This paper develops a stronger version of ‘inference-to-the-best explanation’ scientific realism. I argue against three standard assumptions of current realists: (1) realism is confirmed if it provides the best explanation of theories’ predictive success; (2) the realist claim that successful theories are always approximately true provides the best explanation of their success; and (3) realists are committed to giving the same sort of truth-based explanation of superseded theories’ success that they give to explain our best current theories’ success. On the positive side, I argue that (1) the confirmation of realism requires explaining theories’ explanatory success, not just their predictive success; (2) in turn this task requires a richer realist model of explanation that brings into the explanans both (a) successful theories’ epistemic virtues (e.g., unification and simplicity) and (b) the standards governing these virtues, as well as truth; (3) this richer realist model is further confirmed because it can better explain the success of theories in gaining wide acceptance among scientists; and (4) the model is further supported because it is superior to ‘preservative realism’ in providing a plausible rebuttal of the pessimistic meta-induction from the many past successful-but-false theories to the likelihood that our best current theories are likewise false.

Scientific method interests the linguist not only as it interests every scientific worker, but also in a special way, because the scientist, as part of his method, utters certain very peculiar speech-forms. The linguist naturally divides scientific activity into two phases: the scientist performs “handling” actions (observation, collecting of specimens, experiment) and utters speech (report, classification, hypothesis, prediction). The speech-forms which the scientist utters are peculiar both in their form and in their effect upon hearers.

Thomas Kuhn's The Structure of Scientific Revolutions was published at the beginning of what has come to be known as “the cognitive revolution.” With hindsight one can construct significant parallels between the problems of knowledge, perception, and learning with which Kuhn and cognitive scientists were grappling and between the accounts developed by each. However, by and large Kuhn never utilized the research in cognitive science—especially in cognitive psychology—that we believe would have furthered his own paradigm. This is puzzling since he did not have the traditional philosophical aversion to “psychologizing” and in fact drew on insights from psychology to support the most radical claims in Structure, such as the “Gestalt switch” nature of conceptual change. Indeed, the research program outlined there seems intrinsically historical, philosophical, and psychological and Kuhn's work has had considerable influence on research in cognitive science.

It is not easy to assess the extent to which decision theory constitutes a contribution to philosophy. On the one hand, the enthusiasts seem tirelessly to churn out highly technical Bayesian reconstructions of scientific practice. On the other hand, their opponents dismiss the result as a body of theory that is overidealized and falsely precise, bearing no important relation to the actual conduct of inquiry. And most philosophers seem happy to ignore the debate altogether and pursue a theory of inquiry as best they can. I take the opponents’ criticism seriously, but I am still an enthusiast. The aim of this paper is to explain why. The paper consists of an accessible argument for a Bayesian decision theory designed to enable any interested philosopher to see how the insights of decision theory provide, without idealization or false precision, a fundamental (and somewhat revolutionary) contribution to our understanding of the enterprise of rational inquiry.

Chance Neutrality is the thesis that, conditional on some proposition being true (or being false), its chance of being true should be a matter of practical indifference. The aim of this article is to examine whether Chance Neutrality is a requirement of rationality. We prove that given Chance Neutrality, the Principal Principle entails a thesis called Linearity; the centerpiece of von Neumann and Morgenstern’s expected utility theory. With this in mind, we argue that the Principal Principle is a requirement of practical rationality but that Linearity is not and, hence, that Chance Neutrality is not rationally required.

Most philosophical work on causal selection examines the selection of single causes and aims to clarify what grounds, if any, justify this selection. Such analyses overlook the fact that in scientific contexts multiple factors are often selected as the important causes of some outcome. This analysis examines a multicausal case in which factors in causal pathways are selected in biological explanations. This work provides a novel analysis of the pathway concept, its role in causal selection, and the rationale behind this selection. It is argued that this rationale is guided by principled considerations, which have been overlooked in the extant literature.

The “usual story” regarding molecular chemistry is that it is roughly an application of quantum mechanics. That is to say, quantum mechanics supplies everything necessary and sufficient, both ontologically and epistemologically, to reduce molecular chemistry to quantum mechanics. This is a reductive story, to be sure, but a key explanatory element of molecular chemistry, namely molecular structure, is absent from the quantum realm. On the other hand, typical characterizations of emergence, such as the unpredictability or inexplicability of molecular structure based on quantum mechanics, do not characterize the relationship between molecular chemistry and quantum mechanics well either. A different scheme for characterizing reduction and emergence is proposed that accommodates the relationship between quantum mechanics and molecular chemistry and some initial objections to the scheme are considered.

One controversy about the existence of so called evolutionary forces such as natural selection and random genetic drift concerns the sense in which such “forces” can be said to interact. In this paper I explain how natural selection and random drift can interact. In particular, I show how population-level probabilities can be derived from individual-level probabilities, and explain the sense in which natural selection and drift are embodied in these population-level probabilities. I argue that whatever causal character the individual-level probabilities have is then shared by the population-level probabilities, and that natural selection and random drift then have that same causal character. Moreover, natural selection and drift can then be viewed as two aspects of probability distributions over frequencies in populations of organisms. My characterization of population-level probabilities is largely neutral about what interpretation of probability is required, allowing my approach to support various positions on biological probabilities, including those which give biological probabilities one or another sort of causal character.

“He's [the Red King's] dreaming now,” said Tweedledee: “and what do you think he's dreaming about?”

Alice said, “Nobody can guess that.”

“Why, about you!” Tweedledee exclaimed, clapping his hands triumphantly. “And if he left off dreaming about you, where do you suppose you'd be?”

“Where I am now, of course,” said Alice.

“Not you!” Tweedledee retorted contemptuously. “You'd be nowhere. Why, you're only a sort of thing in his dream!”

