Various causal details of the genetic process of translation have been singled out to account for its privileged status as a ‘code’. We explicate the biological uses of coding talk by characterizing a class of special causal processes in which topological properties are the causally relevant ones. This class contains both the process of translation and communication theoretic coding processes as special cases. We propose a formalism in terms of graphs for expressing our theory of biological codes and discuss its utility in understanding biological systems.

This article suggests a ‘best alternative’ justification of induction (in the sense of Reichenbach) which is based on meta-induction. The meta-inductivist applies the principle of induction to all competing prediction methods which are accessible to her. It is demonstrated, and illustrated by computer simulations, that there exist meta-inductivistic prediction strategies whose success is approximately optimal among all accessible prediction methods in arbitrary possible worlds, and which dominate the success of every noninductive prediction strategy. The proposed justification of meta-induction is mathematically analytical. It implies, however, an a posteriori justification of object-induction based on the experiences in our world.

Skipper and Millstein (2005) argued that existing conceptions of mechanisms failed to “get at” natural selection but left open the possibility that a refined conception of mechanisms could resolve the problems that they identified. I respond to Skipper and Millstein, and argue that while many of their points have merit, their objections can be overcome and that natural selection can be characterized as a mechanism. In making this argument, I discuss the role of regularity in mechanisms, and develop an account of stochastic (i.e., probabilistic) mechanisms. Explaining the phenomenon of adaptation through the mechanism of natural selection illustrates the power and flexibility of using mechanistic strategies to explain natural phenomena.

This article identifies a fundamental distinction in scientific practice: the mismatch between what scientists do and what they state they did when they communicate their findings in their publications. The insight that such a mismatch exists is not new. It was already implied in Hans Reichenbach's distinction between the contexts of discovery and justification, and it is taken for granted across the board in philosophy of science and science studies. But while there is general agreement that the mismatch exists, the epistemological implications of that mismatch are not at all clear. Philosophers, historians, and sociologists of different stripes have expressed widely different views about how one should understand and interpret the relation between what scientists do and what they state they did. This article surveys a number of approaches to the mismatch. Based on this survey, I offer an assessment of the epistemological significance of the mismatch and identify the major meta-epistemological challenges that it poses for the analysis of scientific practice.

The article defends the doctrine that Linnaean taxa, including species, have essences that are, at least partly, underlying intrinsic, mostly genetic, properties. The consensus among philosophers of biology is that such essentialism is deeply wrong, indeed incompatible with Darwinism. I argue that biological generalizations about the morphology, physiology, and behavior of species require structural explanations that must advert to these essential properties. The objection that, according to current “species concepts,” species are relational is rejected. These concepts are primarily concerned with what it is for a kind to be a species and throw little light on the essentialist issue of what it is for an organism to be a member of a particular kind. Finally, the article argues that this essentialism can accommodate features of Darwinism associated with variation and change.

What sort of claims do scientific models make and how do these claims then underwrite empirical successes such as explanations and reliable policy interventions? In this paper I propose answers to these questions for the class of models used throughout the social and biological sciences, namely idealized deductive ones with a causal interpretation. I argue that the two main existing accounts misrepresent how these models are actually used, and propose a new account.