Modal relationism is the view that our best physical theories can dispense with substantival space or spacetime in favor of possible configurations of particles. Kenneth Manders argues that the substantivalist conception is equivalent to this Leibnizian conception of space. To do so, Manders provides a translation $f$ from the Newtonian theory ${T_N}$ into the Leibnizian modal relationist account ${T_L}$. In this article, we show that the translation does not establish equivalence because there is no translation $g:{T_L} \mapsto {T_N}$ that preserves theoremhood. This seems to show that the modal relationist theory ${T_L}$ is less parsimonious than its substantivalist rival.

In light of recent criticisms by Woodward (2017) and Rescorla (2018), we examine the relationship between mechanistic explanation and phenomenological laws. We disambiguate several uses of the phrase “phenomenological law” and show how a mechanistic theory of explanation sorts them into those that are and are not explanatory. We also distinguish the problem of phenomenological laws from arguments about the explanatory power of purely phenomenal models, showing that Woodward and Rescorla conflate these problems. Finally, we argue that the temptation to pit mechanistic and interventionist theories of explanation against one another occludes important and scientifically relevant research questions.

Robustness with respect to climate models is often considered by philosophers of climate science to be a crucial issue in determining whether and to what extent the projections of the Earth’s future climate that models yield should be trusted. Wendy Parker and Lisa Lloyd have introduced influential accounts of robustness for climate models with seemingly conflicting conclusions. I argue that Parker and Lloyd are characterizing distinct notions of robustness and providing complementary insights. Confidence, if warranted, need be by virtue of causally consistent climate models rather than by agreement upon projections by a diverse range of models.

I argue that the temporal asymmetry of influence is not merely the result of thermodynamics: it is a consequence of the fact that the modal structure of the universe must admit only processes that cannot give rise to contradictions. I appeal to the process matrix formalism developed in the field of quantum foundations to characterize processes that are compatible with local free will while ruling out contradictions, and I argue that this gives rise to “consistent-chaining” requirements that explain the temporal asymmetry of influence. I compare this view to the perspectival account of causation advocated by Price and Ramsey.

Thermodynamics is an unusual theory. Prominent figures, including J. C. Maxwell and E. T. Jaynes, have suggested that thermodynamics is anthropocentric, and contemporary approaches label thermodynamics a “subjective theory.” Here, we evaluate the arguments for anthropocentrism but conclude that instead of pointing to an anthropocentric view, they point towards a resource-relative understanding of thermodynamics which can be shorn of the “subjective gloss.”

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.

I argue that there are two distinct approaches to understanding reduction: the ontology-first approach and the theory-first approach. They concern the relation between ontological reduction and inter-theoretic reduction. Further, I argue for the significance of this distinction by demonstrating that either one or the other approach has been taken as an implicit assumption in, and has in fact shaped, our understanding of what statistical mechanics is. More specifically, I argue that Boltzmannian statistical mechanics assumes and relies on the ontology-first approach, whereas Gibbsian statistical mechanics should assume the theory-first approach.

This article draws on Kukla’s “Institutional Definition of Health” to provide a definition of “psychiatric condition” that delineates the proper bounds of psychiatry. I argue that this definition must include requirements that psychiatrization of a condition benefit the well-being of (1) the society as a collective and (2) the individual whose condition is in question. I then suggest that psychiatry understand individual well-being in terms of the subjective values of individuals. Finally, I propose that psychiatry’s understanding of collective well-being should be the result of a “socially objective” process and give certain desiderata for this understanding.

This article shows how institutional values influence the design and evaluation of arguments in funding proposals for research. We characterize a general argument made within proposals and several kinds of subarguments that contribute to it. We indicate that funders’ values inform the kinds of proposal documents funders require and their relative weighting of them. We illustrate these points by showing how a program office in the US National Oceanic and Atmospheric Administration (NOAA) uses its public service mission to require and heavily weigh arguments to transition new knowledge to NOAA service providers, and we suggest conceptual questions raised by the use of transition knowledge.

