Most accounts of mechanism discovery have focused on mechanisms that perform the work required to produce a phenomenon. These mechanisms are often subject to regulation by control mechanisms. Using the example of the molecular motor dynein, this paper examines one process by which such control mechanisms are discovered—the process by which researchers, after identifying additional components required to produce the phenomenon but not directly involved in the work of producing that phenomenon, investigate both how these components act on the original mechanism and how they do so in response to measurements of conditions relevant to the operation of the controlled mechanism.

I provide an account of precision testing in particle physics that makes a virtue of theory-ladenness in experiments. Combining recent work on the philosophy of experimentation with a broader view of the scientific process allows one to understand that the most precise and secure knowledge produced in a mature science cannot be achieved in a theory-independent fashion. I discuss precision tests of the muon’s magnetic moment and effective field theory as a means to repurpose precision tests for exploratory purposes.

Bargaining games have played a prominent role in modeling the evolution of social conventions. Previous models assumed that agents must choose from a predetermined set of strategies. I present a new model of two agents learning in bargaining games in which new strategies must be invented and reinforced. I study the efficiency and fairness of the model outcomes. The outcomes are somewhat efficient, but a significant part of the resource is wasted nonetheless. I implement two forms of forgetting and restrictions to the set of strategies that can be invented.

Synthetic biology has a strong modal dimension that is part and parcel of its engineering agenda. In turning hypothetical biological designs into actual synthetic constructs, synthetic biologists reach toward potential biology instead of concentrating on naturally evolved organisms. We analyze synthetic biology’s goal of making biology easier to engineer through the combinatorial theory of possibility, which reduces possibility to (re)combinations of individuals and their attributes in the actual world. While the last decades of synthetic biology explorations have shown biology to be much more difficult to engineer than originally conceived, synthetic biology has not given up its combinatorial approach.

A major goal of the open science movement is to make more scientific information available to non-specialists, but it has been difficult to meaningfully achieve that goal. In response, this paper argues for two steps: (1) focusing on the scientific content that is most relevant to non-specialist audiences; and (2) packaging that content in meaningful ways for those audiences. The paper uses a case study involving a major environmental health issue (namely, PFAS pollution) to illustrate how the proponents of open science can work with groups like government agencies, nongovernmental organizations, and extension services to implement these two steps.

We provide three innovations to recent debates about whether topological or “network” explanations are a species of mechanistic explanation. First, we more precisely characterize the requirement that all topological explanations are mechanistic explanations and show scientific practice to belie such a requirement. Second, we provide an account that unifies mechanistic and non-mechanistic topological explanations, thereby enriching both the mechanist and autonomist programs by highlighting when and where topological explanations are mechanistic. Third, we defend this view against some powerful mechanist objections. We conclude from this that topological explanations are autonomous from their mechanistic counterparts.

Many contemporary biologists and philosophers of biology admit that selection occurs at any level of the biological hierarchy at which entities showing heritable variation in fitness are found, while insisting that fitness at any level entails differential reproduction, not differential persistence. Those who allow that persistence can be selected doubt that selection on nonreproducing entities can be reiterated, to produce “complex adaptations.” We present here a verbal model of subclones evolving in a simple idealized chemostat that calls into question these suppositions and is usefully explanatory when taken as an analogy to selection for persistence of clades.

The cosmological constant problem stems from treating quantum field theory and general relativity as an effective field theory (EFT). We argue that the problem is a reduction ad absurdum and that one should reject the assumption that general relativity can generically be treated as an EFT. This marks a failure of naturalness and provides an internal signal that EFT methods do not apply in all spacetime domains. We then take an external view, showing that the assumptions for using EFTs are violated in general relativistic domains where Λ is relevant. We highlight some ways forward that do not depend on naturalness.

According to David Albert, there are certain situations where we can cause events that lie in our past. In response to a well-known objection that we never observe backward causation, he argues that there are good reasons why we can’t tell when it obtains. However, I identify another difficulty with Albert’s view: at face value, it has the unattractive consequence that backward causation is not just possible, but rife. In this article, I show how this implication can be blocked. I then use my analysis to defend Albert’s account from a second well-known objection, namely, that it allows us to control the past.

There is a persisting debate about what chemical bonds are and whether they exist. I argue that chemical bonds are real patterns of interactions between subatomic particles. This proposal resolves the problems raised in the context of existing understandings of the chemical bond and provides a novel way to defend the reality of chemical bonds.

