Enjoying great popularity in decision theory, epistemology, and philosophy of science, Bayesianism as understood here is fundamentally concerned with epistemically ideal rationality. It assumes a tight connection between evidential probability and ideally rational credence, and usually interprets evidential probability in terms of such credence. Timothy Williamson challenges Bayesianism by arguing that evidential probabilities cannot be adequately interpreted as the credences of an ideal agent. From this and his assumption that evidential probabilities cannot be interpreted as the actual credences of human (...) agents either, he concludes that no interpretation of evidential probabilities in terms of credence is adequate. I argue to the contrary. My overarching aim is to show on behalf of Bayesians how one can still interpret evidential probabilities in terms of ideally rational credence and how one can maintain a tight connection between evidential probabilities and ideally rational credence even if the former cannot be interpreted in terms of the latter. By achieving this aim I illuminate the limits and prospects of Bayesianism. (shrink)

On an evidentialist position, it is epistemically rational for us to believe propositions that are (stably) supported by our total evidence. We are epistemically permitted to believe such propositions, and perhaps even ought to do so. Epistemic rationality is normative. One popular way to explain the normativity appeals to epistemic teleology. The primary aim of this paper is to argue that appeals to epistemic teleology do not support that we ought to believe what is rational to believe, only that we (...) are permitted to do so. In arguing for that, I defend an epistemic teleological position that is radical in nature. It involves no commitment to aiming at the truth. I conclude by dispelling some worries that have been raised about my position. (shrink)

In everyday life and in science we acquire evidence of evidence and based on this new evidence we often change our epistemic states. An assumption underlying such practice is that the following EEE Slogan is correct: 'evidence of evidence is evidence' (Feldman 2007, p. 208). We suggest that evidence of evidence is best understood as higher-order evidence about the epistemic state of agents. In order to model evidence of evidence we introduce a new powerful framework for modelling epistemic states, Dyadic (...) Bayesianism. Based on this framework, we then discuss characterizations of evidence of evidence and argue for one of them. Finally, we show that whether the EEE Slogan holds, depends on the specific kind of evidence of evidence. (shrink)

How should an agent revise her epistemic state in the light of doxastic disagreement? The problems associated with answering this question arise under the assumption that an agent’s epistemic state is best represented by her degree of belief function alone. We argue that for modeling cases of doxastic disagreement an agent’s epistemic state is best represented by her confirmation commitments and the evidence available to her. Finally, we argue that given this position it is possible to provide an adequate answer (...) to the question of how to rationally revise one’s epistemic state in the light of disagreement. (shrink)

This entry provides an opinionated overview of key epistemological debates regarding experts. To comprehend, justify, and enhance our practices of trusting, utilising, and depending on experts' judgments, it is crucial to clarify the characteristics of experts and the means of identifying those who exemplify them. Consequently, this entry examines and evaluates accounts of the main characteristics of experts. Furthermore, it discusses indicators of experts that help recognise experts and considers to what extent they are accessible to other experts and laypersons.

This entry provides an opinionated overview of central debates surrounding doxastic disagreement, focusing on doxastic states or attitudes such as beliefs and credences. It differentiates between various types of epistemologically significant disagreement based on the agents involved and the source of the disagreement. It also examines and evaluates current accounts of how peers should (rationally) address disagreement and highlights the fundamental principles that support these accounts.

A characterization of epistemic rationality, or epistemic justification, is typically taken to require a process of conceptual clarification, and is seen as comprising the core of a theory of (epistemic) rationality. I propose to explicate the concept of rationality. -/- It is essential, I argue, that the normativity of rationality, and the purpose, or goal, for which the particular theory of rationality is being proposed, is taken into account when explicating the concept of rationality. My position thus amounts to an (...) instrumentalist position about theories of epistemic rationality. Since there are different purposes, or goals, for which theories of rationality are proposed, the method of explication leaves room for different characterizations of rationality. I focus on two such (kinds of) purposes: first, the purpose of guiding the formation (or maintenance) of doxastic states and, second, the purpose of assessing (the formation or maintenance of) doxastic states. I conclude by outlining a pluralistic picture concerning rationality. (shrink)

When members of a group doxastically disagree with each other, decisions in the group are often hard to make. The members are supposed to find an epistemic compromise. How do members of a group reach a rational epistemic compromise on a proposition when they have different (rational) credences in the proposition? I answer the question by suggesting the Fine-Grained Method of Aggregation, which is introduced in Brössel and Eder 2014 and is further developed here. I show how this method faces (...) challenges of the standard method of aggregation, Weighted Straight Averaging, in a successful way. One of the challenges concerns the fact that Weighted Straight Averaging does not respect the evidential states of agents. Another challenge arises because Weighted Straight Averaging does not account for synergetic effects. (shrink)

I focus on the No-Paradise Dilemma, which results from some initially plausible epistemic ideals, coupled with an assumption concerning our evidence. Our evidence indicates that we are not in an epistemic paradise, in which we do not experience cognitive failures. I opt for a resolution of the dilemma that is based on an evidentialist position that can be motivated independently of the dilemma. According to this position, it is rational for an agent to believe a proposition on the agent’s total (...) evidence just in case the (total) evidence stably supports the proposition. Based on this evidentialist position, I argue that it is not an epistemic ideal in the actual world that we hold rational beliefs that are logically equivalent to our rational beliefs. The dilemma is resolved by giving up this ideal for the actual world and adopting the evidentialist position in terms of stable evidential support. (shrink)