I defend a formulation of the Ramsey Test with a condition for accepting negations of conditionals. It is implicit in the assumptions of the triviality theorems of Gärdenfors, Harper, and Lewis; and it allows for a unified proof of those theorems, from weaker assumptions about belief revision. This leads to a proof of McGee's thesis that iterated conditionals do not obey modus ponens.

I show why Michael Friedman's idea that we should view new constitutive frameworks introduced in paradigm change as members of a convergent series introduces an uncomfortable tension in his views. It cannot be justified on realist grounds, as this would compromise his Kantian perspective, but his own appeal to a Kantian regulative ideal of reason cannot do the job either. I then explain a way to make better sense of the rationality of paradigm change on what I take to be Friedman's own terms.

This article raises two difficulties that certain approaches to causation have with would-cause counterfactuals. First, there is a problem with David Lewis's semantics of counterfactuals when we ‘suppose in’ some positive event of a certain kind. And, second, there is a problem with embedded counterfactuals. I show that causal-modeling approaches do not have these problems.

Thought experiments are ubiquitous in science and especially prominent in domains in which experimental and observational evidence is scarce. One such domain is the causal analysis of singular events in history. A long-standing tradition that goes back to Max Weber addresses the issue by means of ‘what-if’ counterfactuals. In this paper I give a descriptive account of this widely used method and argue that historians following it examine difference makers rather than causes in the philosopher's sense. While difference making is neither necessary nor sufficient for causation, to establish difference makers is more consistent with the historians’ more ultimate purposes.

Natural kinds are often contrasted with other kinds of scientific kinds, especially functional kinds, because of a presumed categorical difference in explanatory value: supposedly, natural kinds can ground explanations, while other kinds of kinds cannot. I argue against this view of natural kinds by examining a particular type of explanation—mechanistic explanation—and showing that functional kinds do the same work there as traditionally recognized natural kinds are supposed to do in “standard” scientific explanations. Breaking down this categorical distinction between traditional natural kinds and other kinds of kinds, I argue, delivers two goods: It provides us with a view of natural kindhood that does justice to the epistemic roles of kinds in scientific explanations. And it allows us to solve a problem that HPC theory, currently one of the more popular accounts of natural kindhood, confronts.

Bogen and Woodward characterized data as embedded in the context in which they are produced (‘local’) and claims about phenomena as retaining their significance beyond that context (‘nonlocal’). This view does not fit sciences such as biology, which successfully disseminate data via packaging processes that include appropriate labels, vehicles, and human interventions. These processes enhance the evidential scope of data and ensure that claims about phenomena are understood in the same way across research communities. I conclude that the degree of locality of both data and claims about phenomena varies depending on the packaging used to make them travel and on the research setting in which they are used.

It has been argued that biological fitness cannot be defined as expected number of offspring in all contexts. Some authors argue that fitness therefore merely satisfies a common schema or that no unified mathematical characterization of fitness is possible. I argue that comparative fitness must be relativized to an evolutionary effect; thus relativized, fitness can be given a unitary mathematical characterization in terms of probabilities of producing offspring and other effects. Such fitnesses will sometimes be defined in terms of probabilities of effects occurring over the long term, but these probabilities nevertheless concern effects occurring over the short term.

In many domains of biology, explanation takes the form of characterizing the mechanism responsible for a particular phenomenon in a specific biological system. How are such explanations generalized? One important strategy assumes conservation of mechanisms through evolutionary descent. But conservation is seldom complete. In the case discussed, the central mechanism for circadian rhythms in animals was first identified in Drosophila and then extended to mammals. Scientists' working assumption that the clock mechanisms would be conserved both yielded important generalizations and served as a heuristic for discovery, especially when significant differences between the insect and mammalian mechanism were identified.

There has recently been a renewed interest in the “force” interpretation of evolutionary biology. In this article, I present the general structure of the arguments for the force interpretation and identify a problem in its overly permissive conditions for being a Newtonian force. I then attempt a solution that (1) helps to illuminate the difference between forces and other types of causes and (2) makes room for random genetic drift as a force. In particular, I argue that forces are not different in kind from other types of causes but rather that forces are situated on a continuum of causes distinguished by their unifying power.

Sewall Wright and Gustave Malécot developed important theories of isolation by distance. Wright's theory was statistical and Malécot's probabilistic. Because of this mathematical difference, they were not clear about the relationship between their theories. In this paper, I make two points to clarify this relationship. First, I argue that Wright's theory concerns what I call ecological isolation by distance, whereas Malécot's concerns what I call genetic isolation by distance. Second, I suggest that if Wright's theory is interpreted appropriately, a previously unnoticed connection between the two theories emerges.