Bayesians often appeal to “merging of opinions” to rebut charges of excessive subjectivity. But what happens in the short run is often of greater interest than what happens in the limit. Seidenfeld and coauthors use this observation as motivation for investigating the counterintuitive short run phenomenon of dilation, since, they allege, dilation is “the opposite” of asymptotic merging of opinions. The measure of uncertainty relevant for dilation, however, is not the one relevant for merging of opinions. We explicitly investigate the short run behavior of the metric relevant for merging, and show that dilation is independent of the opposite of merging.

If one has Humean inclinations, what account should one provide for idealization laws? I introduce the currently most popular Humean approach to laws of nature, the best systems account, along with some basic requirements for how to be Humean. I then show why idealization laws are unlikely to be accommodated within this account of laws. Finally, I offer an alternative approach that takes idealization laws to be meta-laws, placing requirements on the theorems of the best system.

According to the Argument for Autonomous Mental Disorder (AAMD), mental disorder can occur in the absence of brain disorder, just as software problems can occur in the absence of hardware problems in a computer. This article argues that the AAMD is unsound. I begin by introducing the “natural dysfunction analysis” of disorder, before outlining the AAMD. I then analyze the necessary conditions for realizer autonomous dysfunction. Building on this, I show that software functions disassociate from hardware functions in a way that mental functions do not disassociate from brain functions. It follows that mental disorders are brain disorders necessarily.

A number of philosophers have thought that fair lotteries over countably infinite sets of outcomes are conceptually incoherent by virtue of violating countable additivity. In this article, I show that a qualitative analogue of this argument generalizes to an argument against the conceptual coherence of a much wider class of fair infinite lotteries—including continuous uniform distributions. I argue that this result suggests that fair lotteries over countably infinite sets of outcomes are no more conceptually problematic than continuous uniform distributions. Along the way, I provide a novel argument for a weak qualitative, epistemic version of regularity.

This paper introduces the Cummins Functions Approach to neural representations (CFA), which aims to capture the notion of representation that is relevant to contemporary neuroscientific practice. CFA shares the common view that “to be a representation of X” amounts to “having the function of tracking X,” but maintains that the relevant notion of function is defined by Robert Cummins’s account. Thus, CFA offers a notion of neural representation that is dependent on explanatory context. I argue that CFA can account for the normativity of neural representations, and defend its dependence on explanations.

Discussion of epistemic responsibility typically focuses on belief formation and actions leading to it. Similarly, accounts of collective epistemic responsibility have addressed the issue of collective belief formation and associated actions. However, there has been little discussion of collective responsibility for preventing epistemic harms, particularly those preventable only by the action of an unorganized group. We propose an account of collective epistemic responsibility that fills this gap. Building on Hindriks’s (2019) account of collective moral responsibility, we introduce the epistemic duty to join forces. Our theory provides an account of the responsibilities of scientists to prevent epistemic harms during inquiry.

We lay groundwork for applying ethnographic methods in philosophy of science. We frame our analysis in terms of two tasks: (1) to identify the benefits of an ethnographic approach in philosophy of science and (2) to structure an ethnographic approach for philosophical investigation best adapted to provide information relevant to philosophical interests and epistemic values. To this end, we advocate for a purpose-guided form of cognitive ethnography that mediates between the explanatory and normative interests of philosophy of science, while maintaining openness and independence when framing such an investigation to achieve robust unbiased results.

A phenomenon resulting from a computationally irreducible (or computationally incompressible) process is supposedly unpredictable except via simulation. This notion of unpredictability has been deployed to formulate recent accounts of computational emergence. Via a technical analysis, I show that computational irreducibility can establish the impossibility of prediction only with respect to maximum standards of precision. By articulating the graded nature of prediction, I show that unpredictability to maximum standards is not equivalent to being unpredictable in general. I conclude that computational irreducibility fails to fulfill its assigned philosophical roles in theories of computational emergence.

Standard proposals of scientific anti-realism assume that the methodology of a scientific research program can be endorsed without accepting its metaphysical commitments. I argue that the distinction between competence, the rules governing one’s language faculty, and performance, or linguistic behavior, precludes this. Linguistic theories aim to describe competence, not performance, and so must be able to distinguish observations reflective of the former from those reflective of the latter. This classification of data makes sense only against the background of a psychologically realistic view of linguistic theory. So the very methodology of the science commits one to its realistic interpretation.

Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced. Philosophers have only begun to comprehend the changed nature of scientific reasoning in this age of “big data.” We analyze data-focused practices in biology and climate modeling, identifying distinct species of data-centric science: phenomena-laden in biology and phenomena-agnostic in climate modeling, each better suited for its own domain of application, though each entail trade-offs. We argue that data-centric practices in science are not monolithic because the opportunities and challenges presented by big data vary across scientific domains.