In the Book of Common Prayer’s Rite II version of the Eucharist, the congregation confesses, “we have sinned against you in thought, word, and deed”. According to this confession we wrong God not just by what we do and what we say, but also by what we think. The idea that we can wrong someone not just by what we do, but by what think or what we believe, is a natural one. It is the kind of wrong we feel (...) when those we love believe the worst about us. And it is one of the salient wrongs of racism and sexism. Yet it is puzzling to many philosophers how we could wrong one another by virtue of what we believe about them. This paper defends the idea that we can morally wrong one another by what we believe about them from two such puzzles. The first puzzle concerns whether we have the right sort of control over our beliefs for them to be subject to moral evaluation. And the second concerns whether moral wrongs would come into conflict with the distinctively epistemic standards that govern belief. Our answer to both puzzles is that the distinctively epistemic standards governing belief are not independent of moral considerations. This account of moral encroachment explains how epistemic norms governing belief are sensitive to the moral requirements governing belief. (shrink)

The pragmatic character of the Dutch book argument makes it unsuitable as an "epistemic" justification for the fundamental probabilist dogma that rational partial beliefs must conform to the axioms of probability. To secure an appropriately epistemic justification for this conclusion, one must explain what it means for a system of partial beliefs to accurately represent the state of the world, and then show that partial beliefs that violate the laws of probability are invariably less accurate than they could be otherwise. (...) The first task can be accomplished once we realize that the accuracy of systems of partial beliefs can be measured on a gradational scale that satisfies a small set of formal constraints, each of which has a sound epistemic motivation. When accuracy is measured in this way it can be shown that any system of degrees of belief that violates the axioms of probability can be replaced by an alternative system that obeys the axioms and yet is more accurate in every possible world. Since epistemically rational agents must strive to hold accurate beliefs, this establishes conformity with the axioms of probability as a norm of epistemic rationality whatever its prudential merits or defects might be. (shrink)

According to Bayesian epistemology, the epistemically rational agent updates her beliefs by conditionalization: that is, her posterior subjective probability after taking account of evidence X, pnew, is to be set equal to her prior conditional probability pold(·|X). Bayesians can be challenged to provide a justification for their claim that conditionalization is recommended by rationality—whence the normative force of the injunction to conditionalize? There are several existing justifications for conditionalization, but none directly addresses the idea that conditionalization will be epistemically rational (...) if and only if it can reasonably be expected to lead to epistemically good outcomes. We apply the approach of cognitive decision theory to provide a justification for conditionalization using precisely that idea. We assign epistemic utility functions to epistemically rational agents; an agent’s epistemic utility is to depend both upon the actual state of the world and on the agent’s credence distribution over possible states. We prove that, under independently motivated conditions, conditionalization is the unique updating rule that maximizes expected epistemic utility. (shrink)

A challenge we face in a world that has been shaped by, and continues to be shaped by, racist attitudes and institutions is that the evidence is often stacked in favor of racist beliefs. As a result, we may find ourselves facing the following conflict: what if the evidence we have supports something we morally shouldn’t believe? For example, it is morally wrong to assume, solely on the basis of someone’s skin color, that they’re a staff member. But, what if (...) you’re in a context where, because of historical patterns of discrimination, someone’s skin color is a very good indicator that they’re a staff member? When this sort of normative conflict looms, a conflict between moral considerations on the one hand and what you epistemically ought to believe given the evidence on the other, what should we do? It might be unfair to assume that they’re a staff member, but to ignore the evidence would mean risking inaccurate beliefs. Some, notably Tamar Gendler (2011), have suggested that we simply face a tragic irresolvable dilemma. In this chapter, I consider how these cases of conflict arise and I canvass the viability of suggested resolutions of the conflict. In the end, I argue that there’s actually no conflict here. Moral considerations can change how we epistemically should respond to the evidence. (shrink)

According to Bayesian epistemology, rational learning from experience is consistent learning, that is learning should incorporate new information consistently into one's old system of beliefs. Simon M. Huttegger argues that this core idea can be transferred to situations where the learner's informational inputs are much more limited than Bayesianism assumes, thereby significantly expanding the reach of a Bayesian type of epistemology. What results from this is a unified account of probabilistic learning in the tradition of Richard Jeffrey's 'radical probabilism'. Along (...) the way, Huttegger addresses a number of debates in epistemology and the philosophy of science, including the status of prior probabilities, whether Bayes' rule is the only legitimate form of learning from experience, and whether rational agents can have sustained disagreements. His book will be of interest to students and scholars of epistemology, of game and decision theory, and of cognitive, economic, and computer sciences. (shrink)

I propose a general alethic theory of epistemic risk according to which the riskiness of an agent’s credence function encodes her relative sensitivity to different types of graded error. After motivating and mathematically developing this approach, I show that the epistemic risk function is a scaled reflection of expected inaccuracy. This duality between risk and information enables us to explore the relationship between attitudes to epistemic risk, the choice of scoring rules in epistemic utility theory, and the selection of priors (...) in Bayesian epistemology more generally. (shrink)