In a recent paper,[3], Harold T. Walsh has argued that (1) Whewell's commentators have in the past misunderstood his use of “necessary,” that (2) Whewell's theory of necessary truth developed only gradually through thirty years of scholarly activity, finally finding a “mature” expression in Philosophy of Discovery, published in 1860, and that (3) a proper understanding of Whewell's “mature” theory of necessary truth leads to a fundamental re-interpretation of the nature of the Ideas and of their role in scientific systems—that the meaning of “necessity” as Whewell expresses it in Philosophy of Discovery leads, in short, to a new and more illuminating reading of his philosophy of inductive science. It is my view that the three propositions asserting Walsh's view are incorrect, expecially the two important and crucial ones (2) and (3). In what follows, I shall state what seem to me to be the grounds for rejecting Walsh's view that Whewell's theory of necessity was not arrived at before 1860, and hence for rejecting his interpretation of some basic aspects of Whewell's philosophy of science.

Predictivism asserts that where evidence E confirms theory T, E provides stronger support for T when E is predicted on the basis of T and then confirmed than when E is known before T's construction and ‘used’, in some sense, in the construction of T. Among the most interesting attempts to argue that predictivism is a true thesis (under certain conditions) is that of Patrick Maher (1988, 1990, 1993). The purpose of this paper is to investigate the nature of predictivism using Maher's analysis as a starting point. I briefly summarize Maher's primary argument and expand upon it; I explore related issues pertaining to the causal structure of empirical domains and the logic of discovery.

To date, both sides in the conventionality of simultaneity debate grant that transformations from “standard” to “nonstandard” coordinates are possible without any empirically significant effects. However, it is argued here that the very possibility of defining nonstandard coordinates vanishes if one represents special relativity, not by real four-vectors (as has been the case so far in the debate), but by complex spinors as used in the representation of half-integer spin. Thus, in the topologically simplest representation of the Lorentz group, the transformation over which the debate has been fought does not exist and one of the most basic foundations of the conventionalist thesis is challenged.

This paper proposes a revision of our understanding of causation that is designed to address what Hartry Field has suggested is the central problem in the metaphysics of causation today: reconciling Bertrand Russell's arguments that the concept of causation can play no role in the advanced sciences with Nancy Cartwright's arguments that causal concepts are essential to a scientific understanding of the world. The paper shows that Russell's main argument is, ironically, very similar to an argument that Cartwright has put forward against the truth of universal laws of nature. The paper uses this insight to develop an account of causation that does justice to traditional views yet avoids the arguments of Russell.

This paper examines the nomic covariationist strategy of using idealization to define representation. While the literature has focused upon the possibility of defining ideal conditions for perception, I argue that nomic covariationist appeals to idealization are pseudoscientific and contrary to a foundational and empirically well-supported methodological presupposition in cognitive science. Moreover, one major figure in this camp fails to come to grips with its role and its problems in mainstream science. Thus he forwards a false dichotomy of the sciences and treats idealization as a blank check written by scientists on an unknown bank. Finally, I consider and reject alternative formulations of the nomic covariationist's idealization strategy.

Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced. Philosophers have only begun to comprehend the changed nature of scientific reasoning in this age of “big data.” We analyze data-focused practices in biology and climate modeling, identifying distinct species of data-centric science: phenomena-laden in biology and phenomena-agnostic in climate modeling, each better suited for its own domain of application, though each entail trade-offs. We argue that data-centric practices in science are not monolithic because the opportunities and challenges presented by big data vary across scientific domains.

We identify a species of experiment—Kon-Tiki experiments—used to demonstrate the competence of a cause to produce a certain effect, and we examine their role in the historical sciences. We argue that Kon-Tiki experiments are used to test middle-range theory, to test assumptions within historical narratives, and to open new avenues of inquiry. We show how the results of Kon-Tiki experiments are involved in projective (rather than consequentialist) inferences, and we argue (against Kyle Stanford) that reliance on projective inferences does not provide historical scientists with any special protection against the problem of unconceived alternatives.

Substrate independence and mind-body functionalism claim that thinking does not depend on any particular kind of physical implementation. But real-world information processing depends on energy, and energy depends on material substrates. Biological evidence for these claims comes from ecology and neuroscience, while computational evidence comes from neuromorphic computing and deep learning. Attention to energy requirements undermines the use of substrate independence to support claims about the feasibility of artificial intelligence, the moral standing of robots, the possibility that we may be living in a computer simulation, the plausibility of transferring minds into computers, and the autonomy of psychology from neuroscience.

Glymour has developed an account of the confirmation of scientific hypotheses which he advocates as an alternative to the hypothetico-deductive and Bayesian accounts. This account is subject to a counter-example which may be accomodated by a slight modification. So modified it describes an important dimension of confirmation. If the modification of Glymour's account is slightly extended, both the resulting account and the hypothetico-deductive account may be seen as special cases of a Bayesian theory which is immune to Glymour's criticisms.

