Axel Gelfert

This broad and insightful book presents current scholarship in important subfields of philosophy of science and addresses an interdisciplinary and multidisciplinary readership. It groups carefully selected contributions into the four fields of I) philosophy of physics, II) philosophy of life sciences, III) philosophy of social sciences and values in science, and IV) philosophy of mathematics and formal modeling. Readers will discover research papers by Paul Hoyningen-Huene, Keizo Matsubara, Kian Salimkhani, Andrea Reichenberger, Anne Sophie Meincke, Javier Suárez, Roger Deulofeu, Ludger Jansen, Peter Hucklenbroich, Martin Carrier, Elizaveta Kostrova, Lara Huber, Jens Harbecke, Antonio Piccolomini d'Aragona and Axel Gelfert. This collection fosters dialogue between philosophers of science working in different subfields, and brings readers the finest and latest work across the breadth of the field,illustrating that contemporary philosophy of science has successfully broadened its scope of reflection. It will interest and inspire a wide audience of philosophers as well as scholars of the natural sciences, social sciences and the humanities. The volume shares selected contributions from the prestigious second triennial conference of the German Society for Philosophy of Science/ Gesellschaft für Wissenschaftsphilosophie (GWP.2016, March 8, 2016 - March 11, 2016).

The study of similarity is fundamental to biological inquiry. Many homology concepts have been formulated that function successfully to explain similarity in their native domains, but fail to provide an overarching account applicable to variably interconnected and independent areas of biological research despite the monistic standpoint from which they originate. The use of multiple, explicitly articulated homology concepts, applicable at different levels of the biological hierarchy, allows a more thorough investigation of the nature of biological similarity. Responsible epistemological pluralism as advocated herein is generative of fruitful and innovative biological research, and is appropriate given the metaphysical pluralism that underpins all of biology.

That science is more than the unilinear application of general theories to specific empirical circumstances is, one hopes, no longer something that is controversial or requires detailed argument. To be sure, there were times when devising universally applicable theories was seen as the most worthy task of science, with less lofty activities such as experimentation and scientific modeling being relegated to the underbelly of “proper science.” Arguing for a pluralistic recognition of the diversity of scientific practices, methods, and goals, might—at least on the pages of this journal—amount to preaching to the converted. Yet, once the diversity and heterogeneity of science is acknowledged, the real work only starts:...

Whether or not the results of a computer simulation are credible depends to a large extent on the credibilityCredibility of the underlying conceptual model. If a model has been developed explicitly with the goal of running a computer simulation in mind, the two types of credibilityCredibility may seem deeply intertwined. Yet, often enough, conceptual modelsConceptual model predate the subsequent development of simulation techniques, or were first developed outside the context of computer simulation. In such a situation, the specific contribution that a conceptual model makes to the credibility of a simulation requires considerable analysis. How, then, should we assess the credibility of a conceptual model, and which factors ought to playPlay a role in judging whether simulation results derived on its basis are trustworthy? In order to answer these questions, the present chapter begins with the premise that models are never by themselves credible simpliciter, but acquire credibility within a given context of inquiry, which itself depends on the cognitive interests of the inquirer. Judgments concerning the credibility of a conceptual model thus need to be based partly on a characterization of the intrinsic features of the model, partly on the cognitive goals and interests of its users. This realization helps explain why credible models have been variously understood as adequate representations of real-world target systems, as constructions of “credible worlds”Credible world that display internal coherence, and as exploratory tools that may aid our understanding even before a well-developed underlying theory takes shape.

Among contemporary epistemologists of testimony, David Hume is standardly regarded as a ‘global reductionist’, where global reductionism requires the hearer to have sufficient first-hand knowledge of the facts in order to individually ascertain the reliability of the testimony in question. In the present paper, I argue that, by construing Hume’s reductionism in too individualistic a fashion, the received view of Hume on testimony is inaccurate at best, and misleading at worst. Overall, Hume is much more willing to regard testimonial acceptance as a natural response to testimony than has traditionally been thought. In particular, Hume believes that indirect evidence of human nature and of the social world around us, can take the place of first-hand evidence of the track record of individual speakers or specific classes of testimony. In developing this interpretation of Hume’s views on testimony, the present paper draws on discussions found in the Treatise, the Enquiry, and in Hume’s writings on historical knowledge. In der zeitgenössischen Debatte um den erkenntnistheoretischen Status zeugnisbasiertenWissens wird gern auf David Hume als den Urheber eines „globalen Reduktionismus“ verwiesen, demzufolge der Zeugnisempfänger über ausreichend empirische Belege für die Verlässlichkeit des betreffenden Zeugnisses verfügen muss. Im vorliegenden Aufsatz soll gezeigt werden, dass die in der Literatur vorherrschende Meinung ein übertrieben „individualistisches“ Bild von Humes Reduktionismus zeichnet; dadurch wird Humes Position in der gegenwärtigen Debatte ungenau und bisweilen irreführend wiedergegeben. Bei genauerer Betrachtung erweist sich Hume als ausgesprochen aufgeschlossen gegenüber dem Akzeptieren fremden Zeugnisses und sieht darin eine Art Grundmuster im testimonialen Umgang mit anderen. Insbesondere konzediert Hume, dass indirekt erworbenes Wissen um die menschliche Natur und die soziale Welt an die Stelle direkter Belege für die Verlässlichkeit einzelner Zeugen treten kann. Die im vorliegenden Aufsatz entwickelte Neuinterpretation stützt sich auf den Treatise, den Enquiry und auf Humes Schriften zum Problem der historischen Erkenntnis.

