There are myriad techniques industry actors use to shape the public understanding of science. While a naive view might assume these techniques typically involve fraud or outright deception, the truth is more nuanced. This paper analyzes industrial distraction, a common technique where industry actors fund and share research that is accurate, often high-quality, but nonetheless misleading on important matters of fact. This involves reshaping causal understanding of phenomena with distracting information. Using case studies and causal models, we illustrate how this impacts belief and decision making even for rational learners, informing science policy and debates about misleading content.

This article defends informed preference satisfaction theories of welfare against the most influential objections put forward in the economic and philosophy of science literatures. The article explicates and addresses in turn: the objection from inner rational agents; the objection from unfeasible preference reconstruction; the objection from dubious normative commitments; the objection from conceptual ambiguity; and the objection from conceptual replacement. My defence does not exclude that preference satisfaction theories of welfare face significant conceptual and practical challenges. Still, if correct, it demonstrates that philosophers/welfare economists are justified in relying on specific versions of such theories, namely informed preference satisfaction theories.

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 paper advances a theory of disease as domino dysfunction. It is often argued that diseases are biological dysfunctions. However, a theory of disease as biological dysfunction is complicated by some plausible cases of dysfunction that seem clearly non-pathological. I argue that pathological conditions are not just dysfunctions but domino dysfunctions, and that domino dysfunctions can be distinguished on principled biological grounds from non-pathological dysfunctions. I then show how this theory can make sense of the problem cases; they are not diseases because they are not domino dysfunctions.

We characterize four fruitful and underappreciated epistemic roles played by the concept of an individualized niche in contemporary biology, utilizing results of a qualitative empirical study conducted within an interdisciplinary biological research center. We argue that the individualized niche concept (1) shapes the research agenda of the center, (2) facilitates explaining core phenomena related to interindividual differences, (3) helps with managing individual-level causal complexity, and (4) promotes integrating local knowledge from ecology, evolutionary biology, behavioral biology, and other biological fields. We thereby also challenge arguments that the niche concept is superfluous in ecology.

It is often claimed that biology is autonomous from the physical sciences, but this is seldom made precise. This article makes explicit, for the first time, five distinct “autonomy of biology” theses. Three moderate theses concerning scientific status, methodological distinctness, and nonreducibility of biology to physics are correct and are nearly universally accepted. Two stronger theses, concerning the exclusivity of biological explanation and irrelevance of physical laws, are shown to be false on the basis of two case studies of physical explanations of biological phenomena. Which scales and laws are explanatorily relevant for a particular phenomenon must be decided empirically.

The debate over context and neural function pits contextualists, who maintain that neural functions vary by context, against invariantists, who maintain that they do not. In this article, I defend a moderate position that permits some context sensitive yet invariant functions. I distinguish performance from competence as well as different types of contexts, accounting for many cases of variability by context. The discussion suggests changes in networks can sometimes change the competences of parts. I conclude that some context sensitivity of function is consistent with invariant functions whereas other changes in context imply changes in the competence to perform functions.

Scientific fields frequently need to exchange data to advance their own inquiries. Data unification is the process of stabilizing these forms of interfield data exchange. I present an account of the epistemic structure of data unification, drawing on case studies from model-based cognitive neuroscience (MBCN). MBCN is distinctive because it shows that modeling practices play an essential role in mediating these data exchanges. Models often serve as interfield evidential integrators, and models built for this purpose have their own representational and inferential functions. This form of data unification should be seen as autonomous from other forms, particularly explanatory unification.

“Relativistic thermodynamics” should be understood not as a generalization of a non-relativistic theory but as an application of a general thermodynamic framework, neutral as to spacetime setting and allowing arbitrary conserved quantities, to the specific case of relativity. That framework gives an unambiguous result as to the thermodynamics of relativistically moving systems (an answer coinciding with Einstein’s, and Planck’s, original results). Thermodynamic temperature is unambiguously defined as rate of change of energy with entropy at constant momentum; that said, its operational significance is limited and other measures of energy/entropy covariance, which incorporate momentum transfer, are often more useful.

