Abstract

My goal in this dissertation is to start a conversation about the role of risk in the decision-theoretic assessment of partial beliefs or credences in formal epistemology. I propose a general theory of epistemic risk in terms of relative sensitivity to different types of graded error. The approach I develop is broadly inspired by the pragmatism of the American philosopher Charles Sanders Peirce and his notion of the ``economy of research.'' I express this framework in information-theoretic terms and show that epistemic risk, so understood, is dual to information entropy. As a result, every unit increase in risk comes with a corresponding unit decrease in information entropy and epistemic risk may be expressed in terms of entropic change. I explain the significance of this for the selection of priors and the Laplacian principle of indifference. I also extend this notion of epistemic risk to the assessment of updating rules, where a similar duality between risk and information holds. In the dynamic context, epistemic risk is given by cross-entropic change. Here I explore the relationship between risk, the Value of Knowledge Theorem, dynamic coherence, and the role of expected accuracy in the selection of update rules. Finally, I apply these considerations to a social institution where attitudes to error are especially salient -- namely, legal decision-making -- and argue that considerations regarding the relative severity of different types of error are central to understanding evidentiary burdens of proof and the probative value of statistical evidence.