Epistemologists of testimony have tended to construct highly stylized (so-called “null setting”) examples in support of their respective philosophical positions, the paradigmatic case being the casual request for directions from a random stranger. The present paper analyzes the use of such examples in the early controversy between reductionists and anti-reductionists about testimonial justification. The controversy concerned, on the one hand, the source of whatever epistemic justification our testimony-based beliefs might have, and, on the other hand, the phenomenology of testimonial acceptance and rejection. As it turns out, appeal to “null setting” cases did not resolve, but instead deepened, the theoretical disputes between reductionists and anti-reductionists. This, it is suggested, is because interpreters ‘fill in’ missing details in ways that reflect their own peculiarities in perspective, experience, upbringing, and philosophical outlook. In response, two remedial strategies have been pursued in recent years: First, we could invert the usual strategy and turn to formal contexts, rather than informal settings, as the paradigmatic scenarios for any prospective epistemology of testimony. Second, instead of “null setting” scenarios, we can focus on richly described cases that either include, or are embedded into, sufficient contextual information to allow for educated judgments concerning the reliability and trustworthiness of the testimony and testifiers involved. The prospects of both of these approaches are then discussed and evaluated.

According to one influential view, model-building in science is primarily a matter of simplifying theoretical descriptions of real-world target systems using abstraction and idealization. This view, however, does not adequately capture all types of models. Many contemporary models in the natural and social sciences – from physics to biology to economics – stand in a more tenuous relationship with real-world target systems and have a decidedly stipulative element, in that they create, by fiat, ‘model worlds’ that operate according to some set of specified rules. While such models may be motivated by an interest in actual target phenomena, their validity is not – at least not primarily – to be judged by whether they constitute an empirically adequate representation of any particular empirical system. The present paper compares and contrasts three such types of models: minimal models, toy models, and exploratory models. All three share some characteristics and thus overlap in interesting ways, yet they also exhibit significant differences. It is argued that, in all three cases, modal considerations have an important role to play: by exploring the modal structure of theories and phenomena – that is, by probing possibilities in various ways – such models deepen our understanding and help us gain knowledge not only about what there is in the world, but also about what there could be.

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.

In this article we argue that idealizations and limiting cases in models play an exploratory role in science. Four senses of exploration are presented: exploration of the structure and representational capacities of theory; proof-of-principle demonstrations; potential explanations; and exploring the suitability of target systems. We illustrate our claims through three case studies, including the Aharonov-Bohm effect, the emergence of anyons and fractional quantum statistics, and the Hubbard model of the Mott phase transitions. We end by reflecting on how our case studies and claims compare to accounts of idealization in the philosophy of science literature such as Michael Weisberg’s three-fold taxonomy.

Despite being a new term, ‘fake news’ has evolved rapidly. This paper argues that it should be reserved for cases of deliberate presentation of false or misleading claims as news, where these are misleading by design. The phrase ‘by design’ here refers to systemic features of the design of the sources and channels by which fake news propagates and, thereby, manipulates the audience’s cognitive processes. This prospective definition is then tested: first, by contrasting fake news with other forms of public disinformation; second, by considering whether it helps pinpoint conditions for the proliferation of fake news.

Although the emerging field of synthetic biology looks back on barely a decade of development, the stakes are high. It is a multidisciplinary research field that aims at integrating the life sciences with engineering and the physical/chemical sciences. The common goal is to design and construct novel biological components, functions and systems in order to implement, in a controlled way, biological devices and production systems not necessarily found in nature. Among the many potential applications are novel drugs and pesticides, cancer treatments, biofuels, and new materials. According to the most optimistic visions, synthetic biology may thus lead to a biotechnological revolution by transforming microorganisms into ‘factories’ of sorts, which could eventually displace conventional industrial methods.

