This paper examines Descartes' problematic relational theory of motion, especially when viewed within the context of his dynamics, the Cartesian natural laws. The work of various commentators on Cartesian motion is also surveyed, with particular emphasis placed upon the recent important texts of Garber and Des Chene. In contrast to the methodology of most previous interpretations, however, this essay employs a modern “spacetime” approach to the problem. By this means, the role of dynamics in Descartes' theory, which has often been neglected in favor of kinematic factors, is shown to be central to finding a solution to the puzzle of Cartesian motion.

The Hard Problem of the mind is addressed and it is argued that physical-phenomenal property identities have the same status as the identification of an ostended bit of physical space and the coordinates assigned the spot on a map of the terrain. It is argued, that is to say, that such identities are, or follow from, stipulations which interpret the map.

Hempel's high probability requirement asserts that any rationally acceptable answer to the question ‘Why did event X occur?‘ must offer information which shows that X was to be expected at least with reasonable probability. Salmon rejected this requirement in his S-R model. This led to a series of paradoxical consequences, such as the assertion that an explanation of an event can both lower its probability and make it arbitrarily low, and the assertion that the explanation of an outcome would have qualified as an explanation of its non-occurrence as well.

We argue that if inductive explanations are to be seen as generalizations of the causal-deterministic model, or if they are to be seen as satisfying the requirement—fulfilled by the D-N model—that explanations ought to identify certain features of the universe that are nomically responsible for the explanadum event, then the high probability requirement seems to be unacceptable. If this is so, a realistically inspired theory of inductive explanation will be committed to the paradoxes that follow from Salmon's model.

This paper concerns the problem of how to give historical explanations of scientist's decisions to prefer one theory over another. It is argued that such explanations ought to contain only statements about the beliefs and preferences of the agents involved, and, in particular, ought not to include evaluative premises about the theories themselves. It is argued that Lakatos's attempt to build into such historical explanations premises of an evaluative kind is deficient. The arguments of Laudan to the effect that such explanations depend crucially upon evaluative assumptions about the rationality or irrationality of decisions are examined. It is argued that they do not establish the need for such assumptions.

Similar criticisms are then shown to be applicable to a version of the ‘hermeneutical’ model of explanation for human actions.

This portion of the essay concludes a two-part paper, Part I of which appeared in an earlier issue of this Journal. Part II begins with a careful study of the quantum description of real experiments in order to motivate a proposal that two distinct quantum theoretical measurement constructs should be recognized, both of which must be distinguished from the concept of preparation. The different epistemological roles of these concepts are compared and explained. It is then concluded that the only possible type of “quantum measurement theory” is one of little metaphysical interest and that quantum measurement seems problematical only when viewed from an overly narrow classical perspective.

In accepting the invitation of your society to discuss some aspects of the philosophy of science, particularly as they might bear on the problems of an experimental psychologist, I should like from the start to make it very clear that I do not consider myself a philosopher, not even an amateur one. Like most empirical scientists, particularly of the laboratory variety, I usually take it for granted that my world is real and that the things I perceive as existing in it can be investigated by my empirical methods. I seem to get along quite well with this unpremeditated, common-sense realism, and, for the most part, I go along minding my own experimental business. As the psychologists among you may know, however, every once in a while I seem to have to abandon this happy, naive state and let myself get involved in questions about my science, the kinds of questions that are more properly the business of the philosopher of science.

There are two reasons, I claim, scientists do and should ignore standard philosophical theories of objective evidence: (1) Such theories propose concepts that are far too weak to give scientists what they want from evidence, viz., a good reason to believe a hypothesis; and (2) They provide concepts that make the evidential relationship a priori, whereas typically establishing an evidential claim requires empirical investigation.

In their paper “Why Gibbs Phase Averages Work—The Role of Ergodic Theory” (1980), David Malament and Sandy Zabell attempt to explain why phase averaging over the microcanonical ensemble gives correct predictions for the values of thermodynamic observables, for an ergodic system at equilibrium. Their idea is to bypass the traditional use of limit theorems, by relying on a uniqueness result about the microcanonical measure—namely, that it is uniquely stationary translation-continuous. I argue that their explanation begs questions about the relationship between thermodynamic equilibrium and statistical equilibrium; I argue in addition that any account which supports their view of the relationship between these two notions of equilibrium will likely use the limit theorems in traditional ways, and thereby bypass the explanation they offer.

