This paper is about the connection between rationality and accuracy. I show that one natural picture about how rationality and accuracy are connected emerges if we assume that rational agents are rationally omniscient. I then develop an alternative picture that allows us to relax this assumption, in order to accommodate certain views about higher order evidence.

The aim of this paper is to apply the accuracy based approach to epistemology to the case of higher order evidence: evidence that bears on the rationality of one's beliefs. I proceed in two stages. First, I show that the accuracy based framework that is standardly used to motivate rational requirements supports steadfastness—a position according to which higher order evidence should have no impact on one's doxastic attitudes towards first order propositions. The argument for this will require a generalization of (...) an important result by Greaves and Wallace for the claim that conditionalization maximizes expected accuracy. The generalization I provide will, among other things, allow us to apply the result to cases of self-locating evidence. In the second stage, I develop an alternative framework. Very roughly, what distinguishes the traditional approach from the alternative one is that, on the traditional picture, we're interested in evaluating the expected accuracy of conforming to an update procedure. On the alternative picture that I develop, instead of considering how good an update procedure is as a plan to conform to, we consider how good it is as a plan to make. I show how, given the use of strictly proper scoring rules, the alternative picture vindicates calibrationism: a view according to which higher order evidence should have a significant impact on our beliefs. I conclude with some thoughts about why higher order evidence poses a serious challenge for standard ways of thinking about rationality. (shrink)

Higher‐order evidence can make an agent doubt the reliability of her reasoning. When this happens, it seems rational for the agent to adopt a cautious attitude towards her original conclusion, even in cases where the higher‐order evidence is misleading and the agent's original reasons were actually perfectly good. One may think that recoiling to a cautious attitude in the face of misleading self‐doubt involves a failure to properly respond to one's reasons. My aim is to show that this is not (...) so. My proposal is that (misleading) higher‐order evidence can undermine the agent's possession of her first‐order reasons, constituting what I call a dispossessing defeater. After acquiring the higher‐order evidence, the agent is no longer in a position to rely competently on the relevant first‐order considerations as reasons for her original conclusion, so that such reasons stop being available to her (even if they remain as strong as in the absence of the higher‐order evidence). In this way, an agent with misleading higher‐order evidence can adopt a cautious stance towards her original conclusion, while properly responding to the set of reasons that she possesses–a set that is reduced due to the acquisition of higher‐order dispossessing defeaters. (shrink)

The externalist says that your evidence could fail to tell you what evidence you do or not do have. In that case, it could be rational for you to be uncertain about what your evidence is. This is a kind of uncertainty which orthodox Bayesian epistemology has difficulty modeling. For, if externalism is correct, then the orthodox Bayesian learning norms of conditionalization and reflection are inconsistent with each other. I recommend that an externalist Bayesian reject conditionalization. In its stead, I (...) provide a new theory of rational learning for the externalist. I defend this theory by arguing that its advice will be followed by anyone whose learning dispositions maximize expected accuracy. I then explore some of this theory’s consequences for the rationality of epistemic akrasia, peer disagreement, undercutting defeat, and uncertain evidence. (shrink)

Accuracy-first epistemology says that the rational update rule is the rule that maximizes expected accuracy. Externalism says, roughly, that we do not always know what our total evidence is. It’s been argued in recent years that the externalist faces a dilemma: Either deny that Bayesian Conditionalization is the rational update rule, thereby rejecting traditional Bayesian epistemology, or else deny that the rational update rule is the rule that maximizes expected accuracy, thereby rejecting the accuracy-first program. Call this the Bayesian Dilemma. (...) Here is roughly how the argument goes. Schoenfield (2017) has shown that following Metaconditionalization maximizes expected accuracy. But if externalism is true, Metaconditionalization is not Bayesian Conditionalization. Therefore, the externalist must choose between the rule that maximizes expected accuracy (Metaconditionalization) and Bayesian Conditionalization. I am not convinced by this argument; once we make the premises fully explicit, we see that it relies on assumptions that the externalist has every reason to reject. Still, I think that the Bayesian Dilemma is a genuine dilemma. I give a new argument—I call it the continuity argument—that doesn’t make any assumptions the externalist rejects. Roughly, I show that if you're sufficiently confident that you will correctly identify your evidence, then you'll expect adopting a rule that I call Accurate Metaconditionalization to be more accurate than adopting Bayesian Conditionalization. (shrink)

Hilary Greaves and David Wallace argue that conditionalization maximizes expected accuracy and so is a rational requirement, but their argument presupposes a particular picture of the bridge between rationality and accuracy: the Best-Plan-to-Follow picture. And theorists such as Miriam Schoenfield and Robert Steel argue that it's possible to motivate an alternative picture—the Best-Plan-to-Make picture—that does not vindicate conditionalization. I show that these theorists are mistaken: it turns out that, if an update procedure maximizes expected accuracy on the Best-Plan-to-Follow picture, it's (...) guaranteed to maximize expected accuracy on the Best-Plan-to-Make picture as well, in which case moving from the former to the latter can't help us avoid the conclusion that conditionalization is a rational requirement. If there's a problem with Greaves and Wallace’s argument, it must lie elsewhere. (shrink)