I argue that when we use ‘probability’ language in epistemic contexts—e.g., when we ask how probable some hypothesis is, given the evidence available to us—we are talking about degrees of support, rather than degrees of belief. The epistemic probability of A given B is the mind-independent degree to which B supports A, not the degree to which someone with B as their evidence believes A, or the degree to which someone would or should believe A if they had B as (...) their evidence. My central argument is that the degree-of-support interpretation lets us better model good reasoning in certain cases involving old evidence. Degree-of-belief interpretations make the wrong predictions not only about whether old evidence confirms new hypotheses, but about the values of the probabilities that enter into Bayes’ Theorem when we calculate the probability of hypotheses conditional on old evidence and new background information. (shrink)

The quantitative problem of old evidence is the problem of how to measure the degree to which e confirms h for agent A at time t when A regards e as justified at t. Existing attempts to solve this problem have applied the e-difference approach, which compares A's probability for h at t with what probability A would assign h if A did not regard e as justified at t. The quantitative problem has been widely regarded as unsolvable primarily on (...) the grounds that the e-difference approach suffers from intractable problems. Various philosophers have proposed that 'Bayesianism' should be rejected as a research strategy in confirmation theory in part because of the unsolvability of this problem. I develop a version of the e-difference approach which overcomes these problems and possesses various advantages (but also certain limitations). I develop an alternative 'theistic' approach which handles many cases that my development of the e-difference approach does not handle. I conclude with an assessment of the significance of the quantitative problem for Bayesianism and argue that this problem is misunderstood in so far as it is regarded as unsolvable, and in so far as it is regarded as a problem only for Bayesians. (shrink)

How should we reason in science? Jan Sprenger and Stephan Hartmann offer a refreshing take on classical topics in philosophy of science, using a single key concept to explain and to elucidate manifold aspects of scientific reasoning. They present good arguments and good inferences as being characterized by their effect on our rational degrees of belief. Refuting the view that there is no place for subjective attitudes in 'objective science', Sprenger and Hartmann explain the value of convincing evidence in terms (...) of a cycle of variations on the theme of representing rational degrees of belief by means of subjective probabilities (and changing them by Bayesian conditionalization). In doing so, they integrate Bayesian inference—the leading theory of rationality in social science—with the practice of 21st century science. Bayesian Philosophy of Science thereby shows how modeling such attitudes improves our understanding of causes, explanations, confirming evidence, and scientific models in general. It combines a scientifically minded and mathematically sophisticated approach with conceptual analysis and attention to methodological problems of modern science, especially in statistical inference, and is therefore a valuable resource for philosophers and scientific practitioners. (shrink)

Wenmackers and Romeijn (2016) formalize ideas going back to Shimony (1970) and Putnam (1963) into an open-minded Bayesian inductive logic, that can dynamically incorporate statistical hypotheses proposed in the course of the learning process. In this paper, we show that Wenmackers and Romeijn’s proposal does not preserve the classical Bayesian consistency guarantee of merger with the true hypothesis. We diagnose the problem, and offer a forward-looking open-minded Bayesians that does preserve a version of this guarantee.

One of the most troubling and persistent challenges for Bayesian Confirmation Theory is the Problem of Old Evidence. The problem arises for anyone who models scientific reasoning by means of Bayesian Conditionalization. This article addresses the problem as follows: First, I clarify the nature and varieties of the POE and analyze various solution proposals in the literature. Second, I present a novel solution that combines previous attempts while making weaker and more plausible assumptions. Third and last, I summarize my findings (...) and put them into the context of the general debate about POE and Bayesian reasoning. (shrink)

It is reasonable to take Kierkegaard and Plantinga as presenting very different approaches to the rationality of adopting religious beliefs. Kierkegaard says Christian doctrines are absurd, and Plantinga argues that the existence of God is part of the deliverances of reason. I argue that in spite of these apparent differences, Kierkegaard and Plantinga agree on some foundational epistemological issues. I begin by exploring the topic of radical conversion, as discussed by van Fraassen. I use the notion of radical conversion as (...) a tool, to focus our investigation and illuminate the agreements between Kierkegaard and Plantinga. Because of the role of passions and affections in epistemology, we will see that Kierkegaard and Plantinga share a basic epistemological outlook. (shrink)

Charles Chihara has presented a problem he claims Bayesian confirmation theory cannot handle. Chihara gives examples in which he claims the change in belief cannot be construced as conditionalizing on new evidence. These are situations in which the agent suddenly thinks of new possibilities. I propose a solution that incorporates the important ideas of Bayesian theory. In particular, I present a principle which shows that the change of belief in Chihara's example is due to simple conditionalization.

