This article introduces the snap hook methodology, a method used notably in astrochemistry as a way to indirectly validate and assess the accuracy of computational calculations in the absence of experimental or observational data. We argue that this methodology has tremendous potential for all computationally intensive scientific fields as a substitute for traditional verification and validation standards when those are not accessible and estimating the reliability of numerical predictions becomes a real difficulty. The goal of this article is to give to this method, which seems to be implicitly relied upon in many areas, a proper formulation, in order for philosophers of science to enter the debate and to highlight its undeniable potential in terms of interdisciplinary facilitation and knowledge transmission.

An influential heuristic for thinking about climate adaptation asserts that “natural” adaptation strategies are the best ones. This heuristic has been roundly criticized but is difficult to dislodge in the absence of an alternative. We introduce a new heuristic that assesses adaptation strategies by looking at their maturity, power, and commitment. Maturity is the extent to which we understand an adaptation strategy’s effects. Power is the size of the effect an adaptation strategy will have. Commitment is the degree to which an adaptation strategy is difficult to test or reverse.

Biomedical deployments of data science capitalize on vast, heterogeneous data sources. This promotes a diversified understanding of what counts as evidence for health-related interventions, beyond the strictures associated with evidence-based medicine. Focusing on COVID-19 transmission and prevention research, I consider the epistemic implications of this diversification of evidence in relation to (1) experimental design, especially the revival of natural experiments as sources of reliable epidemiological knowledge; and (2) modeling practices, particularly the recognition of transdisciplinary expertise as crucial to developing and interpreting data models. Acknowledging such shifts in evidential, experimental, and modeling practices helps avoid harmful applications of data-intensive methods.

“Actionability” is a key concept in precision oncology. Its precise definition, however, remains contested. This article undertakes a philosophical analysis of “actionability” to aid in conceptual clarification. We map distinct concepts of actionability, arguing that each is best understood as a contextually objective category articulated to mitigate risk of “conceptual slippage.” We defend “interactive pluralism,” acknowledging the need for distinct concepts but also for conceptual interaction in practice. This article thus offers insights for both practitioners and philosophers, clarifying approaches to actionability for scientists and clinicians and serving as a case study to test competing views on scientific pluralism.

In recent debates about the replication crisis, two positions have been dominant: one that focuses on methodological reforms and one that focuses on theory building. This paper takes up the suggestion that there might be a deeper difference in play, concerning the ways the very subject matter of psychology is construed by opposing camps, i.e., in terms of stable effects versus in terms of complexity. I argue that each gets something right, but neither is sufficient. My analysis suggests that the context sensitivity of the psychological subject matter needs to be front and center of methodological and theoretical efforts.

The “psychologist’s green thumb” refers to the argument that an experimenter needs an indeterminate set of skills to successfully replicate an effect. This argument is sometimes invoked by psychological researchers to explain away failures of independent replication attempts of their work. In this article, I assess the psychologist’s green thumb as a candidate explanation for individual replication failure and argue that it is potentially costly for psychology as a field. I also present other, more likely reasons for these replication failures. I conclude that appealing to a psychologist’s green thumb is not a convincing explanation for replication failure.

Recent proposals that the substrate of memory is molecular raise questions about where this molecular model stands in relation to the dominant synaptic model of memory. In this article, we address the perceived rivalry between these models and ask whether they can be integrated. We argue that addressing rivalry or integration requires delineating the explananda of synaptic and molecular models, as well as revisiting assumptions about how these models account for their explananda. The perceived rivalry between these models exemplifies epistemic costs that arise when we try to pit explanatory models as rivals or integrate them.

We propose a novel account of evolutionary transitions in individuality as life cycle closure: that is, the emergence of a new embedding life cycle. To characterize this process, we show how the life trajectory of lower-level entities (e.g., cells) can be coarse-grained into classes of a higher-level entity. We argue that only higher-level entities displaying two necessary conditions for the existence of a life cycle (e.g., multicellular organisms) have achieved life cycle closure. Throughout, we illustrate our point with stage-structured demographic models that yield a rigorous characterization of the conditions for life cycle closure.

