One challenge to relationism in general relativity is that the metric field is underdetermined by the stress–energy tensor. This is manifested in the existence of distinct vacuum solutions to Einstein’s equations. In this paper I reformulate the problem of underdetermination as a problem from vacuum solutions. I call this the vacuum challenge and identify the gravitational degrees of freedom (associated with the Weyl tensor) as the “source” of the challenge. The Weyl tensor allows for gravitational effects that something outside a system exerts on the system. I provide a relationist response to the vacuum challenge.

This article advocates for a pragmatist view on quantum theory, offering a response to David Wallace’s recent criticisms of Richard Healey’s quantum pragmatism. In particular, I challenge Wallace’s general claim that quantum pragmatists—and antirepresentationalists more broadly—lack the resources to make sense of the novel “quantum” language used throughout modern physics in applications of quantum theory. I conclude by posing a challenge to quantum representationalists.

Spacetime singularities are expected to disappear in quantum gravity. Singularity resolution prima facie supports the view that spacetime singularities are mathematical pathologies of general relativity. However, this conclusion might be premature. Spacetime singularities are more accurately understood as global properties of spacetime rather than things. Therefore, if spacetime emerges in quantum gravity—as it is often claimed—then so may its singular structure. Although this proposal is intriguing, the attempt to uphold that spacetime singularities may be emergent fails. I provide three arguments in support of this claim, drawing on different views of spacetime emergence.

Galaxy clusters are commonly used tracers of cosmology. Gravitational lensing analysis of the Bullet Cluster is claimed to evidentially support dark matter, an important component in the ${\rm{\Lambda }}$CDM cosmology. I argue that such ${\rm{\Lambda }}$CDM-based models of individual galaxy clusters should be explanatory to meet such claims, but hardly in an ontic sense, due to galaxy cluster anisotropies, empirically equivalent non-${\rm{\Lambda }}$CDM-based models, and currently unaccountable cases. I propose that adopting an alternative epistemic/representational conception of scientific explanation can maintain the explanatory nature of individual galaxy cluster models, cope with the three complications, and be potentially generalizable to other branches of astrophysics.

This paper challenges the notion of emergent time in quantum cosmology by examining the reconciliation of the timeless Wheeler–DeWitt equation with the Universe’s dynamical evolution. We critically evaluate the analogy between the Wheeler–DeWitt and Klein–Gordon equations, highlighting challenges for the identification of an emergent time parameter. We conclude that refining this analogy may lead to a better understanding of emergent time in quantum cosmology, though it is still not free from complications.

In this paper, I first argue that similarity accounts of scientific pictures fail with more realistic cases of scientific pictures. My primary case study is the picture of a black hole, to which I apply an interpretation-based account of picture representation analogous to how models represent: a picture represents a designated target system iff, once interpreted, it exemplifies properties that are imputed to the target via a de-idealising function. Then, I argue that the justification of the inferences from mechanically produced pictures depends on their causal mechanisms of production, in contrast with the standard justificatory strategies employed for model inferences.

Recently, several philosophers and physicists have increasingly noticed the hegemony of unitarity in the discourse on black hole information loss and are challenging its legitimacy in the face of the measurement problem. They proclaim that embracing non-unitarity solves two paradoxes for the price of one. Although I share their distaste regarding the philosophical bias, I disagree with their strategy of still privileging certain interpretations of quantum theory. I argue that information-restoring solutions can be interpretation-neutral because the manifestation of non-unitarity in Hawking’s original derivation is unrelated to what’s found in collapse theories or generalized stochastic approaches, thereby decoupling the two puzzles.

Although fundamental arguments have been presented to support the value-laden nature of all scientific research, they appear to be difficult to apply to basic research in physics. To explain this, I argue that basic research in physics is, in a very specific respect, often value-laden to a lesser degree. To spell this out, I refer to the different signal-to-noise ratios that can be achieved in different fields of research. I also argue that having a very low degree of value-ladenness in the very specific respect that I identify does not mean that the research is not value-laden at all.

Statistical criteria of fairness, although controversial, bring attention to the multiobjective nature of many predictive modeling problems. In this article, I consider how epistemic and nonepistemic values help to justify the design of machine learning algorithms that optimize for more than one normative goal. I focus on a major design choice between biased search strategies that directly incorporate priorities for various objectives into an optimization procedure and unbiased search strategies that do not. I argue that both reliably generate Pareto optimal solutions such that various other values are relevant to making a rational choice between them.

This paper discusses the role of data within scientific reasoning and as evidence for theoretical claims, arguing for the idea that data can yield theoretically grounded models and be inferred, predicted, or explained from/by such models. Contrary to Bogen and Woodward’s rejection of data-to-theory and theory-to-data inferences/predictions, we draw upon artificial intelligence as applied to science literature to argue that (a) many models are routinely inferred and predicted from the data and routinely used to infer and predict data, and (b) such models can, at least in some contexts, play the role of theories.

