Philosophers’ traditional emphasis on theories, theoretical modeling, and explanation misguides research in philosophy of science. Articulating and applying core theories is part of scientific practice, but it is not the essence of scientific practice. Insofar as science has an essence, it is to systematically investigate and learn about what is not yet understood. This lecture analyzes genetics to articulate a broad-practice-centered approach to philosophy of science. It concludes by arguing that this approach can lead to richer, deeper, and more useful philosophies of science, philosophies that can better inform science policy and the public’s understanding of science.

This article defends a version of the Westermarck Effect, integrating existing clinical, biological, and philosophical dimensions to incest avoidance. By focusing on care-based attachment in primates, my formulation of the effect suggests the power of a phylogenetic argument widely accepted by primatologists but not by cultural anthropologists. Identifying postadoption incest as a phenomenon with underexplored evidential value, the article sketches an explanatory strategy for reconciling the effect with the clinical reality of incest, concluding with an explicit argument against culture-first or conventionalist accounts of incest avoidance prevalent in anthropology.

This article is about the analogy between inclusive fitness and utility. In behavioral ecology, it is often assumed that individual organisms behave as if they were “striving” to maximize their inclusive fitness—a measure analogue to the kind of utility function that is used to represent the preferences of rational agents. Here, I explore some conceptual puzzles related to this view and question whether the kind of biological utility posited by the advocates of the “maximizing agent analogy” can be adequately interpreted in inclusive fitness terms.

Whether y obtains under the counterfactual supposition that x is thought to depend on whether y obtains in the most similar world(s) in which x obtains. Graphical causal models have proved useful in developing a principled notion of similarity between worlds, but this notion is limited insofar as it does not apply to counterfactual suppositions about causal structure. Here, we explore the possibility of filling this lacuna by introducing a notion of similarity between causal graphs. Since there are multiple principled senses in which graphs can be similar, we introduce multiple similarity metrics and multiple ways to prioritize these metrics.

I analyze the extent to which classical phase transitions, both first order and continuous, pose a challenge for intertheoretic reduction. My contention is that phase transitions are compatible with a notion of reduction that combines Nagelian reduction and what Thomas Nickles called Reduction2. I also argue that, even if the same approach to reduction applies to both types of phase transitions, there is a crucial difference in their physical treatment: in addition to the thermodynamic limit, in continuous phase transitions there is a second infinite limit involved, which marks an important difference in the reduction of first-order and continuous phase transitions.

This article defends the Negative View of natural selection explanation, according to which natural selection cannot explain of any given individual why it has the traits it does. Over the years, this view has been criticized on empirical, metaphysical, and explanatory grounds. I review the debate and offer additional reasons for rejecting the empirical and metaphysical objections. The explanatory objection, which holds that the Negative View is rooted in a flawed account of contrastive explanation, initially seems plausible. However, I argue that a closer consideration of the desiderata of contrastive explanation shows that this objection fails as well.

This article presents a new framework for the analysis of experimental control. The framework highlights different functions for experimental controls in the realization of an experiment: experimental controls that serve as tests and experimental controls that serve as probes. The approach to experimental control proposed here can illuminate the constitutive role of controls in knowledge production, and it sheds new light on the notion of exploratory experimentation. It also clarifies what can and what cannot be expected from reviewers of scientific journal articles giving feedback on experimental controls.

In the human sciences, experimental research is used to establish causal relationships. However, the extrapolation of these results to the target population can be problematic. To facilitate extrapolation, we propose to use the statistical technique Latent Class Regression Analysis in combination with the analogical reasoning theory for extrapolation. This statistical technique can identify latent classes that differ in the effect of X on Y. In order to extrapolate by means of analogical reasoning, one can characterize the latent classes by a combination of features and then compare these features to features of the target.

The neural reuse framework developed primarily by Michael Anderson proposes that brain regions are involved in multiple and diverse cognitive tasks and that brain regions flexibly and dynamically interact in different combinations to carry out cognitive functioning. We argue that the evidence cited by Anderson and others falls short of supporting the fundamental principles of neural reuse. We map out this problem and provide solutions by drawing on recent advances in network neuroscience, and we argue that methods employed in network neuroscience provide the means to fully engage in a research program operating under the principles of neural reuse.

