A number of cases involving self-locating beliefs have been discussed in the Bayesian literature. I suggest that many of these cases, such as the sleeping beauty case, are entangled with issues that are independent of self-locating beliefs per se. In light of this, I propose a division of labor: we should address each of these issues separately before we try to provide a comprehensive account of belief updating. By way of example, I sketch some ways of extending Bayesianism in order (...) to accommodate these issues. Then, putting these other issues aside, I sketch some ways of extending Bayesianism in order to accommodate self-locating beliefs. Finally, I propose a constraint on updating rules, the "Learning Principle", which rules out certain kinds of troubling belief changes, and I use this principle to assess some of the available options. (shrink)

This paper examines three accounts of the sleeping beauty case: an account proposed by Adam Elga, an account proposed by David Lewis, and a third account defended in this paper. It provides two reasons for preferring the third account. First, this account does a good job of capturing the temporal continuity of our beliefs, while the accounts favored by Elga and Lewis do not. Second, Elga’s and Lewis’ treatments of the sleeping beauty case lead to highly counterintuitive consequences. The proposed (...) account also leads to counterintuitive consequences, but they’re not as bad as those of Elga’s account, and no worse than those of Lewis’ account. (shrink)

Reasoning from inconclusive evidence, or ‘induction’, is central to science and any applications we make of it. For that reason alone it demands the attention of philosophers of science. This Element explores the prospects of using probability theory to provide an inductive logic, a framework for representing evidential support. Constraints on the ideal evaluation of hypotheses suggest that overall support for a hypothesis is represented by its probability in light of the total evidence, and incremental support, or confirmation, indicated by (...) an increased probability given the evidence than otherwise. This proposal is shown to have the capacity to reconstruct many canons of the scientific method and inductive inference. Along the way, significant objections are discussed, such as the challenge from inductive scepticism and the objection that the probabilistic approach makes evidential support arbitrary. (shrink)

According to the Feigl–Reichenbach–Salmon–Schurz pragmatic justification of induction, no predictive method is guaranteed or even likely to work for predicting the future; but if anything will work, induction will work—at least when induction is employed at the meta-level of predictive methods in light of their track records. One entertains a priori all manner of esoteric prediction methods, and is said to arrive a posteriori at the conclusion, based on the actual past, that object-level induction is optimal. Schurz’s refinements largely solve (...) the notorious short-run problem. A difficulty is noted, however, related to short-run worries but based on localized disagreement about the past, a feature characteristic of real debates (especially early modern) involving induction in intellectual history. Given the evidence about past events, _unfiltered by induction_, meta-induction might support a partly non-inductive method—especially as judged by proponents of esoteric prediction methods, who presumably believe that their methods have worked. Thus induction is justified meta-inductively in contexts where it was uncontroversial, while not obviously justified in key contexts where it has been disputed. This objection, momentarily sensed by Reichenbach regarding clairvoyance, is borne out by the Stoics’ use of meta-induction to justify both science and divination and by ancient Hebrew examples of meta-induction. Schurz’s recently introduced criteria for acceptance of testimony play a crucial role in arriving at object-level induction using meta-induction, but one might question them. Given the need for judgment in accepting testimony, it is unclear that the subjectivity of Howson’s Bayesian answer to Hume’s problem is overcome. (shrink)

Around 97% of climate scientists endorse anthropogenic global warming (AGW), the theory that human activities are partly responsible for recent increases in global average temperatures. Clearly, this widespread endorsement of AGW is a reason for non-experts to believe in AGW. But what is the epistemic significance of the fact that some climate scientists do not endorse AGW? This paper contrasts expert unanimity, in which virtually no expert disagrees with some theory, with expert consensus, in which some non-negligible proportion either rejects (...) or is uncertain about the theory. It is argued that, from a layperson’s point of view, an expert consensus is often stronger evidence for a theory’s truth than unanimity. Several lessons are drawn from this conclusion, e.g. concerning what laypeople should infer from expert pronouncements, how journalists should report on scientific theories, and how working scientists should communicate with the public. (shrink)

Many philosophers have argued that a hypothesis is better confirmed by some data if the hypothesis was not specifically designed to fit the data. ‘Prediction’, they argue, is superior to ‘accommodation’. Others deny that there is any epistemic advantage to prediction, and conclude that prediction and accommodation are epistemically on a par. This paper argues that there is a respect in which accommodation is superior to prediction. Specifically, the information that the data was accommodated rather than predicted suggests that the (...) data is less likely to have been manipulated or fabricated, which in turn increases the likelihood that the hypothesis is correct in light of the data. In some cases, this epistemic advantage of accommodation may even outweigh whatever epistemic advantage there might be to prediction, making accommodation epistemically superior to prediction all things considered. (shrink)

It is often suggested that disagreement among scientific experts is a reason not to trust those experts, even about matters on which they are in agreement. In direct opposition to this view, I argue here that the very fact that there is disagreement among experts on a given issue provides a positive reason for non-experts to trust that the experts really are justified in their attitudes towards consensus theories. I show how this line of thought can be spelled out in (...) three distinct frameworks for non-deductive reasoning: namely, Bayesian Confirmation Theory, Inference to the Best Explanation, and Inferential Robustness Analysis. (shrink)

How should our beliefs change over time? The standard answer to this question is the Bayesian one. But while the Bayesian account works well with respect to beliefs about the world, it breaks down when applied to self-locating or de se beliefs. In this work I explore ways to extend Bayesianism in order to accommodate de se beliefs. I begin by assessing, and ultimately rejecting, attempts to resolve these issues by appealing to Dutch books and chance-credence principles. I then propose (...) and examine several accounts of the dynamics of de se beliefs. These examinations suggest that an extension of Bayesianism to de se beliefs will require some uncomfortable choices. I conclude by laying out the options available, and assessing the prospects of each. (shrink)

Bayesian confirmation theories might be the best standing theories of confirmation to date, but they are certainly not paradox-free. Here I recognize that BCTs’ appeal mainly comes from the fact that they capture some of our intuitions about confirmation better than those the- ories that came before them and that the superiority of BCTs is suffi- ciently justified by those advantages. Instead, I will focus on Sylvan and Nola’s claim that it is desirable that our best theory of confirmation be (...) as paradox-free as possible. For this reason, I will show that, as they respond to different interests, the project of the BCTs is not incompatible with Sylvan and Nola’s project of a paradox-free confirmation logic. In fact, it will turn out that, provided we are ready to embrace some degree of non-classicality, both projects complement each other nicely. (shrink)

In this paper, I give an overview of different models of understanding attribution and advance a contextualist account of understanding attribution. Whereas other contextualist accounts make the degree in which the epistemic states of the relevant agents satisfy certain invariant conditions context-sensitive, the proposed account makes the conditions themselves context-sensitive.