Court cases involving sexual assault and police violence rely heavily on victim testimony. We consider what we call the Traumatic Untrustworthiness Argument (TUA) according to which we should be skeptical about victim testimony because people are particularly liable to misremember traumatic events. The TUA is not obviously based in mere distrust of women, people of color, disabled people, poor people, and so forth. Rather, it seeks to justify skepticism on epistemic and empirical grounds. We consider how the TUA might appeal to the psychology and neuroscience of memory for empirical support. However, we argue that neither support the TUA.

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.

The cultural red king effect occurs when discriminatory bargaining practices emerge because of a disparity in learning speed between members of a minority and a majority. This effect has been shown to occur in some Nash Demand Game models and has been proposed as a tool for shedding light on the origins of sexist and racist discrimination in academic collaborations. This article argues that none of the three main strategies used in the literature to support the epistemic value of these models—structural similarity, empirical confirmation, and how-possibly explanations—provide strong support for this modeling practice in its present form.

Extant accounts of trust in science focus on reconciling scientific and public value judgments, but neglect the challenge of learning audience values. I argue that for scientific experts to be epistemically trustworthy, they should adopt a cooperative approach to learning about the values of their audience. A cooperative approach, in which expert and nonexpert inquirers iteratively refine value judgments, better achieves important second-order epistemic dimensions of trustworthiness. I suggest that strong objectivity in the feminist standpoint theoretic sense is sometimes a prerequisite for trustworthiness.

Philosophers and metrologists have refuted the view that measurement’s epistemic privilege in scientific practice is explained by its theory neutrality. Rather, they now explicitly appeal to the role that theories play in measurement. I formulate a challenge for this view: Scientists sometimes ascribe epistemic privilege to measurements even if they lack a shared theory about their target quantity, which I illustrate through a case study from early geodesy. Drawing on that case, I argue that the epistemic privilege of measurement can precede shared background theory and is better explained by its pretheoretic function in enabling a distinctive kind of inquiry.

We examine arguments regarding the use of mechanistic evidence in assessing treatment efficacy and find that advocates and critics of EBM+ have largely been talking past each other due to a difference in focus. We explore aducanumab for the treatment of Alzheimer’s disease as a case that may speak to the role of EBM+ in pharmaceutical regulation. The case suggests the debate may be more fruitful if philosophers confine debates to particular domains of medicine and weigh in prospectively instead of relying on historical cases in which outcomes are known and that are susceptible to hindsight bias.

Standard articulations of Inference to the Best Explanation (IBE) imply the uniqueness claim that exactly one explanation should be inferred in response to an explanandum. This claim has been challenged as being both too strong (sometimes agnosticism between candidate explanatory hypotheses seems the rational conclusion) and too weak (in cases where multiple hypotheses might sensibly be conjointly inferred). I propose a novel interpretation of IBE that retains the uniqueness claim while also allowing for agnostic and conjunctive conclusions. I then argue that a particular probabilistic explication of explanatory goodness helpfully guides us in navigating such options when using IBE.

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.

Immunology is a notoriously complex field with distinct concepts and terminology. Yet immunologists regularly and effectively collaborate with other researchers, notably clinicians and experts in population health. How does such “collaboration without convergence” work? This paper offers an answer. Immunology exhibits three features that support collaboration in the absence of major consensus on theories, methods, or concepts. These are a multifaceted target of inquiry, therapeutic aspirations, and a clear interdisciplinary pathway. Building on these features, I sketch a general account of “low-effort interdisciplinarity” and connect this result to recent work on population health.

Measurement of subjective animal welfare creates a special problem in validating the measurement indicators used. Validation is required to ensure indicators are measuring the intended target state, and not some other object. While indicators can usually be validated by looking for correlation between target and indicator under controlled manipulations, this is not possible when the target state is not directly accessible. In this article, I outline a four-step approach using the concept of robustness that can help with validating indicators of subjective animal welfare.

Statistical analysis is often used to evaluate the strength of evidence for or against scientific hypotheses. Here we consider evidence measurement from the point of view of representational measurement theory, focusing in particular on the 0-points of measurement scales. We argue that a properly calibrated evidence measure will need to count up from absolute 0, in a sense to be defined, and that this 0-point is likely to be something other than what one might have expected. This suggests the need for a new theory of statistical evidence in the context of which calibrated evidence measurement becomes tractable.