Every month some one pronounces that science must be rejected if we are to preserve a belief in human freedom, or that only by a faith in freedom that flies in the face of logic and the principle of causality can democracy be justified. Equally often other authors insist that the increase of knowledge in the science of human behavior makes plain the irrelevance and sentimentality of pious talk about free and rational choice. What is the source of this dispute? Why is no resolution of it arrived at?

Many attempts have been made in recent months to throw light on the problem of vagueness. That perfect precision is an ideal not to be attained by any language seems clear. But the obvious fact is that words and sentences in our languages are not so precise as we should like to have them, and we are naturally concerned with finding some sort of device by which vagueness can, in the first place, be detected and measured, and, in the second place, eliminated or reduced to a minimum. I shall not be concerned in what follows with these various special attempts to meet the problem. My task is the somewhat more general one of showing that there are other factors contributing to vagueness, one of which is of particular significance when we leave the realm of ostensively defined symbols and begin to concern ourselves with suppositional symbols, constructs and hypotheses.

“Of Minds and Molecules” attempts to show the difficulties in mental-state brain-state monism. By exploring the differences in meaning between mental-state sentences and brain-state sentences, and by analysing the implications of the theory of the molecular composition of matter, a kind of dualism is arrived at that no scientist should feel uncomfortable with. It is a dualism without mental substance but it does not deprive mental states of their uniqueness. Arguments are given for the propriety of asserting causal connections and dependency relations between mental states and molecular states of the brain.

An examination of the foundations of Elliot Sober's philosophy of biology as reflected in his introductory textbook of that title reveals substantial and controversial philosphical commitments. Among these are the claim that all understanding is historical, the assertion that there are biological laws but they are necessary truths, the view that the fundamental theory in biology is a narrative, and the suggestion that biology adverts to ungrounded probablistic propensities of the sort to be met with elsewhere only in quantum mechanics.

This paper provides the first thorough assessment of a physics-based answer, the Bound State Answer (BSA), to the Special Composition Question (SCQ). According to the BSA some objects compose something if they are in a common bound state. The reasons to endorse such an answer, in particular, motivations coming from empirical adequacy and conservativeness, precision, simplicity, and parsimony, are critically addressed. I then go on to compare the BSA to other moderate answers to the SCQ and consider whether objections raised against such answers can be raised against the BSA as well. I finally relate the discussion with mereological pluralism.

Recent advocates of “field philosophy” make the case that philosophy “needs to get outside more often”; alongside disciplinary modes of practice we should cultivate philosophical work that is consequential in real-world, practical terms. This takes a number of different forms, many of them captured by a framework for analyzing engaged philosophy of science proposed by Plaisance and Elliott. I draw on three examples of field-engaged philosophy of science that address the legacies of settler-colonialism archaeology to illustrate the promise of field philosophy.

Some scientific models and some claims about model–target relations are fruitfully diagnosed as dogwhistles. Dogwhistles, broadly speaking, are speech acts that send different, conflicting, and often differentially inflammatory messages to listeners. I distinguish two ways in which scientific models can be dogwhistles: representational dogwhistling and fit-for-purpose dogwhistling. I illustrate both kinds of dogwhistling using an example from computational social science, the diversity trumps ability theorem. I argue that dogwhistling threatens the objectivity of science, and I propose some ameliorative strategies.

A recent controversy about neural networks allegedly capable of detecting a person’s sexual orientation raises the question of whether all research on homosexuality should be permitted. This paper considers two arguments for limits to such research, and concludes that there are good reasons to limit at least the dissemination of applied research on the etiology of homosexuality. The paper then briefly sketches how this could work, and looks at three objections against these limitations.

Formal and social epistemologists have devoted significant attention to the question of how to aggregate the credences of a group of agents who disagree about the probabilities of events. Moss (2011) and Pettigrew (2019) argue that group credences can be a linear mean of the credences of each individual in the group. By contrast, I argue that if the epistemic value of a credence function is determined solely by its accuracy, then we should, where possible, aggregate the underlying statistical models that individuals use to generate their credence functions, using “stacking” techniques from statistics and machine learning first developed by Wolpert (1992).

When Whitehead's views of relativity are mentioned, everyone thinks of the views Whitehead published between 1919 and 1924. Concerning these views, C. D. Broad is “depressed”1 (1: pp. 143–4) that physicists paid so little attention. And Victor Lowe has written:

There is a bare possibility that Whitehead's theory of relativity may yet affect the history of science. (4: p. 69)

Researchers of language origins typically try to explain how compositional communication might evolve to bridge the gap between animal communication and natural language. However, as an explanatory target, compositionality has been shown to be problematic for a gradualist approach to the evolution of language. In this article, I suggest that reflexivity provides an apt and plausible alternative target that does not succumb to the problems that compositionality faces. I further explain how protoreflexivity, which depends on functional reference, gives rise to complex communication systems via modular composition.

Jarrett Leplin has recently criticized a number of aspects of my views of the aims and goals of science, of the rationale of scientific change, and of the consequent relations between science and the philosophy of science, and in particular of the methodology of the latter (Leplin 1984, 1987, 1988a, 1988b). Here I will respond to those criticisms, and also reply to some related criticisms made by other writers.

Wilfrid Sellars has proposed a materialist account of sensation which relies in part on the postulation of special kinds of individuals. This postulational strategy appears to be analogous to the one that introduces such entities as electrons. After setting out Sellars' account, I focus on his application of the postulational strategy. I argue that this application requires the discovery of new effects for familiar properties; that this kind of discovery is disanalogous to what postulation usually does; and that this kind of discovery cannot really be arrived at by postulation. I conclude that Sellars has not provided a successful, materialist account of sensation.

Kirsten Walsh has criticized my interpretation of Newton’s view on the certainty of his new science of color, namely, that it shifted through his long career from one of certainty to probabilism. I defend my view by further considering his statements on certainty in his Optical Lectures, “New Theory about Light and Colors,” Query 31 of the Opticks, and Rule 4 added to the Principia in 1726.

One challenge in explaining neural evolution is the formal equivalence of different computational architectures. If a simple architecture suffices, why should more complex neural architectures evolve? The answer must involve the intense competition for resources under which brains operate. I show how recurrent neural networks can be favored when increased complexity allows for more efficient use of existing resources. Although resource constraints alone can drive a change, recurrence shifts the landscape of what is later evolvable. Hence organisms on either side of a transition boundary may have similar cognitive capacities but very different potential for evolving new capacities.