How can we make informed decisions about whom to trust given expert disagreement? Can experts on both sides be reasonable in holding conflicting views? Epistemologists have engaged the issue of reasonable expert disagreement generally; here I consider a particular expert dispute in physics, given conflicting accounts from Harry Collins and Allan Franklin, over Joseph Weber's alleged detection of gravitational waves. Finding common ground between Collins and Franklin, I offer a characterization of the gravity wave dispute as both social and evidential. While experimental evidence alone may not have forced resolution of the dispute, there were also credibility-based reasons warranting epistemic trust and distrust. Thus we see how social factors can have evidential significance and how expert disagreement can be reasonable.

This article analyzes the epistemic value of understanding and offers an account of the role of understanding in science. First, I discuss the objectivist view of the relation between explanation and understanding, defended by Carl Hempel and J. D. Trout. I challenge this view by arguing that pragmatic aspects of explanation are crucial for achieving the epistemic aims of science. Subsequently, I present an analysis of these pragmatic aspects in terms of ‘intelligibility’ and a contextual account of scientific understanding based on this notion.

Philosophers of science readily acknowledge that nonepistemic values influence the discovery and pursuit of scientific theories, but many tend to regard these influences as epistemically uninteresting. The present paper challenges this position by identifying three avenues through which nonepistemic values associated with discovery and pursuit in contemporary pollution research influence theory appraisal: (1) by guiding the choice of questions and research projects, (2) by altering experimental design, and (3) by affecting the creation and further investigation of theories or hypotheses. This analysis indicates that the effects of these values are sufficiently complex and epistemically significant to merit further attention.

Helen Longino's “contextual empiricism” is one of the most sophisticated recent attempts to defend a social theory of science. On this view, objectivity and epistemic acceptability require that research be produced within communities that approximate a Millian marketplace of ideas. I argue, however, that Longino's embedding of her epistemology within the framework of Mill's political liberalism implies a conception of individual epistemic agents that is incompatible with her view that scientific knowledge is necessarily social, and I begin to articulate an alternative conception that is better suited to a truly social theory of science.

This article explores Michael Faraday's “Historical Sketch of Electro-Magnetism” as a fruitful source for understanding the epistemic significance of experimentation. In this work Faraday provides a catalog of the numerous experimental and theoretical developments in the early history of electromagnetism. He also describes methods that enable experimentalists to dissociate experimental results from the theoretical commitments generating their research. An analysis of the methods articulated in this sketch is instructive for confronting epistemological worries about the theory-dependence of experimentation.

To what extent does the design of statistical experiments, in particular sequential trials, affect their interpretation? Should postexperimental decisions depend on the observed data alone, or should they account for the used stopping rule? Bayesians and frequentists are apparently deadlocked in their controversy over these questions. To resolve the deadlock, I suggest a three-part strategy that combines conceptual, methodological, and decision-theoretic arguments. This approach maintains the pre-experimental relevance of experimental design and stopping rules but vindicates their evidential, postexperimental irrelevance.

Robustness is a common platitude: hypotheses are better supported with evidence generated by multiple techniques that rely on different background assumptions. Robustness has been put to numerous epistemic tasks, including the demarcation of artifacts from real entities, countering the “experimenter's regress,” and resolving evidential discordance. Despite the frequency of appeals to robustness, the notion itself has received scant critique. Arguments based on robustness can give incorrect conclusions. More worrying is that although robustness may be valuable in ideal evidential circumstances (i.e., when evidence is concordant), often when a variety of evidence is available from multiple techniques, the evidence is discordant.

The present text comments on Steel 2005, in which the author claims to extend from the deterministic to the general case, the result according to which the causal Markov condition is satisfied by systems with jointly independent exogenous variables. I show that Steel's claim cannot be accepted unless one is prepared to abandon standard causal modeling terminology. Correlatively, I argue that the most fruitful aspect of Steel 2005 consists in a realist conception of error terms, and I show how this conception sheds new light on the relationship between determinism and the causal Markov condition.

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.