Classical mechanics is empirically successful because the probabilistic mean values of quantum mechanical observables follow the classical equations of motion to a good approximation (Messiah 1970, 215). We examine this claim for the one-dimensional motion of a particle in a box, and extend the idea by deriving a special case of the ideal gas law in terms of the mean value of a generalized force used to define “pressure.” The examples illustrate the importance of probabilistic averaging as a method of abstracting away from the messy details of microphenomena, not only in physics, but in other sciences as well.

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.

The relation between models and the world is mediated by experimental procedures generating data that are used as evidence to evaluate the model. Data can serve as empirical evidence, for or against, only if they result from reliable experimental procedures. The aim of this article is to discuss the role of relevance judgments in the evaluation of reliability and to clarify the conditions under which reliability can be a strictly empirical matter. It is argued that reliability is a strictly empirical issue only in the restricted case in which the claim under test/investigation is about a data-generating procedure.

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 book, Laudan has put forward a new and provocative theory of scientific progress. In this discussion we shall show that some of the claims which Laudan presents as new, are (or at least can be made into) parts of more “traditional” wisdom. In particular (in Section 1) we shall criticize his claim to have uncovered a new class of non-refuting anomalies for theories. We shall also (in section 2) criticize Laudan's view that a theory may have an anomaly because of conflict with non-scientific theories—as long as those theories are perceived of as rational. Laudan's view of theory-assessment—via the assessment of “research traditions”—is also examined in this section. It is found to be a significant departure from both traditional, and more radical (Lakatosian, Kuhnian, Feyerabendian) alternatives. Finally, (in Section 3), we examine Laudan's claim that theories may have anomalies because of conflict with methodological theories.

We argue that many recent philosophical discussions about the reference of everyday concepts of intentional states have implicitly been predicated on descriptive theories of reference. To rectify this, we attempt to demonstrate how a causal theory can be applied to intentional concepts. Specifically, we argue that some phenomena in early social development (e.g., mimicry, gaze following, and emotional contagion) can serve as reference fixers that enable children to track others’ intentional states and, thus, to refer to those states. This allows intentional concepts to be anchored to their referents, even if folk psychological descriptions turn out to be false.

This paper takes issue with Tyler Burge's claim that intentional states are nonindividualistically individuated in cognitive psychology. A discussion of current models of children's acquisition of semantic knowledge is used to motivate a thought-experiment which shows that psychologists working in this area are not committed to describing the concepts children attach to words in terms of the concepts standardly attached to those words in the child's community. The content of the child's representational states are thus not individuated with reference to linguistic environment in the manner that Burge's nonindividualistic view requires. The paper concludes that the explanatory states of cognitive psychology are sometimes individualistically individuated.

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.

Proponents of individual-based modeling in ecology claim that their models explain the emergence of population-level behavior. This article argues that individual-based models have not, as yet, provided such explanations. Instead, individual-based models can and do demonstrate and explain the emergence of population-level behaviors from individual behaviors and interactions.

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.

We show that as game theory was transferred from mathematical oncology to experimental cancer biology, a new mode of inquiry was created. Modeling was replaced by measuring. The game measured by a game assay can serve as a bridge that allows knowledge to flow backward from target (cancer research) to source (game theory). Our finding suggests that the conformist and creative (Houkes and Zwart 2019) types of transfer need to be augmented. We conclude by introducing the expansive and transformative types to get a four-tier typology of knowledge transfer.

Feyerabend's “Classical Empiricism” (1970) draws on a 17th century Jesuit argument against Protestant fundamentalism. The argument is very general, and applies to any simple foundationalist epistemology. Feyerabend uses it against Classical Empiricism—roughly, the view that what is to be believed is exactly what experience establishes, and no more—which he identifies as among other things Newton's “dogmatic ideology.”

The logical empiricists knew that scientific theories sometimes arise out of the attempt to reconcile or unify two existing theories. They also thought that, at best, old theories would be retained as approximations to their successors. Kuhn lost both insights when he rejected the logical empiricists' formal approach in favor of an exclusively historical and psychological one. But when Putnam tried to restore such ideas he failed to provide them with the historical support they require. An account of revolutionary unifications is defended as reconciling Putnam's realist ideas about methodology with Kuhn's historical approach.