“If that there King was to wake,” added Tweedledum, “you'd go out—bang!—just like a candle.”

“I shouldn't!” Alice exclaimed indignantly. “Besides, if I'm only a sort of thing in his dream, what are you, I should like to know?”

“Ditto,” said Tweedledum.

“Ditto, ditto!” cried Tweedledee.

He shouted this so loud that Alice couldn't help saying “Hush! You'll be waking him, I'm afraid, if you make so much noise.”

“Well, it's no use your talking about waking him,” said Tweedledum, “when you're only one of the things in his dream. You know very well you're not real.”

“I am real!” said Alice, and began to cry.

“You won't make yourself a bit realler by crying,” Tweedledee remarked: “there's nothing to cry about.”

“If I wasn't real,” Alice said—half laughing through her tears, it all seemed so ridiculous —“I shouldn't be able to cry.”

“I hope you don't suppose those are real tears?” Tweedledum interrupted in a tone of great contempt.

—Through the Looking Glass

The boundaries of psychology have never been very distinctly defined and, as a consequence, science has witnessed frequent border incidents. But it obviously is not psychology alone which suffers from such lack of delineation, but its neighbors, the biological and social sciences, do as well. Cooperation between sciences becomes difficult under these conditions. All agree that psychology is the science of mind, but few agree to what “mind” is. At least within our century “mind” has been taken to be “behavior”, for the present day outlook is that mind is behavior rather than that which is responsible for behavior (3). The metaphysical dynamism of a prime mover has been replaced by an experimental concept, i.e., one that is susceptible to scientific investigation. There is no need to review the complex history of behavioristic psychology in its many ramifications, ranging from Watson's mechanism to the various vitalistic teleologies. We turn instead to a fundamental problem on which there is little agreement: “What kind of behavior should be called mind?”

Much controversy surrounds the question of what ought to be the proper definition of ‘singularity’ in general relativity, and the question of whether the prediction of such entities leads to a crisis for the theory. I argue that a definition in terms of curve incompleteness is adequate, and in particular that the idea that singularities correspond to ‘missing points’ has insurmountable problems. I conclude that singularities per se pose no serious problem for the theory, but their analysis does bring into focus several problems of interpretation at the foundation of the theory often ignored in the philosophical literature.

Structural realist interpretations of generally relativistic spacetimes have recently come to enjoy a remarkable degree of popularity among philosophers. I present a challenge to these structuralist interpretations that arises from considering cosmological models in general relativity. As a consequence of their high degree of spacetime symmetry, these models resist a structuralist interpretation. I then evaluate the various strategies available to the structuralist to react to this challenge.

Psychiatry’s failure to validate its diagnostic constructs is often attributed to the prioritizing of reliability over validity in the Diagnostic and Statistical Manual of Mental Disorders (DSM). I argue that a more powerful way in which the DSM has retarded biomedical progress is by encouraging unwarranted optimism about diagnostic discrimination: the assumption that our diagnostic tests group patients together in ways that allow for relevant facts about mental disorder to be discovered. I argue that the Research Domain Criteria (RDoC) framework, a new paradigm for classifying objects of psychiatric research, solves some of the challenges brought on by this assumption.

Modelling became one of the primary tools of mathematical economic research in the twentieth century, but when we look at examples of how nonanalogical models were first built in economics, both the process of making representations and aspects of the representing relation remain opaque. Like early astronomers, economists have to imagine how the hidden parts of their world are arranged and to make images, that is, create models, to represent how they work. The case of the Edgeworth Box, a model widely used for theoretical work in twentieth-century economics, provides a good example to explore the process of making mathematical representations of the economy. It shows how, in making these new representations, conceptual elements were developed which could not have been represented in the older verbal forms of economics.

According to standard (quantum) statistical mechanics, the phenomenon of a phase transition, as described in classical thermodynamics, cannot be derived unless one assumes that the system under study is infinite. This is naturally puzzling since real systems are composed of a finite number of particles; consequently, a well-known reaction to this problem was to urge that the thermodynamic definition of phase transitions (in terms of singularities) should not be “taken seriously.” This article takes singularities seriously and analyzes their role by using the well-known distinction between data and phenomena, in an attempt to better understand the origin of the puzzle.

Motor imagery typically involves an experience as of moving a body part. Recent studies reveal close parallels between the constraints on motor imagery and those on actual motor performance. How are these parallels to be explained? We advance a simulative theory of motor imagery, modeled on the idea that we predict and explain the decisions of others by simulating their decision-making processes. By proposing that motor imagery is essentially off-line motor action, we explain the tendency of motor imagery to mimic motor performance. We close by arguing that a simulative theory of motor imagery gives (modest) support to and illumination of the simulative theory of decision-prediction.

Bayesian probabilistic explication of inductive inference conflates neutrality of supporting evidence for some hypothesis H (“not supporting H”) with disfavoring evidence (“supporting not-H”). This expressive inadequacy leads to spurious results that are artifacts of a poor choice of inductive logic. I illustrate how such artifacts have arisen in simple inductive inferences in cosmology. In the inductive disjunctive fallacy, neutral support for many possibilities is spuriously converted into strong support for their disjunction. The Bayesian “doomsday argument” is shown to rely entirely on a similar artifact.

Why do gases reach equilibrium when left to themselves? The canonical answer, originally proffered by Boltzmann, is that the systems have to be ergodic. This answer is now widely regarded as flawed. We argue that some of the main objections in particular arguments based on the Kolmogorov-Arnold-Moser theorem and the Markus-Meyer theorem are beside the point. We then argue that something close to Boltzmann’s proposal is true: gases behave thermodynamic-like if they are epsilon-ergodic, that is, ergodic on the phase space except for a small region of measure epsilon. This answer is promising because there is evidence that relevant systems are epsilon-ergodic.