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.

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.

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.

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.

Brouwer’s intuitionism was a far-reaching attempt to reform the foundations of mathematics. Although the mathematical community was reluctant to accept Brouwer’s work, its response to later-developed brands of intuitionism, such as those presented by Hermann Weyl and Arend Heyting, was different. The article accounts for this difference by analyzing the intuitionistic versions of Brouwer, Weyl, and Heyting in light of a two-tiered model of the body and image of mathematical knowledge. Such a perspective provides a richer account of each story and points to a possible connection between the community’s reaction and the changes each mathematician had proposed.

Recently, several funding agencies have introduced the distribution of funds by a lottery system; however, the effects of this system on the productivity of the research community are unclear. Simulation studies in philosophy of science have argued that a combination of peer review and lottery is an optimal method. However, these models overlook several important aspects of research activities, such as baseline funding through block grants. In this article, I present a general theoretical model that incorporates these aspects and argue that the conventional combination of peer review and baseline funding outperforms the combination of peer review and lottery in many situations.

Current cosmological observations place little constraints on the nature of dark matter, allowing the development of a large number of models and various methods for probing their properties, which seem to provide ideal grounds for the employment of robustness arguments. In this article, the extent to which such arguments can be used to overcome various methodological and theoretical challenges is examined. The conclusion is that although robustness arguments have a limited scope in the context of dark-matter research, they can still be used to increase the scientists’ confidence about the properties of specific models.

Forster and Sober (1994) introduced the “sub-family problem” for model selection criteria that recommend balancing goodness-of-fit against simplicity. This problem arises when a maximally simple model (family of hypotheses) is artificially constructed to have excellent fit with the data. We argue that the problem arises because of a violation of the general maxim that balancing goodness-of-fit against simplicity leads to desirable inferences only if one is comparing models for the consideration of which one has a positive reason independently of the current data.

Those who wish to defend the role of aesthetic values in science face a dilemma: aesthetic language is either used metaphorically for what are ultimately epistemic features, or it is used literally, but the importance of such values for science is unclear. This article introduces a new account that gets around this problem by considering an overlooked source of aesthetic value in science: the relation between form and content. A fit between the content of a thought experiment and the way in which that content is formulated can have important epistemic payoffs by contributing to scientific understanding.

My aim here is (1) to argue that the usual argument for thinking that dysfunction has no place in a dispositionalist approach to functions is deeply flawed and (2) to develop a positive account of the explanatory role dysfunction attributions play in dispositionalist-style functional analysis. I also argue that while my account undermines one common motivation for preferring an etiological over a dispositionalist approach, perhaps more interestingly, it also blurs the boundary between the two and opens a path to unifying them.

The compatibility of the Gaia hypothesis with Darwinism is often challenged on the grounds that (1) to be potent, natural selection requires the existence of a population (whereas Gaia is a single entity), and (2) natural selection requires the entities forming a population to reproduce (whereas Gaia merely persists). However, using the Price equation, I argue, following others, that the Gaia hypothesis can fit squarely within a Darwinian framework because Gaia can exhibit adaptations if a process at a lower level (e.g., an ecosystem) can occur, and the notion of natural selection can be extended to accommodate evolution without reproduction.

Experiences of racial discrimination can seem to be caused by one’s race, a combination of social identities, or non-social features. In other words, racial discrimination can be intersectional or attributionally ambiguous. This poses challenges for current understandings and measurement tools of racial discrimination in public health research, such as the explanation of racial health disparities. Different kinds of discriminatory experiences plausibly produce different psychological effects that mediate their negative health impacts. Thus, multiple characterizations and measurements of racial discrimination are needed. As a result, I argue that racial discrimination should be a polysemous concept (with multiple meanings).

In a recent paper, Sprenger advances what he calls a “suppositional” answer to the question of why a Bayesian agent’s degrees of belief should align with the probabilities found in statistical models. I show that Sprenger’s account trades on an ambiguity between hypothetical and subjunctive suppositions and cannot succeed once we distinguish between the two.