This article analyzes the recent debate surrounding inclusive fitness and argues that certain limitations ascribed to it by critics—such as requiring weak selection or providing dynamically insufficient models—are better thought of as limitations of the methodological framework most often used with inclusive fitness (quantitative genetics). In support of this, I show how inclusive fitness can be used with the replicator dynamics (of evolutionary game theory, a methodological framework preferred by inclusive fitness critics). I conclude that much of the debate is best understood as being about the orthogonal issue of using abstract versus idealized models.

Many authors have posited an “object file” system, which underlies perceptual selection and tracking of objects. Several have proposed that this system internalizes principles specifying what counts as an object and relies on them during tracking. Here I consider a popular view on which the object file system is tuned to entities that satisfy principles of three-dimensionality, cohesion, and boundedness. I argue that the evidence gathered in support of this view is consistent with a more permissive view on which object files select and track according to well-known perceptual organization criteria. Further evidence supplies positive support for the permissive view.

The goal of this article is to address the problem of inconsistent models and the challenge it poses for perspectivism. I analyze the argument, draw attention to some hidden premises behind it, and deflate them. Then I introduce the notion of perspectival models as a distinctive class of modeling practices whose primary function is exploratory. I illustrate perspectival modeling with two examples taken from contemporary high-energy physics at the Large Hadron Collider at the European Organization for Nuclear Research (CERN), which are designed to show how a plurality of seemingly incompatible models (suitably understood) is methodologically crucial to advance the realist quest in cutting-edge areas of scientific inquiry.

Most philosophical work on causal selection examines the selection of single causes and aims to clarify what grounds, if any, justify this selection. Such analyses overlook the fact that in scientific contexts multiple factors are often selected as the important causes of some outcome. This analysis examines a multicausal case in which factors in causal pathways are selected in biological explanations. This work provides a novel analysis of the pathway concept, its role in causal selection, and the rationale behind this selection. It is argued that this rationale is guided by principled considerations, which have been overlooked in the extant literature.

Research on diseases such as cancer reveals that primary mechanisms, which have been the focus of study by the new mechanists in philosophy of science, are often subject to control by other mechanisms. Cancer cells employ the same primary mechanisms as healthy cells but control them differently. I use cancer research to highlight just how widespread control is in individual cells. To provide a framework for understanding control, I reconceptualize mechanisms as imposing constraints on flows of free energy, with control mechanisms operating on flexible constraints in primary mechanisms. I argue that control mechanisms themselves often form complex, integrated networks.

This article (i) uncovers a foundational relationship between the ‘gauge symmetry’ of a Newton-Cartan theory and the celebrated Trautman Recovery Theorem and (ii) explores its implications for recent philosophical work on Newton-Cartan gravitation.

This article argues that the controversy over genetically modified crops is best understood not in terms of the supposed bias, dishonesty, irrationality, or ignorance on the part of proponents or critics, but rather in terms of differences in values. To do this, the article draws on and extends recent work of the role of values and interests in science, focusing particularly on inductive risk and epistemic risk, and it shows how the GMO debate can help to further our understanding of the various epistemic risks that are present in science and how these risks might be managed.

This article uses the case study of ethnobiological classification to develop a positive and a negative thesis about the state of natural kind debates. On the one hand, I argue that current accounts of natural kinds can be integrated in a multidimensional framework that advances understanding of classificatory practices in ethnobiology. On the other hand, I argue that such a multidimensional framework does not leave any substantial work for the notion “natural kind” and that attempts to formulate a general account of naturalness have become an obstacle to understanding classificatory practices.

This article considers the temporal dimension of data processing and use and the ways in which it affects the production and interpretation of knowledge claims. I start by distinguishing the time at which data collection, dissemination, and analysis occur (Data time, or Dt) from the time in which the phenomena for which data serve as evidence operate (Phenomena time, or Pt). Building on the analysis of two examples of data reuse from modeling and experimental practices in biology, I then argue that Dt affects how researchers (1) select and interpret data as evidence and (2) identify and understand phenomena.

It is commonly claimed that the universality of critical phenomena is explained through particular applications of the renormalization group (RG). This article has three aims: to clarify the structure of the explanation of universality, to discuss the physics of such RG explanations, and to examine the extent to which universality is thus explained. The derivation of critical exponents proceeds via a real-space or a field-theoretic approach to the RG. Building on work by Mainwood, this article argues that these approaches ought to be distinguished: while the field-theoretic approach explains universality, the real-space approach fails to provide an adequate explanation.

Two strategies for using a model as “null” are distinguished. Null modeling evaluates whether a process is causally responsible for a pattern by testing it against a null model. Baseline modeling measures the relative significance of various processes responsible for a pattern by detecting deviations from a baseline model. When these strategies are conflated, models are illegitimately privileged as accepted until rejected. I illustrate this using the neutral theory of ecology and draw general lessons from this case. First, scientists cannot draw certain conclusions using null modeling. Second, these conclusions follow using baseline modeling, but doing so requires more evidence.

This article investigates epistemological aspects of scientists’ reuse of empirical data over decades and centuries. Giving examples, I discuss three respects in which empirical data are historical entities and the implications for the notion of data reuse. First, any data reuse necessitates metadata, which specify the data’s circumstances of origin. Second, interpretation of historical data often requires the tools of humanities disciplines, which produce a further historicization of data. Finally, some qualitative social scientists hold that data are personal to the researcher who coconstructs them in the research process and are therefore skeptical about the prospects of reusing data.

Gordon Belot argues that Bayesian theory is epistemologically immodest. In response, we show that the topological conditions that underpin his criticisms of asymptotic Bayesian conditioning are self-defeating. They require extreme a priori credences regarding, for example, the limiting behavior of observed relative frequencies. We offer a different explication of Bayesian modesty using a goal of consensus: rival scientific opinions should be responsive to new facts as a way to resolve their disputes. Also we address Adam Elga’s rebuttal to Belot’s analysis, which focuses attention on the role that the assumption of countable additivity plays in Belot’s criticisms.

We use game theoretic models to take an in-depth look at the dynamics of discrimination and academic collaboration. We find that in collaboration networks, small minority groups may be more likely to end up being discriminated against while collaborating. We also find that discrimination can lead members of different social groups to mostly collaborate with in-group members, decreasing the effective diversity of the social network. Drawing on previous work, we discuss how decreases in the diversity of scientific collaborations might negatively affect the progress of epistemic communities.