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.

When solving a complex problem in a group, should group members always choose the best available solution that they are aware of? In this paper, I build simulation models to show that, perhaps surprisingly, a group of agents who individually randomly follow a better available solution than their own can end up outperforming a group of agents who individually always follow the best available solution. This result has implications for the feminist philosophy of science and social epistemology.

Cognitive representations are typically analyzed in terms of content, vehicle, and format. Although current work on formats appeals to intuitions about external representations, such as words and maps, in this article, we develop a computational view of formats that does not rely on intuitions. In our view, formats are individuated by the computational profiles of vehicles, that is, the set of constraints that fix the computational transformations vehicles can undergo. The resulting picture is strongly pluralistic, makes space for a variety of different formats, and is intimately tied to the computational approach to cognition in cognitive science and artificial intelligence.

Among advocates and critics of the “extended evolutionary synthesis” (EES), “reciprocal causation” refers to the view that adaptive evolution is a bidirectional phenomenon, whereby organisms and environments impinge on each other through processes of niche construction and natural selection. I argue that reciprocal causation is incompatible with the view that natural selection is a metaphysically emergent causal process. The emergent character of selection places reciprocal causation on the horns of dilemma, and neither horn can rescue it. I conclude that proponents of the EES must abandon the claim that the process of natural selection features in cycles of reciprocal causation.

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.

Standard narratives about physical teleology say its death was a fait accompli of the Scientific Revolution, but the principle of least action (PLA) has been taken to instantiate teleology’s survival into Enlightenment physics. Other scholars claim this PLA-based teleological metaphysics fell to general philosophical attacks on final causes. None of these narratives fully captures the philosophical interest of its demise. It illustrates a metaphysics being refuted because it could not be coherently modeled in mathematics, hence directly through mathematization and not by philosophical argument or empirical test.

Oversight institutions that hold science and scientists accountable for responsible science have thus far not focused on the general societal impact of science. Responsibility for societal impact is now a pervasive aspect of scientific practice, but accountability remains elusive. I argue here that we should proceed cautiously, and that only clear and precise floors for responsibility should have accountability mechanisms. For the remainder of societal responsibilities in science, we should institutionalize assistive ethical mechanisms, which help scientists meet their responsibilities and share rather than offload ethical labor.

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.

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.

It is increasingly easy to acquire a large amount of data about a problem before formulating a hypothesis. The idea of exploratory data analysis (EDA) predates this situation, but many researchers find themselves appealing to EDA as an explanation of what they are doing with these new resources. Yet there has been relatively little explicit work on what EDA is or why it might be important. I canvass several positions in the literature, find them wanting, and suggest an alternative: exploratory data analysis, when done well, shows the expected value of experimentation for a particular hypothesis.

I defend a novel account of scientific progress centered around justification. Science progresses, on this account, where there is a change in justification. I consider three options for explicating this notion of change-in-justification. This account of scientific progress dispels with a condition for scientific progress that requires accumulation of truth or truth-likeness, and it emphasizes the social nature of scientific justification.

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.

“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.

Some scientific models and some claims about model–target relations are fruitfully diagnosed as dogwhistles. Dogwhistles, broadly speaking, are speech acts that send different, conflicting, and often differentially inflammatory messages to listeners. I distinguish two ways in which scientific models can be dogwhistles: representational dogwhistling and fit-for-purpose dogwhistling. I illustrate both kinds of dogwhistling using an example from computational social science, the diversity trumps ability theorem. I argue that dogwhistling threatens the objectivity of science, and I propose some ameliorative strategies.

Traditionally, the debate about health and disease is characterized as an opposition between naturalism and normativism. However, recent contributions show that theories of health and disease need not be purely naturalistic or normative but may be located somewhere in between. The first purpose of this article is to further advance this line of nuancing. The second purpose is to argue in favor of a specific position, which the added nuances reveal. I call this position subjectively salient naturalism. If one is interested in scientific concepts of health and disease, subjectively salient naturalism is a more plausible position than naturalism.

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.

In noncausal explanations, some noncausal facts (such as mathematical, modal, or metaphysical) are used to explain some physical facts. However, precisely because these explanations abstract away from causal facts, they face two challenges: (1) it is not clear why one rather than the other noncausal explanantia would be relevant for the explanandum; and (2) why would standing in a particular explanatory relation (e.g., “counterfactual dependence,” “constraint,” “entailment,” “constitution,” and “grounding”), and not in some other, be explanatory. I develop an explanatory relevance account that is based on erotetic constraints and show how it addresses these two challenges.

Behavioral welfare economics usually aims at mere means paternalism, helping agents better pursue their own goals. This paper discusses one initially promising way to inform policies addressed at agents who violate expected utility theory (EUT), namely what I call “CPT debiasing.” I argue that this approach is problematic even if we grant the normative authority of EUT, the descriptive adequacy of CPT (cumulative prospect theory), and the general acceptability of means paternalism. First, it is doubtful whether the CPT utility function measures what its proponents intend. Second, by imposing risk neutrality on agents, the approach involves a more problematic paternalism.

Scientists may sometimes generalize from their samples to broader populations when they have not yet sufficiently supported this generalization. Do such hasty generalizations also occur in experimental philosophy? To check, we analyzed 171 experimental philosophy studies published between 2017 and 2023. We found that most studies tested only Western populations but generalized beyond them without justification. There was also no evidence that studies with broader conclusions had larger, more diverse samples, but they nonetheless had higher citation impact. Our analyses reveal important methodological limitations of many experimental philosophy studies and suggest that philosophical training may not protect against hasty generalizations.

I develop a novel account of how nonepistemic aims and values can appropriately influence scientific investigation. At its heart is a process of epistemic projection in which a nonepistemic aim or value is mapped to an epistemic research problem that aligns with that aim or value. Choices in research are then justified as a means of solving that research problem. This epistemic projection approach makes research responsive to nonepistemic aims and values yet remains consistent with the value-free ideal; it could be acceptable to parties on both sides of the values-in-science debate. It also promises to be useful in practice.