Epistemic Utility Theory and the Aim of Belief

It’s widely accepted that rational belief aims at truth.1 Objectively correct belief is true belief. A more controversial question: how should rational believers pursue the aim of truth? Epistemic utility theorists have argued that the framework of decision theory can explain what it means for rational belief to have the aim of approximating the truth. By combining the tools of decision theory with an epistemic form of value—gradational accuracy, proximity to the truth—we can justify various epistemological norms. These arguments generally use one of two tools: the notion of expected accuracy and the notion of accuracy dominance. For example: it’s been argued that the reason why we should have probabilistically coherent degrees of belief is that it’s the only way to avoid accuracy domination (Joyce 1998, 2009), and the reason why we should shift our beliefs in response to new evidence in a certain way—by a method called “conditionalization”—is that doing so uniquely maximizes expected accuracy (Greaves & Wallace 2006; Leitgeb & Pettigrew 2010b). I’m going to argue that deriving these results requires using notions of “dominance” and “expected utility” that are different in important respects from the versions of dominance and expected utility used in standard (practical) decision theory. If we use the more familiar forms of expected utility and dominance, we can’t justify the epistemic coherence norms that epistemic utility theory had hoped to justify. Indeed, the prescriptions of epistemic utility theory conflict with these norms. Furthermore: the things epistemic utility theorists call “expected accuracy” and “accuracy dominance” can’t really be the expected accuracy or accuracy dominance of epistemic states in any conventional sense. It’s not clear what they are; so far we don’t have a good philosophical interpretation of these pieces of math. Without a philosophical interpretation, they are ill-equipped to do the epistemological work they were meant to do. For example, just telling us that conditionalization maximizes this thing—whatever it is—doesn’t explain why we should conditionalize on our evidence.