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 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.

The article reevaluates the distinction between experiment and observation. It is first argued that to get clear on what role observation plays in the generation of scientific knowledge, we need to distinguish “experiential observation” as a concept closely connected to experience from “observation” in a technical sense and from “field observation” as a concept that reasonably contrasts with “experiment.” It is then argued that observation construed as field observation can enjoy systematic epistemic advantages over experiment, contrary to appearances.

This article first poses a skeptical challenge to clinical trials in medicine. The efficacy of treatments is measured against placebo, but placebo responses are not constant. They fluctuate with demographic variables, and they seem to be increasing over time. We therefore find ourselves measuring with the equivalent of what Wittgenstein termed an “elastic ruler.”

I then propose a “skeptical solution” to the problem. Elastic rulers are suitable tools for measuring dynamic, floating networks of values, like foreign currency exchanges. We can assuage the skeptical concerns by understanding clinical trials in this way; I suggest several practical guidelines for doing so.

Justin Garson has argued, contrary to the claims of proponents of purportedly ahistorical theories of functions, that there are no ahistorical theories of functions. In the interest of satisfying uncontroversial desiderata on a theory of functions, the most influential ahistorical views all smuggle in history. I argue that Garson’s case relies on a misinterpretation of the ahistorical accounts he targets and that the details of the misinterpretation are instructive. They highlight often unquestioned assumptions about how a theory of functions fits into a broader account of scientific practice and what theoretical work a definition of function should do.

Stegenga (forthcoming) formulates and defends a novel account of scientific progress, according to which science makes progress just in case there is a change in scientific justification. Here, we present several problems for Stegenga’s account, concerning, respectively, (i) obtaining misleading evidence, (ii) losses or destruction of evidence, (iii) oscillations in scientific justification, and (iv) the possibility of scientific regress. We conclude by sketching a substantially different justification-based account of scientific progress that avoids these problems.