The thermodynamics of computation assumes that computational processes at the molecular level can be brought arbitrarily close to thermodynamic reversibility and that thermodynamic entropy creation is unavoidable only in data erasure or the merging of computational paths, in accord with Landauer’s principle. The no-go result shows that fluctuations preclude completion of thermodynamically reversible processes. Completion can be achieved only by irreversible processes that create thermodynamic entropy in excess of the Landauer limit.

This article considers the evolution of Feigl's attempt at establishing a stable form of scientific realism. I will argue that Feigl's work in that area should be appreciated for two reasons: (1) it represents a telling case against the view of there being an unbridgeable ‘analytic-continental divide’ in the context of twentieth-century philosophy; (2) it contradicts the idea that scientific realism is at odds with logical empiricism. It will be shown that Feigl developed his scientific realist position from within the logical empiricists’ Vienna Circle. This, in turn, necessitates a fresh approach toward the contemporary scientific realism debate.

Wright’s “adaptive landscape” has been influential in evolutionary thinking but controversial, especially because the landscape that organisms encounter is altered by the evolutionary process itself and the effects organisms have on their environments. Lewontin offered a mathematical heuristic describing the coupling of niche construction and adaptive evolution. Here, we propose a “dual landscape” model to view these relationships. Our model represents change as simultaneous movement on two landscapes, each a function of phenotype and environment. This model clarifies the evolutionary feedback generated by niche construction. We relate our model to Lewontin’s niche construction equations and illustrate it with three examples.

The standard philosophical definition of placebos offered by Grünbaum is incompatible with Cartwright’s conception of randomized clinical trials. I offer a modified account of placebos that respects this role and clarifies why many current medical trials fail to warrant the conclusions they are typically seen as yielding. I then consider recent changes to guidelines for reporting medical trials and show that pessimism over parsing out the cause of “unblinding” is premature. Specifically, using a trial of antidepressants, I show how more sophisticated statistical analyses can parse out the source of such effects and serve as an alternative to placebo control.

Stem cells are defined as having capacities for both self-renewal and differentiation. Many different entities satisfy this working definition. I show that this general stem cell concept is relative to a cell lineage, temporal duration, and characters of interest. Experiments specify values for these variables. So claims about stem cells must be understood in terms of experimental methods used to identify them. Furthermore, the stem cell concept imposes evidential constraints on interpretation of experimental results. From these constraints, it follows that claims about stem cell capacities are inherently uncertain. This result has important implications for stem cell research.

I first propose to distinguish between two kinds of external validity inferences, predictive and explanatory. I then argue that we have a satisfactory answer to the question of the conditions under which predictive external validity inferences are good. If this claim is correct, then it has two immediate consequences: First, some external validity inferences are deductive, contrary to what is commonly assumed. Second, Steel’s requirement that an account of external validity inference break what he calls the ‘Extrapolator’s Circle’ is misplaced, at least when it comes to predictive external validity inferences.

This article examines the adequacy of causal graph theory as a tool for modeling biological phenomena. I argue that the causal graph approach reaches its limits when it comes to modeling biological phenomena that involve complex spatial and chemical-structural relations. Using a case study from molecular biology, I show why causal graph models fail to adequately represent and explain biological phenomena of this kind. The inadequacy of these models is due to their failure to include relevant spatial-structural information in a way that does not render the models nonexplanatory, unmanageable, or inconsistent with basic assumptions of causal graph theory.

Neuroeconomics is a research program founded on the thesis that cognitive and neurobiological data constitute evidence for answering economic questions. I employ confirmation theory in order to reject arguments both for and against neuroeconomics. I also emphasize that some arguments for neuroeconomics will not convince the skeptics because these arguments make a contentious assumption: economics aims for predictions and deep explanations of choices in general. I then argue for neuroeconomics by appealing to a much more restrictive (and thereby skeptic-friendly) characterization of the aims of economics.

In the social sciences we hardly can create laboratory conditions, we only can try to find out which kinds of experiments Nature has carried out. Knowledge about Nature's designs can be used to infer conditions for reliable predictions. This problem was explicitly dealt with in Haavelmo's (1944) discussion of autonomous relationships, Friedman's (1953) as-if methodology, and Simon's (1961) discussions of nearly-decomposable systems. All three accounts take Marshallian partitioning as starting point, however not with a sharp ceteris paribus razor but with the blunt knife of negligibility assumptions. As will be shown, in each account reflection on which influences are negligible, for what phenomena and for how long, played a central role.

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.