Can we acquire a priori mathematical knowledge from the outputs of computer programs? Although we claim Appel and Haken acquired a priori knowledge of the four-color theorem from their computer program insofar as it merely automated human forms of mathematical reasoning, the opacity of modern large language models (LLMs) and deep neural networks (DNNs) creates obstacles in obtaining a priori mathematical knowledge in analogous ways. If, however, a proof-checker automating human forms of proof-checking is attached to such machines, we can indeed obtain a priori mathematical knowledge from them, even though the original machines are entirely opaque to us and the outputted proofs cannot be surveyed by humans.

Researchers in history and the historical sciences explore the use of generative AI (GenAI) systems for reconstructing destroyed artifacts. This paper poses a novel question: Can such GenAI systems generate evidence that provides new knowledge about the world or can they only produce hypotheses that we might seek evidence for? Exploring responses to this question, the paper argues that (1) GenAI outputs can at least be understood as higher-order evidence (Parker 2022) and (2) may also constitute de novo synthetic evidence.

Philosophers have long discussed the virtues of scientific theory and the role they play in pursuing, preferring, or finally accepting a theory on the basis of internal criteria and experimental evidence. Based on two case studies from elementary particle physics, we propose a list of experimental virtues and discuss how they influence experimental strategies and whether a measurement is deemed conclusive—independently of its relation to theory.

The meta-inductive approach to induction justifies induction by proving its optimality. The argument for the optimality of induction proceeds in two steps. The first “a priori” step intends to show that meta-induction is optimal and the second “a posteriori” step intends to show that meta-induction selects object-induction in our world. I critically evaluate the second step and raise two problems: the identification problem and the indetermination problem. In light of these problems, I assess the prospects of any meta-inductive approach to induction.

The stable property cluster view of natural kinds leaves the metaphysical structure of natural kinds open. The positive corollary of this feature is freedom from explaining the nature of problematic entities and that it affords natural kind status and flexibility. However, some worries are raised against this feature; the metaphysical structure of natural kinds is required for explaining their epistemic values such as inductive and explanatory power. Here I take the criticisms at face value and offer a possible metaphysical model of the SPC account that does not meddle with the merits but mitigates the worries.

There are many—not just one—periodic laws in chemistry. These laws correspond to nonaccidental regularity relations about physical and chemical properties of (sets of) chemical elements. I support this by showing how these regularity relations can be understood from the perspective of a philosophical analysis of laws. Specifically, I show that these relations instantiate standard features associated with laws; they can be spelled out in terms of two standard accounts of laws; and they can coherently figure in debates about the reality of laws as plausible candidates of ceteris paribus laws.

In principle, inaccuracies in the representation of the climate’s internal variability could undermine the measurement of the human contribution to warming. Equally in principle, the success of the measurement practice could provide evidence that our assumptions about internal variability are correct. I argue that neither condition obtains: Current measurement practices do not provide evidence for the accuracy of our assumptions precisely because they are not as sensitive to inaccuracy in the representation of internal variability as might be worried. I end by drawing some lessons about stability and “robustness reasoning” more generally.

According to what I call the Probabilistic View, absence of evidence is evidence of absence when finding evidence is highly expected. However, this view fails to make sense of the practice of using absence of evidence in the paleosciences, where finding evidence is typically not highly expected. Using a case from paleogeology, I offer a novel account of when absence of evidence should be evidence of absence, which I call the Pragmatic View: Appeals to absence of evidence as evidence of absence are warranted because they offer a scaffold to investigate auxiliary hypotheses related to the hypothesis in question.

Existing characterizations of “trace” in the philosophy of the historical sciences agree that traces need to be downstream of the long-past event under investigation. I argue that this misses an important type of trace used in historical reconstructions. Existing characterizations of traces focus on what I propose to call direct traces. What I call circumstantial traces (1) share a common cause with a past event, and (2) allow an inference to said event via an intermediate step. I illustrate the significance of checking the alignment between direct and circumstantial traces in historical reconstructions through a case study from (micro-)paleontology.

Practice-based approaches to the philosophy of mathematics have gone mainstream over the past several decades. As the paradigm has grown in popularity, however, there’s been little sustained meditation—and still less any explicit consensus—on what precisely it means for philosophy to take practice seriously. The field’s lack of a clear common methodology has begun to make itself felt in slowed and uncertain progress on core problems. Here, I review the methodological situation and propose five canons to guide future research. I focus throughout on the study of explanation in mathematics as a guiding example.