According to the semantic view of scientific theories, theories are classes of models. I show that this view—if taken literally—leads to absurdities. In particular, this view equates theories that are distinct, and it distinguishes theories that are equivalent. Furthermore, the semantic view lacks the resources to explicate interesting theoretical relations, such as embeddability of one theory into another. The untenability of the semantic view—as currently formulated—threatens to undermine scientific structuralism.

In this essay I consider, by way of the reflections of accomplished synthetic chemists, how the experimental work of the synthetic organic chemist supports the testing, refinement, and creation of theories of organic chemistry. The role of experiments in modern Baconian sciences like organic chemistry is contrasted with their role in fields of more traditional philosophical concern, such as experimental physics.

Most philosophers of science hold that the laws of nature play an important role in determining which counterfactuals are true. Marc Lange reverses this dependence, arguing that it is the truth of certain counterfactuals that determines which statements are laws. I argue that the context sensitivity of counterfactual sentences makes it impossible for them to determine the laws. Next, I argue that Lange's view cannot avoid additional counterexamples concerning nested counterfactuals. Finally, I argue that Lange's counterfacts, posited as the ultimate ontological ground for the laws of nature, are unsuited to the role he demands of them.

In this article I review attempts to define the term “ad hoc hypothesis,” focusing on the efforts of, among others, Karl Popper, Jarrett Leplin, and Gerald Holton. I conclude that the term is unhelpful; what is “ad hoc” seems to be a judgment made by particular scientists not on the basis of any well-established definition but rather on their individual aesthetic senses. Further, a hypothesis considered ad hoc can apparently be retroactively declared non–ad hoc on the basis of subsequent data, rendering the term meaningless.

Sewall Wright's is a mathematical test used to characterize differences between biological populations and identify causes of those differences. I discuss Cockerham and Weir's popular, empirically successful approach to statistical estimation of , arguing that their assumption that actual populations are sampled from an infinite set of counterfactual populations (with a common ancestral population) supports a view of natural selection and genetic drift as distinct causes. I also argue that the way in which F-statistics and other statistical tests are applied to computer simulations in empirical research shows that selection and drift correspond to differences in objective, causal probabilities.

Many explanations in molecular biology, neuroscience, and other fields of experimental biology describe mechanisms underlying phenomena of interest. These mechanistic explanations (MEx) account for higher-level phenomena in terms of causally active parts and their spatiotemporal organization. What makes such a mechanistic description explanatory? The best-developed answer, Craver's causal-mechanical account, has several weaknesses. It does not fully explicate the target of explanation, interlevel relation, or interactive nonmodular character of many biological mechanisms as we understand them. An alternative account of MEx, emphasizing interdependence among a mechanism's components (‘jointness’), remedies these difficulties.

Michael Weisberg has argued that robustness analysis is useful in evaluating both scientific models and their implications and that robustness analysis comes in three types that share their form and aim. We argue for three cautionary claims regarding Weisberg's reconstruction: (1) robustness analysis may be of limited or no value in evaluating models and their implications; (2) the unificatory reconstruction conceals that the three types of robustness differ in form and role; (3) there is no confluence of types of robustness. We illustrate our central first claim with a case study: the application of Lotka-Volterra models to technology diffusion.

It is proposed that we use the term “approximation” for inexact description of a target system and “idealization” for another system whose properties also provide an inexact description of the target system. Since systems generated by a limiting process can often have quite unexpected—even inconsistent—properties, familiar limit processes used in statistical physics can fail to provide idealizations but merely provide approximations.

Recently, several authors have argued that scientific understanding should be a new topic of philosophical research. In this article, I argue that the three most developed accounts of understanding—Grimm's, de Regt's, and de Regt and Dieks's—can be replaced by earlier ideas about scientific explanation without loss. Indeed, in some cases, such replacements have clear benefits.