Bayesian confirmation theory, as traditionally interpreted, treats the temporal relationship between the formulation of a hypothesis and the confirmation (or recognition) of evidence entailed by that hypothesis merely as a component of the psychology of discovery and acceptance of a hypothesis. The temporal order of these events is irrelevant to the logic of rational theory choice. A few years ago Richmond Campbell and Thomas Vinci offered a reinterpretation of Bayes' Theorem in defense of the view that the temporal relationship between (...) hypothesis and evidence really does matter. More specifically, they advocated the thesis that successful predictions implied by scientific hypothesis H will increase the degree of confirmation of H only if they are novel predictions in the following sense; Evidence E is heuristically novel with respect to hypothesis H if and only if H was not deliberately designed to explain E (if E has already been corroborated) or in anticipation of E (if E is regarded as likely to be corroborated at some future date). Campbell and Vinci argue that the traditional interpretation of Bayes' Theorem misconstrues the significance of predictive novelty by ignoring heuristic novelty. In this paper I review the formal component of their preferred interpretation and demonstrate that it fails to establish that heuristic novelty has any special effect on theory confirmation. That is, even on their revisionist interpretation, regardless of whether H was deliberately designed to explain known evidence E (or in anticipation of suspected E), or whether H was designed without any awareness of the entailment relationship between H and E, Bayes' Theorem will generate the same epistemic probability for hypothesis H. Cases where new evidence was not foreseen at the time a hypothesis first emerged do not carry any more epistemic weight than they would have if such evidence had been foreseen. * Research on this paper was supported by an NEH summer grant. My thanks also to Richmond Campbell and Thomas Vinci for their reply to my initial evaluation of their position (in private correspondence), which compelled me to reconsider their arguments more carefully. (shrink)

Confirmation is commonly identified with positive relevance, E being said to confirm H if and only if E increases the probability of H. Today, analyses of this general kind are usually Bayesian ones that take the relevant probabilities to be subjective. I argue that these subjective Bayesian analyses are irremediably flawed. In their place I propose a relevance analysis that makes confirmation objective and which, I show, avoids the flaws of the subjective analyses. What I am proposing is in some (...) ways a return to Carnap's conception of confirmation, though there are also important differences between my analysis and his. My analysis includes new accounts of what evidence is and of the indexicality of confirmation claims. Finally, I defend my analysis against Achinstein's criticisms of the relevance concept of confirmation. (shrink)

Contrary to what has been widely supposed, Bayesian theory deals successfully with the introduction of new theories that have never previously been entertained. The theory enables us to say what sorts of method should be used to assign probabilities to these new theories, and it allows that the probabilities of existing theories may be modified as a result.

Bayesian confirmation theory is a leading theory to decide the confirmation/refutation of a hypothesis based on probability calculus. While it may be much discussed in philosophy of science, is it actually practiced in terms of hypothesis testing by scientists? Since the assignment of some of the probabilities in the theory is open to debate and the risk of making the wrong decision is unknown, many scientists do not use the theory in hypothesis testing. Instead, they use alternative statistical tests that (...) can measure the risk or the reliability in decision making, circumventing some of the theoretical problems in practice. Therefore, the theory is not very popular in hypothesis testing among scientists at present. However, there are some proponents of Bayesian hypothesis testing, and software packages are made available to accelerate utilization by scientists. Time will tell whether Bayesian confirmation theory can become both a leading theory and a widely practiced method. In addition, this theory can be used to model the belief of scientists when testing hypotheses. (shrink)

This paper discusses an aspect of the problem of old evidence which I call here the general problem of old evidence. The probability of old evidence is one or close to one, because background information K entails the evidence E or K consists of propositions which make E probable. In the literature, K is considered as a proposition relevant to E. Based on examples, I argue that K does not support the truth of E; instead, K supports the evidential status (...) of E. I define background information as a set of propositions necessary and sufficient to consider E as the evidence of hypothesis H. Background information is relevant to the bearing of E on H, but not to the truth of E itself. My definition of background information implies that background information of E is probabilistically independent of E; that is, in the case of old evidence, neither P = 1, nor P ≈ 1. (shrink)

