Most philosophical work on causal selection examines the selection of single causes and aims to clarify what grounds, if any, justify this selection. Such analyses overlook the fact that in scientific contexts multiple factors are often selected as the important causes of some outcome. This analysis examines a multicausal case in which factors in causal pathways are selected in biological explanations. This work provides a novel analysis of the pathway concept, its role in causal selection, and the rationale behind this selection. It is argued that this rationale is guided by principled considerations, which have been overlooked in the extant literature.

Research on diseases such as cancer reveals that primary mechanisms, which have been the focus of study by the new mechanists in philosophy of science, are often subject to control by other mechanisms. Cancer cells employ the same primary mechanisms as healthy cells but control them differently. I use cancer research to highlight just how widespread control is in individual cells. To provide a framework for understanding control, I reconceptualize mechanisms as imposing constraints on flows of free energy, with control mechanisms operating on flexible constraints in primary mechanisms. I argue that control mechanisms themselves often form complex, integrated networks.

Two strategies for using a model as “null” are distinguished. Null modeling evaluates whether a process is causally responsible for a pattern by testing it against a null model. Baseline modeling measures the relative significance of various processes responsible for a pattern by detecting deviations from a baseline model. When these strategies are conflated, models are illegitimately privileged as accepted until rejected. I illustrate this using the neutral theory of ecology and draw general lessons from this case. First, scientists cannot draw certain conclusions using null modeling. Second, these conclusions follow using baseline modeling, but doing so requires more evidence.

Mathematicians, physicists, and philosophers of physics often look to the symmetries of an object for insight into the structure and constitution of the object. My aim in this article is to explain why this practice is successful. In order to do so, I present a collection of results that are closely related to (and, in a sense, generalizations of) Beth’s and Svenonius’s theorems.

Recently, Lisa Feldman Barrett and colleagues have introduced the Theory of Constructed Emotions (TCE), in which emotions are constituted by a process of categorizing the self as being in an emotional state. The view, however, has several counterintuitive implications: for instance, a person can have multiple distinct emotions at once. Further, the TCE concludes that emotions are constitutively social phenomena. In this article, I explicate the TCE*, which, while substantially similar to the TCE, makes several distinct claims aimed at avoiding the counterintuitive implications plaguing the TCE. Further, because of the changes that comprise the TCE*, emotions are not constitutively social phenomena.

Daniel M. Hausman holds that preferences in economics are total subjective comparative evaluations—subjective judgments to the effect that something is better than something else all things told—and that economists are right to employ this conception of preference. Here, I argue against both parts of Hausman’s thesis. The failure of Hausman’s account, I continue, reflects a deeper problem, that is, that preferences in economics do not need an explicit definition of the kind that he seeks. Nonetheless, Hausman’s labors were not in vain: his accomplishment is that he has articulated a useful model of the theory.

Proponents of IBE claim that the ability of a hypothesis to explain a range of phenomena in a unifying way contributes to the hypothesis’s credibility in light of these phenomena. I propose a Bayesian justification of this claim that reveals a hitherto unnoticed role for explanatory unification in evaluating the plausibility of a hypothesis: considerations of explanatory unification enter into the determination of a hypothesis’s prior by affecting its ‘explanatory coherence’, that is, the extent to which the hypothesis offers mutually cohesive explanations of various phenomena.

Kevin Elliott and others separate two common arguments for the legitimacy of societal values in scientific reasoning as the gap and the error arguments (respectively, the arguments from underdetermination and from inductive risk). This article poses two questions: How are these two arguments related, and what can we learn from their interrelation? I contend that we can better understand the error argument as nested within the gap because the error is a limited case of the gap with narrower features. Furthermore, this nestedness provides philosophers with conceptual tools for analyzing more robustly how values pervade science.