In this discussion note, we contrast two models of pluralistic policy advice that have recently been proposed: Stephen John’s (2025) representative model and our integrative model (Bschir and Lohse 2022, 2024). We believe that the contrast between representative and integrative pluralism is of high relevance for the broader philosophical discussion on scientific policy advice.

Darwin describes the Origin of Species as ‘one long argument’. The exact structure of this argument has been the subject of controversy among philosophers of biology. I will propose a novel analysis that sheds new light on Darwin’s argument. The central claim will be that the evidence that supports the theory of common descent can only satisfactorily support it after the theory of natural selection has been regarded as an in principle possibility. This account helps us understand some enigmatic features of the structure of the Origin and has consequences for evidentialism and Bayesianism as applied to Darwin’s argument.

Despite the recent “epistemic turn” in the philosophy of measurement, philosophers have ignored a nearly 80-year controversy about the relationship between statistical inference and measurement theory. Some scholars maintain that measurement theory places no constraints on statistics, whereas others argue that the measurement scale (e.g., ordinal or interval) of one’s data determines which statistical methods are “permissible.” I defend an intermediate position: Even if existing measurement theory were irrelevant to statistical inference, it would be critical for scientific inference, which requires connecting statistical hypotheses to broader research hypotheses.

Philosophers of science have overlooked the role of theory in neuroscience, resulting in a somewhat surprising naïveté regarding the nature and function of neuroscientific theories. Here I provide a framework that identifies and begins to characterize what we need to know about neuroscientific theories so as to improve our epistemic standing. I argue that we need an account of the structural, interpretive, and functional aspects of neuroscientific theories, using the neuron doctrine as an illustrative case study. I introduce the novel metaphor of theoretical infrastructure as a guide for making sense of neuroscientific theories and their place within neuroscientific practice.

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.

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.

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.

Mixture of experts is a prediction aggregation method in machine learning that aggregates the predictions of specialized experts. This method often outperforms Bayesian methods despite the Bayesian having stronger inductive guarantees. We argue that this is due to the greater functional capacity of mixture of experts. We prove that in a limiting case, mixture of experts will have greater capacity than equivalent Bayesian methods, which we vouchsafe through experiments on non-limiting cases. Finally, we conclude that mixture of experts is a type of abductive reasoning in the Peircean sense of hypothesis construction.

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.

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.

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.

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.

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.

Recent work by Michael O. Hardimon and Quayshawn Spencer defends a minimalist (or deflationary) biological realism about race. Their approach has two distinct features. First, unlike revisionist biological race, minimalist biological races are a conception of race that correspond to our ordinary race concepts. Second, unlike hereditarian or essentialist accounts, minimalist biological races are not claimed to be robustly explanatory. This article argues against their account of the biological genuineness of race. I argue that the minimalist biological conception of race lacks the explanatory constraints of genuine biological kinds. Rather, minimalist biological races are gerrymandered kinds.

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.

The timing of the origin of flowering plants (Angiosperm) is hotly debated. It has been suggested that the disagreement between the fossil record of angiosperm origin strongly conflicts with the origin estimates generated by molecular clocks. I argue that this conflict reveals lessons about whether or under what conditions scientific disagreement is likely to bear fruit. Specifically, I point to issues of evidence quality and social epistemic structures which deserve more attention in understanding the productivity of disagreement.

It is often said that successful scientific research must be built on trust. Focusing on the alleged necessity of trust for successful scientific communication and thus for scientific cooperation (which underlies much of contemporary science), I argue that science mustn’t be built on trust. Appearances to the contrary come from a failure to distinguish different attitudes toward scientists’ testimony, in particular, trusting and relying on other scientists. This article proposes an account of scientific reliance and explains how it differs from scientific trust; it also shows why this distinction matters for science.

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.

Thick concepts have both descriptive and evaluative dimensions to their meaning. Some have argued that because the descriptive and evaluative dimensions cannot be separated (they are “blended”), the implicit values influence the confirmation of any “mixed claims’’ containing the thick concept. Using the development of the concept of hypersegregation as a case study, we argue for a distinction between the semantic function of definitions and the epistemic function of indicators. While thick concepts are semantically blended, the evaluative meaning of a thick concept does not influence the function of epistemic indicators. Therefore, mixed claims can and should be tested impartially.

This article challenges a false dichotomy between subjectivity and objectivity in understanding the nature of human social relationships. I argue that social relationships are composed of both subjective and objective components, which are inherently interdependent. They are influenced by biological properties and subject to evolutionary processes, yet they cannot be reduced to them. I use emerging research on kinship as an example that showcases the appeal of this integrated approach. This article takes a step in the direction of a unified account of sociality, contributing to a more comprehensive understanding of human social behavior.