We discuss the probabilistic analysis of explanatory power and prove a representation theorem for posterior ratio measures recently advocated by Schupbach and Sprenger. We then prove a representation theorem for an alternative class of measures that rely on the notion of relative probability distance. We end up endorsing the latter, as relative distance measures share the properties of posterior ratio measures that are genuinely appealing, while overcoming a feature that we consider undesirable. They also yield a telling result concerning formal accounts of explanatory power versus inductive confirmation, thereby bridging our discussion to a so-called no-miracle argument.

The relation among method, concept, and theory in science is complicated. I seek to shed light on that relation by considering an instance of it in systematics: the additional challenges phylogeneticists face when reconstructing phylogeny not at a single level but simultaneously at multiple levels of the hierarchy. How does this complicate the task of phylogenetic inference, and how might it inform and shape the conceptual foundations of phylogenetics? This offers a lens through which the interplay of method, theory, and concepts may be understood in systematics, which, in turn, provides data for a more general account.

Reductionists in biology claim that all biological events can be explained in terms of genes and macromolecules alone, while antireductionists argue that some biological events must be explained at a higher level. The literature, however, does not distinguish between different kinds of molecular explanation. The goal of this article is to identify and analyze three such kinds. The analysis of molecular explanations herein carries an important philosophical implication; in shunning crude reductionism and extreme versions of holism, we can combine the insights of thoughtful reductionists with sophisticated antireductionism. When this is done, the question of explanatory reductionism becomes less substantial than often supposed.

In this article, I suggest an argument that seems to show a conflict between the reflection principle and conditionalization. In particular, I show that when the reflection principle is formulated in a standard way, the principle conflicts with Jeffrey conditionalization. And it is argued that the source of the conflict resides in an ambiguity of the standard formulation. Furthermore, I attempt to rescue the principle using Bayes factors. That is, I suggest a new formulation of the principle so as to avoid the conflict.

This article examines a series of Schelling-like models of residential segregation, in which agents prefer to be in the minority. We demonstrate that as long as agents care about the characteristics of their wider community, they tend to end up in a segregated state. We then investigate the process that causes this and conclude that the result hinges on the similarity of informational states among agents of the same type. This is quite different from Schelling-like behavior and suggests (in his terms) that segregation is an instance of macrobehavior that can arise from a wide variety of micromotives.

Homology is a biological sameness relation that is purported to hold in the face of changes in form, composition, and function. In spite of the centrality and importance of homology, there is no consensus on how we should understand this concept. The two leading views of homology, the genealogical and developmental accounts, have significant shortcomings. We propose a new account, the hierarchical-dependency account of homology, which avoids these shortcomings. Furthermore, our account provides for continuity between special, general, and serial homology.

Phylogenetic trees are meant to represent the genealogical history of life and apparently derive their justification from the existence of the tree of life and the fact that evolutionary processes are treelike. However, there are a number of problems for these assumptions. Here it is argued that once we understand the important role that phylogenetic trees play as models that contain idealizations, we can accept these criticisms and deny the reality of the tree while justifying the continued use of trees in phylogenetic theory and preserving nearly all of what defenders of trees have called the “importance of tree thinking.”

Philosophers have traditionally conceived of discovery in terms of internal cognitive acts. Close consideration of Rosalind Franklin's role in the discovery of the DNA double helix, however, reveals some problems with this traditional conception. This article argues that defining discovery in terms of mental operations entails problematic conclusions and excludes acts that should fall within the domain of discovery. It proposes that discovery be expanded to include external acts of making visible. Doing so allows for a reevaluation of Franklin's role in the discovery of the structure of DNA.

The article discusses a well-known type of counterexample to Causal Decision Theory (CDT), in which CDT recommends an option that probably causes the best outcome while itself being evidence that it causes the worst. Intuition disagrees. Many philosophers accept that this justifies either modifying CDT or dropping it altogether. I argue to the contrary that (a) if intuition is right about this case, then transitivity of preference must be violated in another, but (b) this violation is untenable. I conclude that CDT stands.

In this article, I compare the epistemic standing of the function-to-structure inferences found in cognitive neuroscience and of the inferences based on dissociations in neuropsychology. I argue that the former have a poorer epistemic standing than the latter.