Bayesian confirmation does not generally agglomerate over conjunction. That is, whenever a piece of evidence $E$ confirms two hypotheses ${H_1}$ and ${H_2}$ individually, it does not follow that $E$ also confirms them conjunctively. Here, I present a condition under which the latter does follow from the former. But this new condition reveals a surprising fact: Bayesian confirmation agglomerates over conjunction whenever the evidence in question also confirms that both target hypotheses are false.

In this article, I consider two arguments concerning the status of holobionts as evolutionary individuals—one rejects their status by privileging the “stability of lineages” and the other supports their status by privileging the “stability of traits.” I argue that the tension between these two arguments arises from two fundamentally different accounts of natural selection. I suggest that each account of selection corresponds to a unique account of evolutionary individuality. This strategy entitles us to a modest pluralism: Holobionts are evolutionary individuals on one account of selection but not on the other.

We present an agent-based model to study how the structure of a scientific network could affect the public uptake of science and how this impact is influenced by scientific uncertainty and affinity bias. For unbiased agents, a highly connected scientific network decreases the probability that the public favors the correct theory. For biased agents, however, a moderately connected scientific network causes the public to favor the correct theory more often. This results from the competition between the scarcity of information (for poorly connected agents) and the spread of misleading information (for highly connected agents). Adding more scientists strengthens both effects.

In this article I issue a challenge to what I call the Independence Thesis of Theory Assessment (ITTA). According to ITTA, the evidence for (or against) a theory must be assessed independently from the theory explaining the evidence. I argue that ITTA is undermined by cases of evidential uncertainty, in which scientists have been guided by the explanatory power of their theories in the assessment of the evidence. Instead, I argue, these cases speak in favor of a model of theory assessment in which explanatory power may indeed contribute to the stabilization of the evidential basis.

This article distinguishes causal modeling, metaphysical, epistemic, and modeling reasons for variable/parameter choice. I argue in favor of justifying variable/parameter choices by appealing to modeling reasons concerning the limitations of the available measurements, experimental data, modeling techniques, and modeling frameworks. I use this “tyranny of availability” to identify normative criteria for variable/parameter choice that apply across most scientific modeling contexts and investigate their metaphysical and epistemological implications.

There is an “underrepresentation problem” in philosophy departments and journals. Empirical data suggest that while we have seen some improvements since the 1990s, the rate of change has slowed down. Some posit that philosophy has disciplinary norms making it uniquely resistant to change. We present results from an empirical case study of a philosophy department that achieved and maintained male-female gender parity among its faculty as early as 2014. Our analysis extends beyond matters of gender parity because that is only one, albeit important, dimension of inclusion. We build from the study to reflect on strategies that may catalyze change.

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.

The Black–Scholes (B-S) model is considered by the academic environment one of the greatest achievements of financial economics. Yet it brings with it some conundrums. The model is often used in a manner that contradicts one of its assumptions, and its predictions are not supported by market reality. Here we address, from the perspective of philosophy of science, an additional issue related to this model: the distinction between its explanatory and predictive capabilities.

I discuss and clarify the relationship between the recent wave of “intrinsic” coordinate-free approaches to Maxwell gravitation and the coordinate-based discussions of Saunders (2013) and Wallace (2020).

This paper evaluates the back-and-forth between Mayo, Howson, and Achinstein over whether classical statistics commits the base-rate fallacy. I show that Mayo is correct to claim that Howson’s arguments rely on a misunderstanding of classical theory. I then argue that Achinstein’s refined version of the argument turns on largely undefended epistemic assumptions about “what we care about” when evaluating hypotheses. I end by suggesting that Mayo’s positive arguments are no more decisive than her opponents’: even if correct, they are unlikely to compel anyone not already sympathetic to the classical picture.

Three popular objections to reductionism about color, experience as of impossible colors, the unary/binary distinction, and structural mismatch, are issues just, I argue, for the (probably default) version of reductionism according to which colors reduce to sets of surface spectral reflectances. They are not problems for the version on which colors are dispositions to reflect coarse-grained intensities of light are—what in colorimetry are called “object colors.” This article sets out to demonstrate the virtues of the latter reductionism.