Today, mechanisms and mechanistic explanation are very popular in philosophy of science and are deemed a welcome alternative to laws of nature and deductive-nomological explanation. Starting from Mitchell's pragmatic notion of laws, I cast doubt on their status as a genuine alternative. I argue that (1) all complex-systems mechanisms ontologically must rely on stable regularities, while (2) the reverse need not hold. Analogously, (3) models of mechanisms must incorporate pragmatic laws, while (4) such laws themselves need not always refer to underlying mechanisms. Finally, I show that Mitchell's account is more encompassing than the mechanistic account.

Kurt Gödel criticizes Rudolf Carnap's conventionalism on the grounds that it relies on an empiricist admissibility condition, which, if applied, runs afoul of his second incompleteness theorem. Thomas Ricketts and Michael Friedman respond to Gödel's critique by denying that Carnap is committed to Gödel's admissibility criterion; in effect, they are denying that Carnap is committed to any empirical constraint in the application of his principle of tolerance. I argue in response that Carnap is indeed committed to an empirical requirement vis-à-vis tolerance, a fact that becomes clear upon closer scrutiny of Carnap's relevant writings.

Rayleigh and Ramsay discovered the inert gas argon in the atmospheric air in 1895 using a carefully designed sequence of experiments guided by an informal statistical analysis of the resulting data. The primary objective of this article is to revisit this remarkable historical episode in order to make a case that the error-statistical perspective can be used to bring out and systematize (not to reconstruct) these scientists't resourceful ways and strategies for detecting and eliminating error, as well as dealing with Duhemian ambiguities and underdetermination problems as they arose in the context of their local research settings.

When a technique purports to provide information that is not available to the unaided senses, it is natural to think that the only way to validate that technique is by appealing to a theory of the processes that lead from the object of study to the raw data. In fact, scientists have a variety of strategies for validating their techniques. Those strategies can yield multiple independent arguments that support the validity of the technique. Thus, it is possible to produce a robust body of data with a single technique. I illustrate and support these claims with a historical case study.

Prominent defenders of the extended cognition thesis have looked to evolutionary theory for support. Roughly, the idea is that natural selection leads one to expect that cognitive strategies should exploit the environment, and exploitation of the right sort results in a cognitive system that extends beyond the head of the organism. I argue that proper appreciation of evolutionary theory should create no such expectation. This leaves open whether cognitive systems might in fact bear a relationship to the environment that leads to their extension.

Many empirically minded philosophers have used neuroscientific data to argue against the multiple realization of cognitive functions in existing biological organisms. I argue that neuroscientists themselves have proposed a biologically based concept of multiple realization as an alternative to interpreting empirical findings in terms of one-to-one structure-function mappings. I introduce this concept and its associated research framework and also how some of the main neuroscience-based arguments against multiple realization go wrong.

Denis Walsh has written a striking new defense in this journal of the statisticalist (i.e., noncausalist) position regarding the forces of evolution. I defend the causalist view against his new objections. I argue that the heart of the issue lies in the nature of nonadditive causation. Detailed consideration of that turns out to defuse Walsh's ‘description-dependence’ critique of causalism. Nevertheless, the critique does suggest a basis for reconciliation between the two competing views.

Recently, Barbara Renzi argued that Kuhn's account of scientific change is undermined by mismatches in the analogy that Kuhn supposedly draws between scientific change and biological evolution. We argue that Renzi's criticism is inadequate to Kuhn's account of scientific change, as Kuhn does not draw any precise analogy between the mechanisms of scientific change and biological evolution nor aims to argue that the mechanisms of scientific change and biological evolution are similar in any important respects. Therefore, pointing to mismatches between the central concepts that feature in the descriptions of the two phenomena simply misses the point of Kuhn's analogy.