Philosophers of biology generally agree that causal specificity tracks biological importance: more specific causes are more important. I argue that this correlation does not hold in much research aimed at biomedical intervention. Applying James Woodward’s analysis of causal specificity to the development and design of HIV treatments, I show that drugs that are less causally specific produce better therapeutic outcomes and are more highly valued. Thus, I conclude that the importance of biological causes does not track their specificity but instead varies depending on the biological practice we consider and the goals of that practice.

Deidealization as a topic in its own right has attracted remarkably little philosophical interest despite the extensive literature on idealization. One reason for this is the often implicit assumption that idealization and deidealization are, potentially at least, reversible processes. We question this assumption by analyzing the challenges of deidealization within a menu of four broad categories: deidealizing as recomposing, deidealizing as reformulating, deidealizing as concretizing, and deidealizing as situating. On closer inspection, models turn out much more inflexible than the reversal thesis would have us believe, and deidealization emerges as a creative part of modeling.

I analyze a tension at the core of the mechanistic view of computation generated by its joint commitment to the medium independence of computational vehicles and to computational systems possessing teleological functions to compute. While computation is individuated in medium-independent terms, teleology is sensitive to the constitutive physical properties of vehicles. This tension spells trouble for the mechanistic view, suggesting that there can be no teleological functions to compute. I argue that, once considerations about the relevant function-bestowing factors for computational systems are brought to bear, the tension dissolves: physical systems can have the teleological function to compute.

The life sciences are said to be in the midst of a replication crisis because (1) a majority of published results are irreproducible, and (2) scientists rarely replicate existing data. Here I argue that point 2 of this assessment is flawed because there is a hitherto unidentified form of replication in the experimental life sciences, which I call ‘microreplications’ (MRs). Using a case study from biochemistry, I illustrate how MRs depend on a key element of experimentation, namely, experimental controls. I end by reflecting on what MRs mean for the broader debate about the replication crisis.

This article proposes to explicate theoretical equivalence by supplementing formal equivalence criteria with preservation conditions concerning interpretation. I argue that both the internal structure of models and choices of morphisms are aspects of formalisms that are relevant when it comes to their interpretation. Hence, a formal criterion suitable for being supplemented with preservation conditions concerning interpretation should take these two aspects into account. The two currently most important criteria—generalized definitional equivalence (Morita equivalence) and categorical equivalence—are not optimal in this respect. I put forward a criterion that takes both aspects into account: the criterion of definable categorical equivalence.

Merging of opinions results underwrite Bayesian rejoinders to complaints about the subjective nature of personal probability. Such results establish that sufficiently similar priors achieve consensus in the long run when fed the same increasing stream of evidence. Here, we establish a merging result for sets of probability measures updated by Jeffrey conditioning. This generalizes a number of different merging results in the literature. We also show that such sets converge to a shared, maximally informed opinion. Finally, we demonstrate the philosophical significance of our study by detailing applications to the topics of dynamic coherence, imprecise probabilities, and opinion pooling.

This article provides a detailed analysis and explores the prospects of the arguments for higher-level causal autonomy available for the proponents of the mechanistic framework. Three different arguments (context based, organization based, and constraint based) are distinguished. After clarifying previously raised worries with regard to the first two arguments, the article focuses on the newest version of the third argument that has recently been revived by William Bechtel. By using Bechtel’s own case study, it is shown that not even reference to constraints can establish the causal autonomy of higher mechanistic levels.

Fictionalists believe that scientific models are about model systems that are imaginary. Michael Weisberg has claimed that fictionalism is indefensible because many scientific models are about model systems that are unimaginable. According to a certain account of imagination, what Weisberg says is plausible. According to another, more defensible account of imagination, it is not. I discuss these issues within the context of an allegedly unimaginable model system in ecology, but the conclusions I draw are more general. I then describe how fictionalism should be recast in order to deal with Weisberg’s critique.