The requirement of total evidence is a mainstay of Bayesian epistemology. Peter Fisher Epstein argues that the requirement generates mistaken conclusions about several examples that he devises. Here we examine the example of Epstein’s that we find most interesting and argue that Epstein’s analysis of it is flawed.

Theoretical problems concerning concepts of systems and measurement of behavior were encountered during experimental studies of the effects of electrical stimulation of the brain on the social behavior of a monkey colony. General problems involved in the description and measurement of behavior of natural systems, and especially of organisms are discussed.

In animals with differentiated brain the general process of stimulation may be divided into four subprocesses: input, throughput, transput and output.

Categories of behavior, temporal and spatial units, and logical rules of homogeneity for sorting data and comparing measurements and categories, are discussed. Simple behavioral units are divided into: 1. Individual a. static or postural b. dynamic or gestural 2. Social a. static b. dynamic. Complex behavior is divided into: 1. Simultaneous 2. Sequential 3. Syntactic 4. Roles.

The process of interpretation of data by the observer is assumed to be dependent upon actual behavior of the system, techniques of observation and experimentation, theoretical and technical processes of analysis, and synthesis and integration of data. Expression of behavior in mathematical terms insures greater accuracy than descriptive methods and enables data to be processed with computers.

The question is raised, what is going on when there is a tendency for something to happen, but it does not happen. A conspicuous case is that in which there is an equilibrium between opposing forces. One answer, given, for example, by Anscombe and Geach, Harré, and Bhaskar, is that such tendencies are in a sense real, though not manifest. The other answer, given, for example, by Braithwaite and Mackie, is that statements of tendencies are conditional statements about what is manifest but ones in which some antecedents are not specified. The first of these answers is criticized and the second defended.

Janssen argues that special relativity is preferable to Lorentzian dynamics due to its kinematic structure. Brown, along with others, raises an objection, arguing that a dynamical understanding of special relativity is explanatorily prior and hence more fundamental than the principle theory-based kinematic structure of Minkowski space-time. This article challenges this objection, arguing that both Janssen and Brown miss the essential aspect of the principles of special relativity that underwrite its interpretational success. It is not its kinematic structure but the constitutive nature of the principles it employs, by providing a coherent conceptual framework, that does the foundational work.

Combining quantum mechanics with special relativity requires (i) that a spacetime representation of quantum states be found; (ii) that such states, represented as extended along equal-time hyperplanes, be invariant when transformed from one frame to another; and (iii) that collapses of states be instantaneous in every frame. These requirements are met using branching spacetime, in which probabilities of outcomes are represented by the numerical proportions of branches on which the outcomes occur. Quantum states of systems are then identified with the probability values, built into spacetime along spacelike hypersurfaces, of all possible outcomes of all possible tests to which the systems can be subjected.

A physical theory is a construction of thought which is founded on experience so as to constitute knowledge of the natural world. Propositions in physics are constituted of concepts which express the properties and processes of the physical world. For purposes of record and communication concepts are designated by the terms of a language, such as mathematical symbols, and philosophical discussion may be based on linguistic forms. In this essay, however, the element of discussion will be the concept as a mode of thought through which are grasped the attributes of physical things and the relations exemplified in physical processes. The present philosophical analysis of a physical theory, quantum mechanics, is to be carried out by critical analysis of its basic concepts. In particular, the program is to show that in quantum mechanics the concept of physical object exhibits the characters of discontinuity, complementarity, and relativity.

The paper, analyses the role of measurable concepts in deductive explanation. It is shown that such concepts are, although imprecise in a defined sense, exact in that neutral candidates to them do not arise. An analysis is given of the way in which imprecision is related to generalisation, and it is shown how imprecise concepts are incorporated in testable deductive explanations.

From late 1684 through mid-1685, Isaac Newton turned to developing and refining the conceptual foundations presupposed by his emerging physics. Analysis of his manuscripts from this period reveals that Newton’s understanding of the relativity of acceleration led him to seek a spatiotemporally invariant quantity of matter. He found two such quantities and then designed an experiment to discover their relationship. Interpreting the experiment, however, required distinguishing a new notion of force. Others have recognized the conceptual distinction between inertial and gravitational mass. I show here that Newton clearly saw this distinction and that he provisionally established their equivalence.

Standard models of statistical explanation face two intractable difficulties. In his 1984 Salmon argues that because statistical explanations are essentially probabilistic we can make sense of statistical explanation only by rejecting the intuition that scientific explanations are contrastive. Further, frequently the point of a statistical explanation is to identify the etiology of its explanandum, but on standard models probabilistic explanations often fail to do so. This paper offers an alternative conception of statistical explanations on which explanations of the frequency of a property consist in the derivation of that frequency from a statistical specification of the mechanism by which instances of the relevant property are produced. Such explanations are contrastive precisely because they identify the determinate causal etiologies of their explananda.