Recently, Estes and Arnold claimed to have “solved” the paradox of evolutionary stasis; they claim that stabilizing selection, and only stabilizing selection, can explain the patterns of evolutionary divergence observed over “all timescales.” While Estes and Arnold clearly think that they have identified the processes that produce evolutionary stasis, they have not. Instead, Estes and Arnold identify a particular evolutionary pattern but not the processes that produce that pattern. This mistake is important; the slippage between pattern and process is common in population and quantitative genetics and contributes to a persistent misunderstanding of the nature of explanations in evolutionary biology.

A wide range of gene knockout experiments shows that functional stability is an important feature of biological systems. On this backdrop, we present an argument for higher-level causation based on counterfactual dependence. Furthermore, we sketch a metaphysical picture providing resources to explain the metaphysical nature of functional stability, higher-level causation, and the relevant notion of levels. Our account aims to clarify the role empirical results and philosophical assumptions should play in debates about reductionism and higher-level causation. It thereby contributes to the development of a philosophical foundation for systems biology.

James Woodward offers a conception of explanation and mechanism in terms of interventionist counterfactuals. Based on a case from ecology, I show that ecologists’ approach to that case satisfies Woodward's conditions for explanation and mechanism, but his conception does not fully capture what ecologists view as explanatory. The new mechanistic philosophy likewise aims to describe central aspects of mechanisms, but I show that it is not sufficient to account for ecological mechanisms. I argue that in ecology explanation involves identification of invariant and insensitive causal relationships and descriptions of the mechanistic characteristics that make these relations possible.

Comparative genomicists seem to be convinced that the unit of measurement employed in their studies is a gene that drives the function of cells and ultimately organisms. As a result, they have come to some substantive conclusions about how similar humans are to other organisms based on the percentage of genetic makeup they share. I argue that the actual unit of measurement employed in the studies corresponds to a structural rather than a functional gene concept, thus rendering many of the implications drawn from comparative genomic studies largely unwarranted, if not completely mistaken.

Different chemical species are often cited as paradigm examples of structurally delimited natural kinds. While classificatory monism may thus seem plausible for simple molecules, it looks less attractive for complex biological macromolecules. I focus on the case of proteins that are most plausibly individuated by their functions. Is there a single, objective count of proteins? I argue that the vagaries of function individuation infect protein classification. We should be pluralists about macromolecular classification.

Ethological theory standardly attributes representational content to animal signals. In this article I first assess whether Ruth Millikan's teleosemantic theory accounts for the content of animal signals. I conclude that it does not, because many signals do not exhibit the required sort of cooperation between signal-producing and signal-consuming devices. It is then argued that Kim Sterelny's proposal, while not requiring cooperation, sometimes yields the wrong content. Finally, I outline an alternative view, according to which consumers alone are responsible for conferring representational status and determining content. I suggest that consumer-based teleosemantics reconstruct the content of both cooperative and noncooperative signals and explain how a given trait can mean different things to different consumers.

Many cases of gene-environment interaction, or $\mathrm{G}\,\times \mathrm{E}\,$, are misconstrued as evincing a genetic predisposition. I diagnose this misconstrual and then introduce a new concept—interactive predisposition—to correct for the mistake. I conclude by examining how recent debates over screening for individual predispositions are related to older debates about group differences between populations, drawing on the lessons of the latter to inform the former.

Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli and the origin of eukaryotic cells through endosymbiosis.

Paul Churchland has recently offered a novel argument for the “objective reality” of color. The strategy he employs to make this argument is an instance of a more general research program for interpreting perceptual content, “domain-portrayal semantics.” In the first half of the article, I point out some features of color vision that complicate Churchland's conclusion, in particular, the context-sensitive and inferential nature of color perception. In the second half, I examine and defend the general research program, concluding it is naturalistic in a minimal sense and should be of interest to naturalists and nonnaturalists alike.

While many philosophers agree that evolutionary theory has important implications for the study of ethics, there has been no consensus on what these implications are. I argue that we can better understand these implications by examining two related yet distinct issues in evolutionary theory: the evolution of our moral beliefs and the evolution of cooperative behavior. While the prevailing evolutionary account of morality poses a threat to moral realism, a plausible model of how altruism evolved in human beings provides the grounding for a research program that focuses on achieving some of the more practical goals shared by ethicists.