Livengood and Sytsma (2020) challenge the compositionality constraint of actual causation (CCAC), according to which each intermediary of a causal chain is an effect of its predecessor and a cause of its successor link. In several studies, they find support for their hypothesis that the CCAC is not in accordance with the ordinary causal attributions of laypeople. We argue that there are three interrelated problems in their studies’ design that we call the causality-responsibility confusion (CRC), the intermediary-ontology confusion (IOC), and the cause-end questioning (CEQ). Avoiding the CRC, the IOC, and the CEQ leads to strong empirical support for the CCAC.

This article examines the role existence plays in measurement validity. I argue that existing popular theories of measurement and of validity follow a correspondence framework, which starts by assuming that an entity exists in the real world with certain properties that allow it to be measurable. Drawing on literature from the sociology of measurement, I show that the correspondence framework faces several theoretical and practical challenges. I suggest the validity-first framework of measurement, which starts with a practice-based validation process as the basis for a measurement theory and only posits objective existence when it is scientifically useful to do so.

Howson (2000) contends that the No-Miracles argument fails as an argument in support of scientific realism because it commits the base-rate fallacy. Psillos (2009) has defended the argument by appealing to cases that involve conditional probabilities but where base-rate information can properly be ignored. I show that Psillos’s defense of the No-Miracles argument is inadequate and that the prospects for a purely probabilistic formulation of the argument are dim. I end by considering whether interpreting the argument as an inference to the best explanation might better serve the scientific realist, concluding that such an approach would limit the realist’s ability to effectively counter the Pessimistic Induction.

Discussion of epistemic responsibility typically focuses on belief formation and actions leading to it. Similarly, accounts of collective epistemic responsibility have addressed the issue of collective belief formation and associated actions. However, there has been little discussion of collective responsibility for preventing epistemic harms, particularly those preventable only by the action of an unorganized group. We propose an account of collective epistemic responsibility that fills this gap. Building on Hindriks’s (2019) account of collective moral responsibility, we introduce the epistemic duty to join forces. Our theory provides an account of the responsibilities of scientists to prevent epistemic harms during inquiry.

I propose an account of the model-based structure of the present-day high energy physics experiments in which the relations among the theoretical, experimental and simulation models constitute a non-linear structure that is akin to a network of models (NoM). I argue that while the proposed NoM subsumes Suppes’ hierarchy of models (HoM) as the model-based characterization of the inference leading from the data to the validity or invalidity of the hypothesis tested in an experiment, it involves a model-based characterization of the inference leading from the collision of particles to the acquisition of data, which is missing in Suppes’ HoM.

Scientists imagine for epistemic reasons, and these imaginings can be better or worse. But what does it mean for an imagining to be epistemically better or worse? There are at least three metaepistemological frameworks that offer different answers to this question: epistemological consequentialism, deontic epistemology, and virtue epistemology. This paper presents empirical evidence that scientists adopt each of these different epistemic frameworks with respect to imagination, but argues that the way they do this is best explained if scientists are fundamentally epistemic consequentialists about imagination.

Philosophical studies of complex scientific concepts are predominantly “adaptationist,” arguing that conceptual complexity serves important purposes. This is a historical artifact. Having had to defend their views against a monist presumption favoring simpler concepts, pluralists and patchwork theorists felt compelled to show that complexity can be beneficial. This neglects an alternative possibility: Conceptual complexity is largely neutral, persisting simply because it does little harm. This article defends the neutral theory of conceptual complexity in two forms: (a) as a plausible theory in its own right and (b) as a useful foil for adaptationist arguments.

We provide three innovations to recent debates about whether topological or “network” explanations are a species of mechanistic explanation. First, we more precisely characterize the requirement that all topological explanations are mechanistic explanations and show scientific practice to belie such a requirement. Second, we provide an account that unifies mechanistic and non-mechanistic topological explanations, thereby enriching both the mechanist and autonomist programs by highlighting when and where topological explanations are mechanistic. Third, we defend this view against some powerful mechanist objections. We conclude from this that topological explanations are autonomous from their mechanistic counterparts.

