Axel Gelfert

Edgar Allan Poe’s standing as a literary figure, who drew on (and sometimes dabbled in) the scientific debates of his time, makes him an intriguing character for any exploration of the historical interrelationship between science, literature and philosophy. His sprawling ‘prose-poem’ Eureka (1848), in particular, has sometimes been scrutinized for anticipations of later scientific developments. By contrast, the present paper argues that it should be understood as a contribution to the raging debates about scientific methodology at the time. This methodological interest, which is echoed in Poe’s ‘tales of ratiocination’, gives rise to a proposed new mode of—broadly abductive—inference, which Poe attributes to the hybrid figure of the ‘poet-mathematician’. Without creative imagination and intuition, Science would necessarily remain incomplete, evenby its own standards. This concern with imaginative (abductive) inference ties in nicely with his coherentism, which grants pride of place to the twin virtues of Simplicity and Consistency, which must constrain imagination lest it degenerate into mere fancy.

This paper contrasts and compares strategies of model-building in condensed matter physics and biology, with respect to their alleged unequal susceptibility to trade-offs between different theoretical desiderata. It challenges the view, often expressed in the philosophical literature on trade-offs in population biology, that the existence of systematic trade-offs is a feature that is specific to biological models, since unlike physics, biology studies evolved systems that exhibit considerable natural variability. By contrast, I argue that the development of ever more sophisticated experimental, theoretical, and computational methods in physics is beginning to erode this contrast, since condensed matter physics is now in a position to measure, describe, model, and manipulate sample-specific features of individual systems – for example at the mesoscopic level – in a way that accounts for their contingency and heterogeneity. Model-building in certain areas of physics thus turns out to be more akin to modeling in biology than has been supposed and, indeed, has traditionally been the case.

In his influential 1960 paper ‘The Unreasonable Effectiveness of Mathematics in the Natural Sciences’, Eugene P. Wigner raises the question of why something that was developed without concern for empirical facts—mathematics—should turn out to be so powerful in explaining facts about the natural world. Recent philosophy of science has developed ‘Wigner’s puzzle’ in two different directions: First, in relation to the supposed indispensability of mathematical facts to particular scientific explanations and, secondly, in connection with the idea that aesthetic criteria track theoretical desiderata such as empirical success. An important aspect of Wigner’s article has, however, been overlooked in these debates: his worries about the underdetermination of physical theories by mathematical frameworks. The present paper argues that, by restoring this aspect of Wigner’s argument to its proper place, Wigner’s puzzle may become an instructive case study for the teaching of core issues in the philosophy of science and its history.

The present paper argues for a more complete integration between recent “genealogical” approaches to the problem of knowledge and evolutionary accounts of the development of human cognitive capacities and practices. A structural tension is pointed out between, on the one hand, the fact that theexplicandumof genealogical stories is a specifically human trait and, on the other hand, the tacit acknowledgment, shared by all contributors to the debate, that human beings have evolved from non-human beings. Since humans differ from their predecessors in more ways than just the lack of a particular concept or cognitive ability, this casts doubt on the widely shared assumption (the “Constancy Assumption”) that, when constructing a genealogical narrative for a particular concept (e.g., our contemporary concept of knowledge), it is permissible to hold all other factors (e.g., individual “on-board” cognitive capacities) fixed. What is needed instead, I argue, is an ecological perspective that views knowledge as an adaptive response to an evolutionary constellation that allows for a diversity of selective pressures. Several examples of specific conceptual pressures at different stages in human evolution are discussed.

Much of Arnold Hauser’s work on the social history of art and the philosophy of art history is informed by a concern for the cognitive dimension of art. The present paper offers a reconstruction of this aspect of Hauser’s project and identifies areas of overlap with the sociology of knowledge—where the latter is to be understood as both a separate discipline and a going intellectual concern. Following a discussion of Hauser’s personal and intellectual background, as well as of the shifting political and academic setting of his work, the paper addresses one of Hauser’s central questions, viz. how best to square a thoroughgoing commitment to the social nature of art with the reality of successive artistic styles, given that the latter seem to be characterizable on purely formal grounds. This is followed by a discussion of Hauser’s conflicted views on the relation between art, science, and technology. This injects a tension into Hauser’s work, due to his initial reluctance to explain just how the aesthetic and the cognitive realms relate. The final part of the paper, through a closer examination of the analogies and disanalogies that Hauser sees between art history and the history of science, attempts to give a positive answer—“on Hauser’s behalf”, as it were—to the question of whether art may be credited with a specific cognitive dimension of its own, and if so, what its contribution to our cognitive enterprise may consist in

Taking scientific practice as its starting point, this book charts the complex territory of models used in science. It examines what scientific models are and what their function is. Reliance on models is pervasive in science, and scientists often need to construct models in order to explain or predict anything of interest at all. The diversity of kinds of models one finds in science – ranging from toy models and scale models to theoretical and mathematical models – has attracted attention not only from scientists, but also from philosophers, sociologists, and historians of science. This has given rise to a wide variety of case studies that look at the different uses to which models have been put in specific scientific contexts. By exploring current debates on the use and building of models via cutting-edge examples drawn from physics and biology, the book provides broad insight into the methodology of modelling in the natural sciences. It pairs specific arguments with introductory material relating to the ontology and the function of models, and provides some historical context to the debates as well as a sketch of general positions in the philosophy of scientific models in the process.

In his work on the epistemology of testimony, Peter Lipton developed an account of testimonial inference that aimed at descriptive adequacy as well as justificatory sophistication. According to ‘testimonial inference to the best explanation’, we accept what a speaker tells us because the truth of her claim figures in the best explanation of the fact that she made it. In this paper, I argue for a modification of this picture. In particular, I argue that IBE plays a dual role in the management and justification of testimony. On the one hand, the coherence and success of our testimony-based projects provides general abductive support for a default stance of testimonial acceptance; on the other hand, we are justified in rejecting specific testimonial claims whenever the best explanation of the instances of testimony we encounter entails, or makes probable, the falsity or unreliability of the testimony in question.Keywords: Testimony; Inference to the best explanation; Abduction; Epistemic justification.