Axel Gelfert

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.

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.

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.