Many contemporary biologists and philosophers of biology admit that selection occurs at any level of the biological hierarchy at which entities showing heritable variation in fitness are found, while insisting that fitness at any level entails differential reproduction, not differential persistence. Those who allow that persistence can be selected doubt that selection on nonreproducing entities can be reiterated, to produce “complex adaptations.” We present here a verbal model of subclones evolving in a simple idealized chemostat that calls into question these suppositions and is usefully explanatory when taken as an analogy to selection for persistence of clades.

Recent findings in different areas of psychology and cognitive science have brought the unconscious mind back to center stage. However, the unconscious mind worry remains: What renders unconscious phenomena mental? I suggest a new strategy for answering this question, which rests on the idea that categorizing unconscious phenomena as “mental” should be scientifically useful relative to the explanatory research goals. I argue that this is the case if by categorizing an unconscious phenomenon as “mental” one picks out explanatorily relevant similarities to a corresponding paradigmatically mental conscious phenomenon. Explanatory relevance is spelled out in terms of mechanistic norms.

We are often justified in acting on the basis of evidential confirmation. I argue that this is because inductive inference supports belief in non-quantificational—or generic—generalizations, rather than universally quantified generalizations. I show how this account supports, rather than undermines, a Bayesian account of inductive inference. Induction from confirming instances of a generalization to belief in the corresponding generic is part of a reasoning instinct that is typically (but not always) correct, and allows us to approximate the predictions that formal epistemology would make.

There is a persisting debate about what chemical bonds are and whether they exist. I argue that chemical bonds are real patterns of interactions between subatomic particles. This proposal resolves the problems raised in the context of existing understandings of the chemical bond and provides a novel way to defend the reality of chemical bonds.

Some philosophers understand natural kinds to be the categories which are constraints on enquiry. In order to elaborate the metaphysics appropriate to such an account, I consider the complicated history of scurvy, citrus, and vitamin C. It may be tempting to understand these categories in a shallow way (as mere property clusters) or in a deep way (as fundamental properties). Neither approach is adequate, and the case instead calls for middle-range ontology: starting from categories which we identify in the world and elaborating their structure, but not pretending to jump ahead to a complete story about fundamental being.

Physics presents us with a symphony of natural constants: $G$, ℏ, $c$, and so forth. Up to this point, constants have received comparatively little philosophical attention. In this article, I provide an account of dimensionful constants, in particular the gravitational constant. I propose that they represent interquantity structure in the form of relations between quantities with different dimensions. I use this account of $G$ to settle a debate over whether mass scalings are symmetries of Newtonian gravitation. I argue that they are not, but only if we interpret mass anti-quidditistically. This is analogous to anti-haecceitism in the presence of spacetime symmetries.

I describe two candidate representations of a mixture. The first, which I call the standard representation, is not a good representation of a mixture in spite of its widespread popularity. The second, which I call Gibbs’s representation, is less widely adopted but is, I argue, a much better representation. I show that once we have a precise mathematical structure that can be used to represent thermodynamic systems, and once an adequate perspective on representation is adopted, Gibbs’s representation trumps the standard representation.

Newton’s First Law of Motion is typically understood to govern only the motion of force-free bodies. This paper argues on textual and conceptual grounds that the law is in fact a stronger, more general principle. The First Law limits the extent to which any body can change its state of motion—even if that body is subject to impressed forces. The misunderstanding can be traced back to an error in the first English translation of Newton’s Principia, which was published a few years after Newton’s death.

The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is because computable approximations to Solomonoff prediction do not always converge.

Judging a person as morally responsible involves believing that certain responses (such as punishment, reward, or expressions of blame or praise) can be justifiably directed at the person. This paper develops an account of the evolution of moral responsibility judgment that adopts Michael Tomasello’s two-step theory of the evolution of morality and borrows also from Christopher Boehm’s work. The main hypothesis defended is that moral responsibility judgment originally evolved as an adaptation that enabled groups of cooperative individuals to hold free riders responsible more safely by acting in a coordinated way.

Scientism has recently experienced a resurgence of interest in philosophy. One version of scientism often defended is ontological scientism—the view that any kind or property not mentioned in the theories of science has only a subordinate, secondary kind of reality. It is worth noting that a dominant tradition in the history of philosophy of science—classical American pragmatism—undertook decades of critical engagement with contemporaneous scientistic beliefs, many of which resemble those being debated at the present time. This antiscientistic philosophy has multiple points of relevance for contemporary debates and defenses of ontological scientism.