Axel Gelfert

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.

The present paper argues that ‘mature mathematical formalisms’ play a central role in achieving representation via scientific models. A close discussion of two contemporary accounts of how mathematical models apply—the DDI account (according to which representation depends on the successful interplay of denotation, demonstration and interpretation) and the ‘matching model’ account—reveals shortcomings of each, which, it is argued, suggests that scientific representation may be ineliminably heterogeneous in character. In order to achieve a degree of unification that is compatible with successful representation, scientists often rely on the existence of a ‘mature mathematical formalism’, where the latter refers to a—mathematically formulated and physically interpreted—notational system of locally applicable rules that derive from (but need not be reducible to) fundamental theory. As mathematical formalisms undergo a process of elaboration, enrichment, and entrenchment, they come to embody theoretical, ontological, and methodological commitments and assumptions. Since these are enshrined in the formalism itself, they are no longer readily obvious to either the novice or the proficient user. At the same time as formalisms constrain what may be represented, they also function as inferential and interpretative resources.

When it comes to the public debate about the challenge of global climate change, moral questions are inextricably intertwined with epistemological ones. This manifests itself in at least two distinct ways. First, for a fixed set of epistemic standards, it may be irresponsible to delay policy-making until everyone agrees that such standards have been met. This has been extensively discussed in the literature on the precautionary principle. Second, key actors in the public debate may – for strategic reasons, or out of simple carelessness – engage in the selective variation of epistemic standards in response to evidence that would threaten to undermine their core beliefs, effectively leading to epistemic double standards that make rational agreement impossible. The latter scenario is aptly described as an instance of what Stephen Gardiner calls ‘epistemic corruption’. In the present paper, I aim to give an explanation of the cognitive basis of epistemic corruption and discuss its place within the moral landscape of the debate. In particular, I argue that epistemic corruption often reflects an agent’s attempt to reduce dissonance between incoming scientific evidence and the agent’s ideological core commitments. By selectively discounting the former, agents may attempt to preserve the coherence of the latter, yet in doing so they threaten to damage the integrity of evidence-based deliberation.

This short paper grew out of an observation—made in the course of a larger research project—of a surprising convergence between, on the one hand, certain themes in the work of Mary Hesse and Nelson Goodman in the 1950/60s and, on the other hand, recent work on the representational resources of science, in particular regarding model-based representation. The convergence between these more recent accounts of representation in science and the earlier proposals by Hesse and Goodman consists in the recognition that, in order to secure successful representation in science, collective representational resources must be available. Such resources may take the form of (amongst others) mathematical formalisms, diagrammatic methods, notational rules, or—in the case of material models—conventions regarding the use and manipulation of the constituent parts. More often than not, an abstract characterization of such resources tells only half the story, as they are constituted equally by the pattern of (practical and theoretical) activities—such as instances of manipulation or inference—of the researchers who deploy them. In other words, representational resources need to be sustained by a social practice; this is what renders them collective representational resources in the first place.

The present paper focuses on a particular class of models intended to describe and explain the physical behaviour of systems that consist of a large number of interacting particles. Such many-body models are characterized by a specific Hamiltonian (energy operator) and are frequently employed in condensed matter physics in order to account for such phenomena as magnetism, superconductivity, and other phase transitions. Because of the dual role of many-body models as models of physical sys-tems (with specific physical phenomena as their explananda) as well as mathematical structures, they form an important sub-class of scientific models, from which one can expect to draw general conclusions about the function and functioning of models in science, as well as to gain specific insight into the challenge of modelling complex systems of correlated particles in condensed matter physics. In particular, it is argued that many-body models contribute novel elements to the process of inquiry and open up new avenues of cross-model confirmation and model-based understanding. In contradistinction to phenomenological models, which have received comparatively more philosophical attention, many-body models typically gain their strength not from ‘empirical fit’ per se, but from their being the result of a constructive application of mature formalisms, which frees them from the grip of both ‘fundamental theory’ and an overly narrow conception of ‘empirical success’.