any agent with finite computational capacities is incapable of actually putting real numbers over the set of all possible worlds and updating them with perfect precision. All we say is that the closer to this ideal you are, the better.”

Which raises an interesting question: what do we mean by ‘closeness’? We want some metric to say *how* rational/irrational a given a given person is being (and how they can get *closer* to perfect rationality), but it’s not obvious what this metric should be. Also, it’s important to notice that the details of this metric are not specified by Bayesianism! If we want a precise theory of rationality that can be applied in the real world, we probably have to layer on at least this one additional premise.

Trying to think about candidates for a good metric is made more difficult by the realization that descriptively, our actual credences almost certainly don’t form a probability distribution. Humans are notoriously sub additive when considering the probabilities of disjuncts versus their disjunctions. And I highly doubt that most of my actual credences, insofar as I have them, are normalized.

That said, even if we imagine that we have some satisfactory metric for comparing probability distributions to non-probability-distributions-that-really-ought-to-be-probability-distributions, our problems still aren’t over. The demandingness objection doesn’t just say that it’s *hard* to be rational. It says that in some cases the Bayesian standard for rationality *doesn’t actually make sense. *Enter the problem of logical omniscience.

The Bayesian standard for ideal rationality is the Kolmogorov axioms (or something like it). One of these axioms says that for any tautology T, P(T) = 1. In other words, we should be 100% confident in the truth of any tautology. This raises some thorny issues.

For instance, if the Collatz conjecture is true, then it is a tautology (given the definitions of addition, multiplication, natural numbers, and so on). So a perfectly rational being should instantly adopt a 100% credence in its truth. This already seems a bit wrong to me. Whether or not we have deduced the Collatz conjectures from the axioms looks more like an issue of raw computational power than one of rationality. I want to make a distinction between what it takes to be rational, and what it takes to be *smart*. Raw computing power is not necessarily rationality. Rationality is good software running on that hardware.

But even if we put that worry aside, things get even *worse* for the Bayesian. Not only can a Bayesian not say that your credences in tautologies can be reasonably non-1, they also have no way to account for the phenomenon of obtaining evidence for mathematical truths.

If somebody comes up to you and shows you that the first 10^20 numbers all satisfy the Collatz conjecture, then, well, the Collatz conjecture is still either a tautology or a contradiction. Updating on the truth of the first 10^20 cases shouldn’t sway your credences at all, because *nothing* should sway your credences in mathematical truths. Credences of 1 stay 1, always. Same for credences of 0.

That is really *really* undesirable behavior for an epistemic framework. At this moment there are thousands of graduate students sitting around feeling uncertain about mathematical propositions and updating on evidence for or against them, and it looks like they’re being perfectly rational to do so. (Both to be uncertain, and to move that uncertainty around with evidence.)

The problem here is not a superficial one. It goes straight to the root of the Bayesian formalism: the axioms that define probability theory. You can’t just throw out the axiom… what you end up with if you do so is an entirely different mathematical framework. You’re not talking about probabilities anymore! And without it you don’t even have the ability to say things like P(X) + P(-X) = 1. But keeping it entails that you can’t have non-1 credences in tautologies, and correspondingly that you can’t get evidence for them. It’s just *true* that P(theorem | axioms) = 1.

Just to push this point one last time: Suppose I ask you whether 79 is a prime number. Probably the first thing that you automatically do is run a few quick tests (is it even? Does it end in a five or a zero? No? Okay, then it’s not divisible by 2 or 5.) Now you add 7 to 9 to see whether the sum (16) is divisible by three. Is it? No. Upon seeing this, you become more confident that 79 is prime. You realize that 79 is only 2 more than 77, which is a multiple of 7 and 11. So 79 can’t be divisible by either 7 or 11. Your credence rises still more. A reliable friend tells you that it’s not divisible by 13. Now you’re even more confident! And so on.

It sure *looks* like each step of this thought process was perfectly rational. But what is P(79 is prime | 79 is not divisible by 3)? The exact same thing as P(79 is prime): 100%. The challenge for Bayesians is to account for this undesirable behavior, and to explain how we can reason inductively about logical truths.

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