Wilfred Sellars appears in Bas C. van Fraassen’s The Scientific Image as one of van Fraassen’s primary realist opponents. However, little attention has been paid to Sellars’s influence on van Fraassen’s constructive empiricism and van Fraassen’s criticisms of Sellarsian realism, despite the significant impact of The Scientific Image on the realism debate and recent renewed interest in Sellars’s scientific realism. In the first half of this article, I argue that reading The Scientific Image against a Sellarsian background helps clarify and (...) justify van Fraassen’s controversial characterization of scientific realism. Sellars and van Fraassen each inhabit a recognizable position in the other’s description of the realism debate and can reciprocally recognize the other as inhabiting the opposing position. The second half of this article reconstructs van Fraassen’s objections to Sellarsian realism in The Scientific Image and its precursors. I argue that van Fraassen’s criticisms of Sellarsian realism significantly developed between his 1975 paper, “Wilfrid Sellars on Scientific Realism” and his 1980 book. (shrink)

Stein has raised a fundamental problem for any attempt to characterize instrumentalism and realism as substantive alternatives. This is the distinguishability problem, which consists in the problem of developing a form of instrumentalism that is substantially different from a plausible realist alternative and the problem of showing that this form of instrumentalism does justice to actual scientific practice. Using Stein’s own discussion of Maxwell, I formulate instrumentalism and realism as a scientist’s attitudes toward models, where an attitude is understood to (...) be a complex of the scientist’s belief and intention regarding models. Developing a case study of Benzer’s modeling practice, I show that each attitude can structure inquiry differently and argue that to understand certain aspects of scientific practice, such as the practice of genetic mapping in Benzer’s work, we sometimes need to appeal to the coexistence of these attitudes. (shrink)

In computer simulations of physical systems, the construction of models is guided, but not determined, by theory. At the same time simulations models are often constructed precisely because data are sparse. They are meant to replace experiments and observations as sources of data about the world; hence they cannot be evaluated simply by being compared to the world. So what can be the source of credibility for simulation models? I argue that the credibility of a simulation model comes not only (...) from the credentials supplied to it by the governing theory, but also from the antecedently established credentials of the model building techniques employed by the simulationists. In other words, there are certain sorts of model building techniques which are taken, in and of themselves, to be reliable. Some of these model building techniques, moreover, incorporate what are sometimes called “falsifications.” These are contrary-to-fact principles that are included in a simulation model and whose inclusion is taken to increase the reliability of the results. The example of a falsification that I consider, called artificial viscosity, is in widespread use in computational fluid dynamics. Artificial viscosity, I argue, is a principle that is successfully and reliably used across a wide domain of fluid dynamical applications, but it does not offer even an approximately “realistic” or true account of fluids. Artificial viscosity, therefore, is a counter-example to the principle that success implies truth – a principle at the foundation of scientific realism. It is an example of reliability without truth. (shrink)

The aim or goal of science has long been discussed by both philosophers of science and scientists themselves. In The Scientific Image (van Fraassen 1980), the aim of science is famously employed to characterize scientific realism and a version of anti-realism, called constructive empiricism. Since the publication of The Scientific Image, however, various changes have occurred in scientific practice. The increasing use of machine learning technology, especially deep learning (DL), is probably one of the major changes in the last decade. (...) This paper aims to explore the implications of DL-aided research for the aim of science debate. I argue that, while the emerging DL-aided research is unlikely to change the state of classic opposition between constructive empiricism and scientific realism, it could offer interesting cases regarding the opposition between those who espouse truth as the aim of science and those oriented to understanding (of the kind that sacrifices truth). (shrink)

John Dewey provided a robust and thorough conception of scientific explanation within his philosophical writing. I provide an exegesis of Dewey's concept of scientific explanation and argue that this concept is important to contemporary philosophy of science for at least two reasons. First, Dewey's conception of scientific explanation avoids the reification of science as an entity separated from practical experience. Second, Dewey supplants the realist-antirealist debate within the philosophical literature concerning explanation, thus moving us beyond the current stalemate within philosophy (...) of science. (shrink)