A “Zanzibar” is an island of measurement values that internally cohere, but are detached from independent contact with reality. One manifestation of Zanzibars is through “bandwagon effects,” the tendency of contemporaneous measurements to agree. Bandwagons illustrate how the otherwise virtuous drive towards coherence can have negative epistemic consequences. I argue that precision is an epistemic virtue that mitigates against bandwagon effects and illustrate this claim with a case study from the history of measurements of c. This precision-first reasoning motivates the practice of blind data analysis in bleeding edge precision measurement, where outcomes can point the way to new physics.

In the quest to discover the neural bases of cognition, rigorous behavioral tools are equally as important as sophisticated tools for neural intervention. This paper evaluates several episodes in the development of a novel behavioral tool for rodent cognitive testing, the rodent touchscreen operant chamber. Using conceptual tools on offer in the philosophical literature on exploratory experimentation and control, I illuminate how optimization of this behavioral tool and an understanding of the causal knowledge it may be used to generate historically has involved dynamic interplay between community-driven exploratory practices operating in parallel with causal hypothesis-driven research.

Numerous philosophers of science have argued that the incentive structure of science is a major contributor to the replication crisis. In this article I review FiveThirtyEight’s election forecasting model and show that it confronts similar problems in political polling and successfully mitigates them, not by changing the incentives, but by weighting a pollster’s effect on the model by their past reliability. I argue that a similar weighting procedure holds a promising approach to addressing the replication crisis in science.

Merton envisioned his norms of science at a time when peer-reviewed journals controlled scientific communication. Technologies for sharing and finding content have since divorced the certification and amplification of science, generating systemic vulnerabilities. Certified amplification—a new Mertonian-styled norm—enjoins their recoupling and introduces a taxonomy of strategies adopted by institutions to close the certification-amplification gap, including the proportioning of the one to the other. Examples illustrating each taxonomic type collectively paint a picture of an ethos employing a rich range of certification and amplification techniques and emerging in a decentralized fashion across heterogeneous objects, communication modalities, and institutions.

It is a plausible speculation that conventional choices in outcome measures might influence the results of meta-analyses. We test that speculation by simulating data from trials on antidepressants. We vary real drug effectiveness while modulating conventional values for outcome measures. We had previously shown that one conventional choice used in meta-analyses of antidepressants falls in a narrow range of values that maximize estimates of effectiveness. Our present analysis investigates why this phenomenon occurs. Moreover, our results suggest the superiority of absolute outcome measures over relative measures. This research program can be extended to test numerous other aspects of clinical research.

Models typically have actual, existing targets. However, some models are viewed as having non-actual targets. I argue that this interpretation comes at various costs and propose an alternative that fares better along two dimensions: (1) agreement with practice and (2) ontological and epistemological parsimony. My proposal is that many of these models actually have actual targets.

I argue that Open Science as currently conceptualized and implemented does not take sufficient account of epistemic diversity within research. I use three case studies to exemplify how Open Science threatens to privilege some forms of inquiry over others, thus exasperating divides within and across systems of practice, and overlooking important sources and forms of epistemic diversity. Building on insights from pluralist philosophy, I then identify four aspects of diverse research practices that should serve as reference points for debates around Open Science: (1) specificity to local conditions, (2) entrenchment within repertoires, (3) permeability to newcomers, and (4) demarcation strategies.

Scientists imagine for epistemic reasons, and these imaginings can be better or worse. But what does it mean for an imagining to be epistemically better or worse? There are at least three metaepistemological frameworks that offer different answers to this question: epistemological consequentialism, deontic epistemology, and virtue epistemology. This paper presents empirical evidence that scientists adopt each of these different epistemic frameworks with respect to imagination, but argues that the way they do this is best explained if scientists are fundamentally epistemic consequentialists about imagination.

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.