Kinds that share historical properties are dubbed “historical kinds” or “etiological kinds,” and they have some distinctive features. I will try to characterize etiological kinds in general terms and briefly survey some previous philosophical discussions of these kinds. Then I will take a closer look at a few case studies involving different types of etiological kinds. Finally, I will try to understand the rationale for classifying on the basis of etiology, putting forward reasons for classifying phenomena on the basis of diachronic features, thereby making a provisional case for considering at least some etiological kinds to be natural kinds.

Daniel Dennett distinguishes real patterns from bogus patterns by appeal to compressibility. As information theorists have shown, data are compressible if and only if those data exhibit a pattern. Noting that high-level models are much simpler than their low-level counterparts, Dennett interprets high-level models as compressed representations of the fine-grained behavior of their target system. As such, he argues that high-level models depend on patterns in this behavior. Unfortunately, data scientific practice complicates Dennett’s interpretation, undermining the traditional justification for real patterns and suggesting a revised research program for its defenders.

Humans are often altruistic in a variety of contexts, even toward strangers they may never meet again. What explains this behavior? Many argue that kin selection cannot explain it but group selection can. Contra this common line of reasoning, I provide two ways that kin selection might help explain the evolution of broad-scope human altruism: in gene-culture coevolution and in a ‘cultural’ version of kin selection.

In “Resurrecting Biological Essentialism,” I went against the consensus in the philosophy of biology by arguing that a Linnaean taxon, including a species, has an essence that is, at least partly, an underlying intrinsic, mostly genetic, property: this intrinsic nature explains both the truth of generalizations about the phenotypic properties of the taxon and why being in the taxon is explanatory. The current article is a response to two criticisms: that this intrinsic essentialism is at odds with certain biological variations and that this talk of intrinsic essences is an uncalled for metaphysical addition to biology.

Cognitive scientists often describe the mind as constructing and using models of aspects of the environment, but it is not obvious what makes something a model as opposed to a mere representation. The leading proposal among philosophers is that models are structural representations and are therefore distinguished by their format. However, an alternative conception is suggested by recent work in artificial intelligence, on which models are distinguished by their content. This article outlines the two conceptions and argues for the content conception, against the standard philosophical view.

This article offers a characterization of what I call multiple-models juxtaposition (MMJ), a strategy for managing trade-offs among modeling desiderata. MMJ displays models of distinct phenomena together and fulfills different desiderata both in the individual models and by a comparison of those models. I discuss a concrete case from developmental biology, where MMJ coordinates generality and detail. I also clarify the distinction between MMJ and multiple-models idealization (MMI), which also uses multiple models to manage trade-offs among desiderata. MMJ and MMI differ in several points, such as the ways they manage trade-offs and the purposes of using multiple models.

Since the discovery of dark matter in the 1980s, multiple experiments have been set up to detect dark matter particles through some other mode than gravity. Particle physicists provide detailed justifications as to why their experiments should be able to detect dark matter. I show that these justifications take on a structure different from what is often the case in experimental practice, and I argue that this is because of the limited description of dark matter. By illuminating this ‘method-driven logic’, I shed new light on questions surrounding measurement robustness and methodological pluralism in the context of dark matter research.

Conventional wisdom holds that the von Neumann entropy corresponds to thermodynamic entropy, but Meir Hemmo and Orly Shenker have recently argued against this view by attacking von Neumann’s argument. I argue that Hemmo and Shenker’s arguments fail because of several misunderstandings about statistical-mechanical and thermodynamic domains of applicability, about the nature of mixed states, and about the role of approximations in physics. As a result, their arguments fail in all cases: in the single-particle case, the finite-particles case, and the infinite-particles case.

Kirsten Walsh has criticized my interpretation of Newton’s view on the certainty of his new science of color, namely, that it shifted through his long career from one of certainty to probabilism. I defend my view by further considering his statements on certainty in his Optical Lectures, “New Theory about Light and Colors,” Query 31 of the Opticks, and Rule 4 added to the Principia in 1726.