I discuss a broad critique of the classical approach to the foundations of statistical mechanics (SM) offered by N. S. Krylov. He claims that the classical approach is in principle incapable of providing the foundations for interpreting the “laws” of statistical physics. Most intriguing are his arguments against adopting a de facto attitude towards the problem of irreversibility. I argue that the best way to understand his critique is as setting the stage for a positive theory which treats SM as a theory in its own right, involving a completely different conception of a system's state. As the orthodox approach treats SM as an extension of the classical or quantum theories (one which deals with large systems), Krylov is advocating a major break with the traditional view of statistical physics.

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.

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.

The literature of economic theory, like that of philosophy, abounds in prefaces and prolegomena. Methodology and analysis of concepts take an important place in a science which has not found the sure path of development. But there is no sure path for methodology either. The selfconscious methodology of social science has been largely a borrowing from that of physical science, where procedures have developed to a stage of considerable maturity. But the analogy falls down where guidance is most needed, at the points where social science is most likely to develop new concepts and new types of structure. Philosophers have not been lacking, indeed, to belittle the entire enterprise, and to deny the possibility of anything that could strictly be called social science.

The investigation of probabilistic causality has been plagued by a variety of misconceptions and misunderstandings. One has been the thought that the aim of the probabilistic account of causality is the reduction of causal claims to probabilistic claims. Nancy Cartwright (1979) has clearly rebutted that idea. Another ill-conceived idea continues to haunt the debate, namely the idea that contextual unanimity can do the work of objective homogeneity. It cannot. We argue that only objective homogeneity in combination with a causal interpretation of Bayesian networks can provide the desired criterion of probabilistic causality.

The causal theory of reference is often taken to provide a solution to the problems, such as incomparability and referential discontinuity, that the meaning-change thesis raised. I show that Kuhn successfully questioned the causal theory and Putnam's idea that reference is determined via the sameness relation of essences that holds between a sample and other members of a kind in all possible worlds. Putnam's single ‘essential’ properties may be necessary but not sufficient to determine membership in a kind category. Kuhn argued that extension is fixed by similarity-dissimilarity relations that are liable to change in taxonomic reorganizations of science.

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.

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.

This paper contrasts two information-theoretic approaches to statistical explanation: namely, (1) an analysis, which originated in my earlier research on problems of testing stochastic models of learning, based on an entropy-like measure of expected transmitted-information (and here referred to as the Expected-Information Model), and (2) the analysis, which was proposed by James Greeno (and which is closely related to Wesley Salmon's Statistical Relevance Model), based on the information-transmitted-by-a-system. The substantial differences between these analyses can be traced to the following basic difference. On Greeno's view, the essence of explanation lies in the relevance relations expressed by the conditional probabilities that relate the explanans variables to the explanandum variables; on my view, in contrast, the essence of explanation lies in theories viewed as hypothetical structures which deductively entail conditional probability distributions linking the explanans variables and the explanandum variables. The explanatory power of a stochastic theory is identified with information (regarding the values of explanandum variables) which is “absorbed from” the explanans variables. While other information which is “absorbed from” the explanandum variables (through the process of parameter estimation, for example) reflects descriptive power of the theory. I prove that Greeno's measure of transmitted information is a limiting special case of the E-I model, but that the former, unlike the latter, makes no distinction between explanatory power and descriptive power.

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.

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.

The literary and poetic turn in philosophy exudes contempt for science and hostility against technology, both allegedly justified on grounds established by Nietzsche and Heidegger. I try to show that these grounds instead call for an extremely positive assessment of science and technology. It turns out that what Nietzsche and Heidegger describe as our highest achievement, namely, human autonomy, is really made possible by modern experimental physics. And I show how this assessment is borne out by what Heisenberg describes as the lesson of quantum physics.

Some have suggested that certain classical physical systems have undecidable long-term behavior, without specifying an appropriate notion of decidability over the reals. We introduce such a notion, decidability in μ (or d-μ) for any measure μ, which is particularly appropriate for physics and in some ways more intuitive than Ko's (1991) recursive approximability (r.a.). For Lebesgue measure λ, d-λ implies r.a. Sets with positive λ-measure that are sufficiently “riddled” with holes are never d-λ but are often r.a. This explicates Sommerer and Ott's (1996) claim of uncomputable behavior in a system with riddled basins of attraction. Furthermore, it clarifies speculations that the stability of the solar system (and similar systems) may be undecidable, for the invariant tori established by KAM theory form sets that are not d-λ.

Ronald Giere's analysis of causal effectiveness in populations involves the comparison of two hypothetical populations, one in which every individual has the suspected causal factor, and the other in which none do. Elliott Sober has argued that in cases where causal effectiveness depends on relative population sizes, Giere's analysis breaks down. I take issue with this claim, and argue to the contrary that Giere's analysis can help provide insight into these cases.

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 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.

The disagreement with regard to the validity of the principle of causality, existing to-day among scientists, has its roots in the diversity of definitions of the principle itself rather than in a problematic scientific situation. As far as the formulation of quantum theory is complete its bearing upon philosophical questions can be fixed with precision provided the questions are phrased intelligibly. But a question is intelligible from a scientific point of view only if it satisfies two conditions: (1) the meaning of its terms must be fixed; (2) it must be in accord with the conventions of the science to which the question is put.