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.

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.

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.

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.

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.

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.

Mathematics is often taken to play one of two roles in the empirical sciences: either it represents empirical phenomena or it explains these phenomena by imposing constraints on them. This article identifies a third and distinct role that has not been fully appreciated in the literature on applicability of mathematics and may be pervasive in scientific practice. I call this the “bridging” role of mathematics, according to which mathematics acts as a connecting scheme in our explanatory reasoning about why and how two different descriptions of an empirical phenomenon relate to each other. I discuss two bridging roles appearing in biological and physical explanations.

In 1932, John von Neumann argued for the equivalence of the thermodynamic entropy and −Trρlnρ, since known as the von Neumann entropy. Meir Hemmo and Orly R. Shenker recently challenged this argument by pointing out an alleged discrepancy between the two entropies in the single-particle case, concluding that they must be distinct. In this article, their argument is shown to be problematic as it (a) allows for a violation of the second law of thermodynamics and (b) is based on an incorrect calculation of the von Neumann entropy.

According to David Lewis’s Principal Principle, our beliefs about the objective chances of outcomes (typically) determine our rational credences in those outcomes. Lewis influentially argues that any adequate metaphysics of objective chance must explain why the Principal Principle holds. Since no theory of chance is widely agreed to have met this burden, I suggest we change tack. On the view I develop, a central aspect of the Principal Principle holds not because of what objective chances are but rather because of the explanatory role that objective chances play for the rational agents who believe in them.

Although Hale and Resnik are correct in their specific objection to my proposal for nominalizing science, the proposal can be saved by means of a simple and plausible modification.

In a recent article in Philosophy of Science, De Vignemont and Jacob defend the view that empathy involves interpersonal similarity between an empathizer and a target person with respect to internal affective states. Focusing on empathy for pain, they propose a theory of the neural substrate of pain empathy. We point out several flaws in their interpretation of the data and argue that currently available data do not differentiate between De Vignemont and Jacob’s model and alternative models. Finally, we offer some suggestions about how this might be achieved in future research.

I discuss the behavior of the Akaike Information Criterion in the limit when the sample size grows. I show the falsity of the claim made recently by Stanley Mulaik in Philosophy of Science that AIC would not distinguish between saturated and other correct factor analytic models in this limit. I explain the meaning and demonstrate the validity of the familiar, more moderate criticism that AIC is not a consistent estimator of the number of parameters of the smallest correct model. I also give a short explanation why this feature of AIC is compatible with the motives for using it.