The interventionist account of causation has been largely dismissed as a serious candidate for application in physics. This dismissal is related to the problematic assumption that physical causation is entirely a matter of dynamical evolution. In this article, I offer a fresh look at the interventionist account of causation and its applicability to thermodynamics. I argue that the interventionist account of causation is the account of causation that most appropriately characterizes the theoretical structure and phenomenal behavior of thermodynamics.

An example shows that ‘pairwise preferences’ (certain hypothetical choices) can cycle even when rational. General considerations entail that preferences tout court (certain relations of actual valuations) cannot cycle. A world-bayesian theory is explained that accommodates these two kinds of preference, and a theory for rational actions that would have them maximize and be objects of ratifiable choices. It is observed that choices can be unratifiable either because of troublesome credences or because of troublesome preferences. An appendix comments on a third way in which efforts to maximize can be frustrated.

It is the contemporary belief, in American legal circles at all events, that law or jurisprudence, whatever it may have been in the past, has now the status of a social science. This is an assumption easier to make than to substantiate, and in view of the increasing insistence upon this point, it is now appropriate to inquire whether or not it possesses a tangible foundation. This requires a consideration of the distinctive characteristics of social science, the determination whether or not jurisprudence exhibits these characteristics, and, if not, if it is possible for it to assume them. It will be convenient to begin, briefly and concisely, with an examination of the characteristics of modern legal thought.

Paul Feyerabend recommended the methodological policy of proliferating competing theories as a means to uncovering new empirical data, and thus as a means to increase the empirical constraints that all theories must confront. Feyerabend's policy is here defended as a clear consequence of connectionist models of explanatory understanding and learning. An earlier connectionist “vindication” is criticized, and a more realistic and penetrating account is offered in terms of the computationally plastic cognitive profile displayed by neural networks with a recurrent architecture.

In this paper we investigate Accardi's claim that the “quantum paradoxes” have their roots in probability theory and that, in particular, they can be evaded by giving up Bayes' rule, concerning the relation between composite and conditional probabilities. We reach the conclusion that, although it may be possible to give up Bayes' rule and define conditional probabilities differently, this contributes nothing to solving the philosophical problems which surround quantum mechanics.

In a recent paper [2] David Lewis offered an extremely interesting and, if correct, important solution to the main unsolved problem of Carnap's program for inductive logic—the choice of an appropriate C-function (=an inductive method). The gist of Lewis' solution is to first obtain a pilot sample from the target population (a time-honored procedure in statistical methodology) and then select, on the basis of this sample, from among the immodestλ-methods. An immodest inductive method is one which estimates that the mean squared error of its estimates of population relative frequencies is less than or equal that of any other inductive method. By great ingenuity Lewis apparently shows that on any evidence e there is one and only one immodest λ-method. I will attempt to show that Lewis made a conceptual error in formulating the equations to be solved and that when it is corrected it can be easily demonstrated that all admissible λ-methods are immodest and hence that Lewis' criterion is of no value.

The Wittgensteinian thesis that “the result of philosophy is not a number of ‘philosophical propositions,’ but to make propositions clear” has been given various interpretations by subsequent philosophers. Thus, for example, certain British philosophers have confined themselves to the analysis of colloquial language and have developed great skill in sophistical demonstrations intended to show that there is no such thing as a philosophical problem. Another group of philosophers considers language solely as a phenomenon of human behavior and attempts to clarify confusions and misunderstandings within language by utilizing the statistical techniques of contemporary behavior psychology. Still other philosophers, under the impact of logical positivism, have maintained that the clarification of propositions entails the construction of a language with precise rules of formation, transformation, and reference into which one could translate all the non-contradictory portions of ordinary spoken language.

Here we address four objections raised by Julien Deonna, John Michael, and Francesca Fardo against a recent account of empathy for pain (by Frédérique de Vignemont and Tania Singer and Vignemont and Pierre Jacob). First, to what extent must the empathizer share her target’s affective state? Second, how can one interpret neuroscientific findings on vicarious pain in light of recent results challenging the notion of a pain matrix? Third, can one offer a simpler account of how empathy makes one aware of another’s emotion? Finally, to what extent can this account of empathy for pain be generalized to empathy for emotions?

