This paper analyzes Deborah Mayo's recent criticism of use-novelty requirement. She claims that her severity criterion captures actual scientific practice better than use-novelty, and that use-novelty is not a necessary condition for severity. Even though certain cases in which evidence used for the construction of the hypothesis can test the hypothesis severely, I do not think that her severity criterion fits better with our intuition about good tests than use-novelty. I argue for this by showing a parallelism in terms of severity between the confidence interval case and what she calls ‘gellerization’. To account for the difference between these cases, we need to take into account certain additional considerations like a systematic neglect of relevant alternatives.

A (normalized) interpolant I in Craig's theorem is a kind of explanation why the consequence relation (from F to G) holds. This is because I is a summary of the interaction of the configurations specified by F and G, respectively, that shows how G follows from F. If explaining E means deriving it from a background theory T plus situational information A and if among the concepts of E we can separate those occurring only in T or only in A, then the interpolation theorem applies in two different ways yielding two different explanations and two different covering laws.

Van Fraassen (1989) argues that Inference to the Best Explanation is incoherent in the sense that adopting it as a rule for belief change will make one susceptible to a dynamic Dutch book. The present paper argues against this. A strategy is described that allows us to infer to the best explanation free of charge.

Charles S. Peirce argued that, besides deduction and induction, there is a third mode of inference which he called “hypothesis” or “abduction.” He characterized abduction as reasoning “from effect to cause,” and as “the operation of adopting an explanatory hypothesis.” Peirce's ideas about abduction, which are related also to historically earlier accounts of heuristic reasoning (the method of analysis), have been seen as providing a logic of scientific discovery. Alternatively, abduction is interpreted as giving reasons for pursuing a hypothesis. Inference to the best explanation (IBE) has also been regarded as an important mode of justification, both in everyday life, detective stories, and science. In particular, scientific realism has been defended by an abductive nomiracle argument (Smart, Putnam, Boyd), while the critics of realism have attempted to show that this appeal to abduction is question-begging, circular, or incoherent (Fine, Laudan, van Fraassen). This paper approaches these issues by distinguishing weaker and stronger forms of abduction, and by showing how these types of inferences can be given Peircean and Bayesian probabilistic reconstructions.

There seem to be some very good reasons for a philosopher of science to be a deductivist about scientific reasoning. Deductivism is apparently connected with a demand for clarity and definiteness in the reconstruction of scientists' reasonings. And some philosophers even think that deductivism is the way around the problem of induction. But the deductivist image is challenged by cases of actual scientific reasoning, in which hard-to-state and thus discursively ill-defined elements of thought nonetheless significantly condition what practitioners accept as cogent argument. And arguably, these problem cases abound. For example, even geometry—for most of its history—was such a problem case, despite its exactness and rigor. It took a tremendous effort on the part of Hilbert and others, to make geometry fit the deductivist image. Looking to the empirical sciences, the problems seem worse. Even the most exact and rigorous of empirical sciences—mechanics—is still the kind of problem case which geometry once was. In order for the deductivist image to fit mechanics, Hilbert's sixth problem (for mechanics) would need to be solved. This is a difficult, and perhaps ultimately impossible task, in which the success so far achieved is very limited. I shall explore some consequences of this for realism as well as for deductivism. Through discussing links between non-monotonicity, skills, meaning, globality in cognition, models, scientific understanding, and the ideal of rational unification, I argue that deductivists can defend their image of scientific reasoning only by trivializing it, and that for the adequate illumination of science, insights from anti-deductivism are needed as much as those which come from deductivism.

A first step is taken towards articulating a constructive empiricist philosophy of mathematics, thus extending van Fraassen's account to this domain. In order to do so, I adapt Field's nominalization program, making it compatible with an empiricist stance. Two changes are introduced: (a) Instead of taking conservativeness as the norm of mathematics, the empiricist countenances the weaker notion of quasi-truth (as formulated by da Costa and French), from which the formal properties of conservativeness are derived; (b) Instead of quantifying over spacetime regions, the empiricist only admits quantification over occupied regions, since this is enough for his or her needs.

In this paper I offer an ‘integrating account’ of singular causation, where the term ‘integrating’ refers to the following program for analysing causation. There are two intuitions about causation, both of which face serious counterexamples when used as the basis for an analysis of causation. The ‘process’ intuition, which says that causes and effects are linked by concrete processes, runs into trouble with cases of ‘misconnections’, where an event which serves to prevent another fails to do so on a particular occasion and yet the two events are linked by causal processes. The chance raising intuition, according to which causes raise the chance of their effects, easily accounts for misconnections but faces the problem of chance lowering causes, a problem easily accounted for by the process approach. The integrating program attempts to provide an analysis of singular causation by synthesising the two insights, so as to solve both problems.

In this paper I show that extant versions of the integrating program due to Eells, Lewis, and Menzies fail to account for the chance-lowering counterexample. I offer a new diagnosis of the chance lowering case, and use that as a basis for an integrating account of causation which does solve both cases. In doing so, I accept various assumptions of the integrating program, in particular that there are no other problems with these two approaches. As an example of the process account, I focus on the recent CQ theory of Wesley Salmon (1997).

David Lewis's best-system analysis of laws of nature is perhaps the best known sophisticated regularity theory of laws. Its strengths are widely recognized, even by some of its ablest critics. Yet it suffers from what appears to be a glaring weakness: It seems to grant an arbitrary privilege to the standards of our own scientific culture. I argue that by reformulating, or reinterpreting, Lewis's exposition of the best-system analysis, we arrive at a view that is free of this weakness. The resulting theory of laws has the surprising consequence that the term “law of nature” is indexical.

I consider the problem of extending Reichenbach's principle of the common cause to more than two events, vis-à-vis an example posed by Bernstein. It is argued that the only reasonable extension of Reichenbach's principle stands in conflict with a recent proposal due to Horwich. I also discuss prospects of the principle of the common cause in the light of these and other difficulties known in the literature and argue that a more viable version of the principle is the one provided by Penrose and Percival (1962).

Doppelt (1986,1990), Siegel (1990), and Rosenberg (1996) argue that the pivotal feature of Laudan's normative naturalism, namely his axiology, lacks a naturalistic foundation. In this paper I show that this objection turns on a misunderstanding of Laudan's use of the term ‘naturalism’. Specifically, I argue that there are two important senses of naturalism running through Laudan's work. Once these two strands are made explicit, the objection raised by Doppelt and others simply disappears.

Though many agree that we need to account for the role that social factors play in inquiry, developing a viable social epistemology has proved to be difficult. According to Longino, it is the processes that make inquiry possible that are aptly described as “social,” for they require a number of people to sustain them. These processes, she claims, not only facilitate inquiry, but also ensure that the results of inquiry are more than mere subjective opinions, and thus deserve to be called “knowledge.” In this paper, I (a) explain Longino's epistemology, and (b) defend it against charges that have recently been raised by Kitcher, Schmitt, and Solomon. Longino rightly recognizes that not all social factors have the same (adverse) affect on inquiry. She also recommends that we distinguish knowledge from mere opinion by reference to a social standard.