I show that the standard approach to modeling phenomena involving microscopic classical electrodynamics is mathematically inconsistent. I argue that there is no conceptually unproblematic and consistent theory covering the same phenomena to which this inconsistent theory can be thought of as an approximation; and I propose a set of conditions for the acceptability of inconsistent theories.

Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language-like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms “law” and “theory,” I distinguish principles, specific conditions, models, hypotheses, and generalizations. I argue that scientists use designated similarities between models and aspects of the world to form both hypotheses and generalizations.

Modelling became one of the primary tools of mathematical economic research in the twentieth century, but when we look at examples of how nonanalogical models were first built in economics, both the process of making representations and aspects of the representing relation remain opaque. Like early astronomers, economists have to imagine how the hidden parts of their world are arranged and to make images, that is, create models, to represent how they work. The case of the Edgeworth Box, a model widely used for theoretical work in twentieth-century economics, provides a good example to explore the process of making mathematical representations of the economy. It shows how, in making these new representations, conceptual elements were developed which could not have been represented in the older verbal forms of economics.

Recently, several philosophers have challenged the view that evolutionary theory is usefully understood by way of an analogy with Newtonian mechanics. Instead, they argue that evolutionary theory is merely a statistical theory. According to this alternate approach, natural selection and random genetic drift are not even causes, much less forces. I argue that, properly understood, the Newtonian analogy is unproblematic and illuminating. I defend the view that selection and drift are causes in part by attending to a pair of important distinctions—that between process and product and that between natural selection and fitness.

This paper defends an inferential conception of scientific representation. It approaches the notion of representation in a deflationary spirit, and minimally characterizes the concept as it appears in science by means of two necessary conditions: its essential directionality and its capacity to allow surrogate reasoning and inference. The conception is defended by showing that it successfully meets the objections that make its competitors, such as isomorphism and similarity, untenable. In addition the inferential conception captures the objectivity of the cognitive representations used by science, it sheds light on their truth and completeness, and it explains the source of the analogy between scientific and artistic modes of representation.

A variety of inaccurate claims about Gold's Theorem have appeared in the cognitive science literature. I begin by characterizing the logic of this theorem and its proof. I then examine several claims about Gold's Theorem, and I show why they are false. Finally, I assess the significance of Gold's Theorem for cognitive science.

Nature exhibits a rich variety of adaptations. Cells contain complex biomolecular structures, such as proteins, that are exquisitely adapted to perform specific biological functions. Evolutionary biology explains how biomolecular structures evolve. Intelligent design creationists reject evolutionary explanations. They want to believe that all adaptations in nature are the handiwork of God. Their critics aver that “it ain't necessarily so.” The anthology under review is an excellent display of the issues between intelligent design creationists and their critics. I agree with the critics.

This essay explores the connection between representation and explanation in the sciences. I suggest that scientific representation schemes be viewed as pragmatic tools for acquiring the sort of articulated awareness that is the hallmark of nontrivial knowledge. Crystal field theory in chemistry illustrates this perspective. Certain representations achieve the status of being paradigmatically explanatory, thereby shaping models of intelligibility. In turn, these explanatory preferences serve largely to define and differentiate disciplinary communities by implicitly endorsing particular epistemic aims and values. In this way, the pragmatic nature of explanatory discourse effectively grants its intellectual utility.

Criteria of adequacy for scientific representation of the phenomena pertain to accuracy and truth. But that representation is selective and may require distortion even in the selected parameters; this point is intimately connected with the fact that representation is intentional, and its adequacy relative to its particular purpose. Since observation and measurement are perspectival and the appearances to be saved are perspectival measurement outcomes, the question whether this “saving” is an explanatory relation provides a new focus for the realist/antirealist debate. The Born rule and von Neumann's “collapse” postulate in quantum mechanics provide a telling case for this question in recent physics.

We currently have on offer a variety of different theories of causation. Many are strikingly good, providing detailed and plausible treatments of exemplary cases; and all suffer from clear counterexamples. I argue that, contra Hume and Kant, this is because causation is not a single, monolithic concept. There are different kinds of causal relations imbedded in different kinds of systems, readily described using thick causal concepts. Our causal theories pick out important and useful structures that fit some familiar cases—cases we discover and ones we devise to fit.