This paper offers an answer to Glymour's ‘old evidence’ problem for Bayesian confirmation theory, and assesses some of the objections, in particular those recently aired by Chihara, that have been brought against that answer. The paper argues that these objections are easily dissolved, and goes on to show how the answer it proposes yields an intuitively satisfactory analysis of a problem recently discussed by Maher. Garber's, Niiniluoto's and others’ quite different answer to Glymour's problem is considered and rejected, and the (...) paper concludes with some brief reflections on the prediction/accommodation issue. (shrink)

To ascertain the rational credences for the epistemic agents in the famous cases of self-locating belief, one should model the processes by which those agents acquire their evidence. This approach, taken by Darren Bradley (Phil. Review 121, 149–177) and Joseph Halpern (Ergo 2, 195–206), is immensely reasonable. Nevertheless, the work of those authors makes it seem as if this approach must lead to such conclusions as the Doomsday argument being correct, and that Sleeping Beauty should be a halfer. I argue (...) that this is due to an implicit existential bias: it is assumed that the first step in those processes is the determination that the agent in question must necessarily exist. It is much more reasonable to model that determination as contingent and a result of other, earlier, steps in the process. This paper offers such alternative models. They imply an endorsement of what has mockingly been called “presumptuous” reasoning, and a massive shift of credences in favor of (1) the existence of a multiverse and (2) the Everettian interpretation of quantum mechanics. (shrink)

Agustín Rayo’s The Construction of Logical Space offers an exciting and ambitious defense of a broadly Carnapian approach to metaphysics. This essay will focus on one of the main differences between Rayo’s and Carnap’s approaches. Carnap distinguished between analytic, a priori “meaning postulates”, and empirical claims, which were both synthetic and knowable only a posteriori. Like meaning postulates, they determine the boundaries of logical space. But Rayo is skeptical that the a priori/a posteriori or analytic/synthetic distinctions can do the work (...) Carnap wanted them to, so unlike meaning postulates, ‘just is’-statements aren’t assumed to be analytic or knowable a priori. This essay will concern the epistemology of ‘just is’-statements in Rayo’s picture. If not by a priori reflection, how can we determine which ones to accept? I’ll distinguish two competing strands in Rayo’s work. The less radical, Lewisian strand holds that the question of whether to accept a ‘just is’-statement can be addressed in a neutral, non-question-begging way, by a kind of cost-benefit analysis. The more radical, Kuhnian strand holds that there can be no ‘just is’-statement-independent, rational choice of which ‘just is’-statements to accept. I argue that Rayo faces strong internal pressure to adopt the Kuhnian picture. While it is possible for Rayo to resist these Kuhnian pressures, natural strategies for doing so leave his view more similar to Carnap’s than the above gloss suggested. (shrink)

It would be good to have a Bayesian decision theory that assesses our decisions and thinking according to everyday standards of rationality — standards that do not require logical omniscience (Garber 1983, Hacking 1967). To that end we develop a “fragmented” decision theory in which a single state of mind is represented by a family of credence functions, each associated with a distinct choice condition (Lewis 1982, Stalnaker 1984). The theory imposes a local coherence assumption guaranteeing that as an agent's (...) attention shifts, successive batches of "obvious" logical information become available to her. A rule of expected utility maximization can then be applied to the decision of what to attend to next during a train of thought. On the resulting theory, rationality requires ordinary agents to be logically competent and to often engage in trains of thought that increase the unification of their states of mind. But rationality does not require ordinary agents to be logically omniscient. (shrink)

I discuss a contemporary solution to the dynamic problem of old evidence (POE), as proposed by Sprenger. Sprenger’s solution combines the Garber–Jeffrey–Niiniluoto (GJN) approach with Howson’s suggestion of counterfactually removing the old evidence from scientists’ belief systems. I argue that in the dynamic POE, the challenge is to explain how an insight under beliefs in which the old evidence E is known increased the credence of a scientific hypothesis. Therefore, Sprenger’s counterfactual solution, in which E has been artificially removed, does (...) not resolve the problem. I consider several potential responses. (shrink)