Robert MacArthur's mathematical ecology is often regarded as ahistorical and has been criticized by historically oriented ecologists and philosophers for ignoring the importance of history. I clarify and defend his approach, especially his use of simple mathematical models to explain patterns in data and to generate predictions that stimulate empirical research. First I argue that it is misleading to call his approach ahistorical because it is not against historical explanation. Next I distinguish three kinds of criticism of his approach and argue that his approach is compatible with the first two of them. Finally, I argue that the third kind of criticism, advanced by Kim Sterelny and Paul Griffiths, is largely irrelevant to MacArthur's approach.

This article presents a new argument for incorporating a distinction between default and deviant values into the formalism of causal models. The argument is based on considerations about how causal reasoners should represent disagreement over causes, and it is defended against an objection that has been raised against earlier arguments for defaults.

In practice, scientists must convey data in a “representational style” (e.g., as a numerical array or visual representation). Various authors seek to explain the epistemic role of scientific visual representation in terms of formal conventions (e.g., Goodman, Perini, and Kulvicki). Goodman also tends to dismiss the epistemic relevance of human cognition. My position is that visual conventions are nonarbitrary, in that they play to scientists’ cognitive abilities and limitations. My account draws on Perini's formal analysis, scientific case studies, and empirical literature on global pattern detection in neurotypicals, autistics, and dyslexics.

Reductionist accounts of objective chance rely on a notion of fit, which ties the chances at a world to the frequencies at that world. Here, I criticize extant measures of the fit of a chance system and draw on recent literature in epistemic utility theory to propose a new model: chances fit a world insofar as they are accurate at that world. I show how this model of fit does a better job of explaining the normative features of chance, its role in the laws of nature, and its status as an expert function than do previous accounts.

Imagine that we are on a train, playing with some mechanical systems. Why can’t we detect any differences in their behavior when the train is parked versus when it is moving uniformly? The standard answer is that boosts are symmetries of Newtonian systems. In this article, I use the case of a spring to argue that this answer is problematic because symmetries are neither sufficient nor necessary for preserving its behavior. I also develop a new answer according to which boosts preserve the relational properties on which the behavior of a system depends, even when they are not symmetries.

Mixed-methods research (MMR)—the combination of qualitative and quantitative data within the same design to strengthen causal inference—is gaining prominence in the social sciences but its benefits are contested. There remains confusion over which methods to mix and what is the point of mixing them. We argue that variety of evidence is what matters, not of data or methods, and that distinct epistemic principles underlie its added value for causal inference. The centrality of evidential variety also implies that strong causal pluralism is untenable as a foundation for MMR.

It can not be our purpose to give here a complete account of the phenomenological history of mathematical doctrine. It will be enough to refer to the battle of opinions in mathematical theory which was waged within the last eighty-five years, since Riemann's inaugural lecture on Non-Euclidean Geometry. Furthermore, the revolution which Einstein's theory of general relativity created is indicative of the complete absence of any general awareness that mathematics as a science has any relation to social reality. If we are going to establish such relations, we are conscious of the fact that they would be hotly contended by many mathematicians.

In their paper, “Vexing Expectations,” Nover and Hájek (2004) present an allegedly paradoxical betting scenario which they call the Pasadena Game (PG). They argue that the silence of standard decision theory concerning the value of playing PG poses a serious problem. This paper provides a threefold response. First, I argue that the real problem is not that decision theory is “silent” concerning PG, but that it delivers multiple conflicting verdicts. Second, I offer a diagnosis of the problem based on the insight that standard decision theory is, rightly, sensitive to order. Third, I describe a new betting scenario—the Alternating St. Petersburg Game—which is genuinely paradoxical. Standard decision theory is silent on the value of playing this game even if restrictions are placed on the order in which the various alternative payoffs are summed.

This article considers the recent work of David Lewis, Ned Hall, and Michael Thau on chance and the best system account of law. My rejoinder suggests that, though their proposals may succeed at the stated goal of reworking the problematic relationship between chance, credence, and law, the resulting account of law suffers from an even more significant difficulty. To oversimplify a bit, I argue that their account is unable to handle the nomic necessity of laws. My criticisms suggest an alternative account of law which provides, I argue, a preferable account of nomic necessity.

This paper defends a social functionalist interpretation, modeled on psychological functionalism, of the meanings of social facts. Social functionalism provides a better explanation of the possibility of interpreting other cultures than approaches that identify the meanings of social facts with either mental states or behavior. I support this claim through a functionalist reinterpretation of sociological accounts of the categories that identify them with their collective representations. Taking the category of causality as my example, I show that if we define it instead in terms of its functional relations to moral rules, it becomes easier to recognize in other cultures.