I characterize a role for “environments” as ecological scaffolding of organism development in the evolution of novelty. I interpret Rainey’s bacterial experimental system for empirically modeling evolutionary transition to multicellularity as an ecological-developmental problem in terms of a formal model of Kauffman’s concept of evolution into the “adjacent possible.” I propose a scenario to interpret scaffolds dynamically, treating them as organisms modeled in the same way as the developing systems they scaffold, rather than as fixed constraint boundary conditions. The scenario suggests avenues for mathematically modeling scaffolding dynamics.

The founder of conservation biology, Michael Soulé, set out a vision for conservation biology that was explicitly value-laden, analogous to cancer-biology. In so doing, he drew on the writings of Aldo Leopold, known among philosophers primarily for his land ethic. Employing and extending the work of Anderson (2004) and Clough (2020), I argue that the Leopoldian views that Soulé was drawing on were the product of the coevolution of descriptive and evaluative beliefs over the course of Leopold’s life, grounded in his experiences, resulting in tested and reliable—albeit defeasible—values underlying conservation biology.

Fitness has taken center stage in debates concerning how best to identify evolutionary transitions in individuality (ETIs). An influential framework proposes that an ETI occurs only when fitness is exported from constituent particles to a collective. We reformulate the conceptual structure of this framework as involving three steps. The culminating step compares “counterfactual” fitnesses against a long-run measure of fitness. This comparison assumes that collective-level fitness mereologically supervenes on particle fitness. However, if this assumption is rigorously enforced, the proposed conditions for identifying ETIs prove to be too weak. We here suggest an alternative model of ETIs centered around traits.

A long-standing problem in understanding goal-directed systems has been the insufficiency of mechanistic explanations to make sense of them. This article offers a solution to this problem. It begins by observing the limitations of mechanistic decompositions when it comes to understanding physical fields. We argue that introducing the field concept, as it has been developed in field theory, alongside mechanisms is able to provide an account of goal directedness in the sciences.

I argue that machine learning (ML) models used in science function as highly idealized toy models. If we treat ML models as a type of highly idealized toy model, then we can deploy standard representational and epistemic strategies from the toy model literature to explain why ML models can still provide epistemic success despite their lack of similarity to their targets.

I propose the “Propositional Account” of effective quantum field theories. According to the Propositional Account, each effective quantum field theory expresses propositions about various physical items: fields, interactions, and more. In addition, two effective quantum field theories are physically equivalent just in case they express the same propositions. As I explain, the Propositional Account is scientifically naturalistic, since it invokes terms and principles from the empirical science of linguistics. And the Propositional Account avoids problems faced by other accounts of the physical contents of effective theories.

Racial discrimination is a multidimensional concept. Yet, subjective measures of racial discrimination focus on particular dimensions (interpersonal over institutional, attributable over ambiguously attributable). I argue that there is path dependence in the development and validation of subjective measures, such that existing scales prevent the development of scales that are better for some purpose. Path dependence can occur when researchers: (1) adopt a coherentist view of measurement, namely, in iteratively refining constructs and measures and (2) employ current psychometric validation practices. The main takeaway is that norms are needed to evaluate the initial refinement of the construct rather than taking it for granted.

Dewar (2019) argues that removing excess structure via “sophistication” can have explanatory benefits to removing excess structure via “reduction.” In this paper, I argue that a more robust reason to prefer sophisticated theories is that they have representational benefits.

Well-known debates among statistical inferential paradigms emerge from conflicting views on the notion of probability. One dominant view understands probability as a representation of sampling variability; another prominent view understands probability as a measure of belief. The former generally describes model parameters as fixed values, in contrast to the latter. We propose that there are actually two versions of a parameter within both paradigms: a fixed unknown value that generated the data and a random version to describe the uncertainty in estimating the unknown value. An inferential approach based on CDs deciphers seemingly conflicting perspectives on parameters and probabilities.

When many statistical hypotheses are evaluated simultaneously, statisticians often recommend adjusting (or “correcting”) standard hypothesis tests. In this article, I (1) distinguish two senses of adjustment, (2) investigate the prudential and epistemic goals that adjustment might achieve, and (3) identify conditions under which a researcher should not adjust for multiplicity in the two senses I identify. I tentatively conclude that the goals of scientists and the public may be misaligned with the decision criteria used to evaluate multiple-testing regimes.