Newton’s earliest publications contained scandalous epistemological claims: not only did he aim for certainty; he also claimed success. Some commentators argue that Newton ultimately gave up claims of certainty in favor of a high degree of probability. I argue that no such shift occurred. I examine the evidence of a probabilistic shift: a passage from query 23/31 of the Opticks and rule 4 of the Principia. Neither passage supports a probabilistic approach to natural philosophy. The aim of certainty, then, was (...) an enduring feature of Newton’s methodology. (shrink)

Amidst long-running debates within the field, high energy physics has adopted a statistical methodology that primarily employs standard frequentist techniques such as significance testing and confidence interval estimation, but incorporates Bayesian methods for limited purposes. The discovery of the Higgs boson has drawn increased attention to the statistical methods employed within HEP. Here I argue that the warrant for the practice in HEP of relying primarily on frequentist methods can best be understood as pragmatic, in the sense that statistical methods (...) are chosen for their suitability for the practical demands of experimental HEP, rather than reflecting a commitment to a particular epistemic framework. In particular, I argue that understanding the statistical methodology of HEP through the perspective of pragmatism clarifies the role of and rationale for significance testing in the search for new phenomena such as the Higgs boson. (shrink)

In this article I develop a model of theoretical understanding in science. This is a philosophical theory that specifies the conditions that are both necessary and sufficient for a scientist to satisfy the construction ‘S understands theory T ’. I first consider how this construction is preferable to others, then build a model of the requisite conditions on the basis of examples from elementary physics. I then show how this model of theoretical understanding can be made philosophically robust and provide (...) a more sophisticated account than we see from models of a similar kind developed by those working in the psychology of physics and artificial intelligence. 1 Introduction2 The Explicandum/Analysandum3 Analysis of ‘S understands T’ 4 The Inferential Model4.1 Which problems are we talking about?4.2 Does the solution have to be true?4.3 Does each cycle have to be correct for S to understand T?4.4 What is problem-solving reliability?4.5 Does every specific inference of a cycle have to be correct?4.6 Does each inference have to use a principle that is part of the theory?5 Theoretical Understanding and Conceptual Expertise: Empirical Considerations6 Conclusion: Inferential Model of Scientific Understanding and Our Traditional Problems. (shrink)

Arguments from non-causal analogy form a distinctive class of analogical arguments in science not recognized in authoritative classifications by, e.g., Hesse and Bartha. In this paper, I illustrate this novel class of scientific analogies by means of historical examples from physics, biology and economics, at the same time emphasizing their broader significance for contemporary debates in epistemology.

Peter Achinstein has argued at length and on many occasions that the view according to which evidential support is defined in terms of probability-raising faces serious counterexamples and, hence, should be abandoned. Proponents of the positive probabilistic relevance view have remained unconvinced. The debate seems to be in a deadlock. This paper is an attempt to move the debate forward and revisit some of the central claims within this debate. My conclusion here will be that while Achinstein may be right (...) that his counterexamples undermine probabilistic relevance views of what it is for e to be evidence that h, there is still room for a defence of a related probabilistic view about an increase in being supported, according to which, if p > p, then h is more supported given e than it is without e. My argument relies crucially on an insight from recent work on the linguistics of gradable adjectives. (shrink)

Newton’s Regulae philosophandi—the rules for reasoning in natural philosophy—are maxims of causal reasoning and induction. This essay reviews their significance for Newton’s method of inquiry, as well as their application to particular propositions within the Principia. Two main claims emerge. First, the rules are not only interrelated, they defend various facets of the same core idea: that nature is simple and orderly by divine decree, and that, consequently, human beings can be justified in inferring universal causes from limited phenomena, if (...) only fallibly. Second, the rules make substantive ontological assumptions on which Newton’s argument in the Principia relies. (shrink)

This chapter examines issues surrounding inference to the best explanation, its justification, and its role in different arguments for scientific realism, as well as more general issues concerning explanations’ ontological commitments. Defending the reliability of inference to the best explanation has been a central plank in various realist arguments, and realists have drawn various ontological conclusions from the premise that a given scientific explanation best explains some phenomenon. This chapter stresses the importance of thinking carefully about the nature of explanation (...) in connection with evaluating realists’ appeals to explanatory reasoning and inference to the best explanation. (shrink)