My Representing and Intervening (1983) concludes with what it calls an experimental argument for scientific realism about entities. The argument is evidently inapplicable to extragalactic astrophysics, but leaves open the possibility that there might be other grounds for scientific realism in that domain. Here I argue for antirealism in astrophysics, although not for any particular kind of antirealism. The argument is conducted by a detailed examination of some current research. It parallels the last chapter of (1983). Both represent the methodological (...) opinion that abstract or semantic realism/antirealism debates are empty, and typically lead to confused or wrong conclusions because they pay so little attention to the details of a science. (shrink)

This paper examines the process of modelling a complex empirical phenomenon in modern astrophysics: extended extragalactic radio sources. I show that modelling is done piecemeal, addressing selected striking or puzzling features of that phenomenon separately and individually. The result is various independent and separate sub-models concerned only with limited aspects of the same phenomenon. Because the sub-models represent features of the same physical phenomenon, they need to be reasonably consistent with each other - a criterion not always fully adhered to (...) - and there needs to be a way to conceptually `re-unite' the sub-models to form an overall-model. Visualisation, that is, supplying a concrete interpretation of abstract, theoretical sub-models, aids this modelling process. My case study further endorses the view that modelling is `work in progress', i.e. a form of developing knowledge whereby models represent, not replicate, a phenomenon. (shrink)

Traditional frameworks for evaluating scientific models have tended to downplay their exploratory function; instead they emphasize how models are inherently intended for specific phenomena and are to be judged by their ability to predict, reproduce, or explain empirical observations. By contrast, this paper argues that exploration should stand alongside explanation, prediction, and representation as a core function of scientific models. Thus, models often serve as starting points for future inquiry, as proofs of principle, as sources of potential explanations, and as (...) a tool for reassessing the suitability of the target system. This is illustrated by a case study of the varied career of reaction-diffusion models in the study of biological pattern formation, which was initiated by Alan Turing in a classic 1952 paper. Initially regarded as mathematically elegant, but biologically irrelevant, demonstrations of how, in principle, spontaneous pattern formation could occur in an organism, such Turing models have only recently rebounded, thanks to advances in experimental techniques and computational methods. The long-delayed vindication of Turing’s initial model, it is argued, is best explained by recognizing it as an exploratory tool. (shrink)

Traditional frameworks for evaluating scientific models have tended to downplay their exploratory function; instead they emphasize how models are inherently intended for specific phenomena and are to be judged by their ability to predict, reproduce, or explain empirical observations. By contrast, this paper argues that exploration should stand alongside explanation, prediction, and representation as a core function of scientific models. Thus, models often serve as starting points for future inquiry, as proofs of principle, as sources of potential explanations, and as (...) a tool for reassessing the suitability of the target system. This is illustrated by a case study of the varied career of reaction-diffusion models in the study of biological pattern formation, which was initiated by Alan Turing in a classic 1952 paper. Initially regarded as mathematically elegant, but biologically irrelevant, demonstrations of how, in principle, spontaneous pattern formation could occur in an organism, such Turing models have only recently rebounded, thanks to advances in experimental techniques and computational methods. The long-delayed vindication of Turing’s initial model, it is argued, is best explained by recognizing it as an exploratory tool. (shrink)

In this article, I explore the value of philosophy of science for history of science. I start by introducing a distinction between two ways of integrating history and philosophy of science: historical philosophy of science and philosophical history of science. I then offer a critical discussion of Imre Lakatos’s project to bring philosophy of science to bear on historical interpretation. I point out certain flaws in Lakatos’s project, which I consider indicative of what went wrong with PHS in the past. (...) Finally, I put forward my own attempt to bring out the historiographical potential of philosophy of science. Starting from Norwood Russell Hanson’s insight that historical studies of science involve metascientific concepts, I argue that philosophical reflection on those concepts can be historiographically fruitful. I focus on four issues and discuss their significance and utility for historiographical practice. (shrink)