The present paper argues for the relevance of Immanuel Kant and the German Enlightenment to contemporary social epistemology. Rather than distancing themselves from the alleged ‘individualism’ of Enlightenment philosophers, social epistemologists would be well-advised to look at the substantive discussion of social-epistemological questions in the works of Kant and other Enlightenment figures. After a brief rebuttal of the received view of the Enlightenment as an intrinsically individualist enterprise, this paper charts the historical trajectory of philosophical discussions of testimony as a source of knowledge, via such philosophers as C. Thomasius, C. A. Crusius, J. M. Chladenius, G. F. Meier, and finally Kant. Building on recent work on Kant's epistemology of testimony, the paper considers Kant's broader contributions to social epistemology. This includes an analysis of Kant's comments on the social basis of contingent epistemic standards, e.g. in the sciences, as well as on problems arising from the management of what Kant calls the growing ‘volume of knowledge’. Special attention is paid to the relation between Kant's views and contemporary problems arising both in the context of education and from our increased reliance on scientific experts.

In his previous books, The Theory of Epistemic Rationality (1987) and Working Without a Net (1993), Richard Foley presented a highly influential account of what it means for one’s beliefs and belief-forming practices to be rational. Developing a positive new account of epistemic rationality, however, has never been Foley’s sole concern. His project is metaepistemological in character as much as it is epistemological. Put crudely, questions such as ‘What makes some beliefs knowledge?’ are of equal importance to Foley as such questions as ‘How is scepticism possible?’. Indeed, given the way in which philosophical debates tend to be shaped, it may be the more fruitful way of tackling a philosophical problem to start from questions of the latter type and work one’s way backward to the fundamental questions that gave rise to the debate in the first place. Such an approach need not be strictly historical; rather, it will be meta-epistemological in that it probes deeply into the possibility of an epistemological theory, its prospective subject matter as well as its limitations. Given the difficulty of constructing a coherent epistemological theory and defending it against the various objections that are standardly run against such theories, it should often prove more viable to illustrate the general meta-epistemological ‘lessons’ by way of referring to previous epistemological theories and the long-standing debates that surround them. Hence, a metaepistemological approach naturally gives rise to an historically informed outlook.

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’.

Synthetic biology presents a challenge to traditional accounts of biology: Whereas traditional biology emphasizes the evolvability, variability, and heterogeneity of living organisms, synthetic biology envisions a future of homogeneous, humanly engineered biological systems that may be combined in modular fashion. The present paper approaches this challenge from the perspective of the epistemology of technoscience. In particular, it is argued that synthetic-biological artifacts lend themselves to an analysis in terms of what has been called ‘thing knowledge’. As such, they should neither be regarded as the simple outcome of applying theoretical knowledge and engineering principles to specific technological problems, nor should they be treated as mere sources of new evidence in the general pursuit of scientific understanding. Instead, synthetic-biological artifacts should be viewed as partly autonomous research objects which, qua their material-biological constitution, embody knowledge about the natural world—knowledge that, in turn, can be accessed via continuous experimental interrogation.

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.

Inner speech is a pervasive feature of our conscious mental lives. Yet its function and character remain an issue of philosophical debate. The present paper focuses on the relation between inner speech and natural language and on the cognitive functions that various contributors have ascribed to inner speech. In particular, it is argued that inner speech does not consist of bare, context-free internal presentations of sentential (or subsentential) content, but rather has an ineliminably perspectival element. The proposed model of inner speech, which characterizes inner speech as akin to the testimony of an inner interlocutor, accounts for this perspectival element and, it is argued, is explanatorily superior, insofar as it better explains, amongst other phenomena, the often condensed character of inner speech.

In recent years, the idea has been gaining ground that our traditional conceptions of knowledge and cognition are unduly limiting, in that they privilege what goes on inside the ‘skin and skull’ of an individual reasoner. Instead, it has been argued, knowledge and cognition need to be understood as embodied, situated, and extended. Whether these various interrelations and dependencies are ‘merely’ causal, or are in a more fundamental sense constitutive of knowledge and cognition, is as much a matter of controversy as the degree to which they pose a challenge to ‘traditional’ conceptions of cognition, knowledge and the mind. In this paper we argue that when the idea of ‘extendedness’ is applied to a core concept in epistemology and the philosophy of science—namely, scientific evidence—things appear to be on a much surer footing. The evidential status of data gathered through extended processes—including its utility as justification or warrant—do not seem to be weakened by virtue of being extended, but instead are often strengthened because of it. Indeed, it is often precisely by virtue of this extendedness that scientific evidence grounds knowledge claims, which individuals may subsequently ascribe to themselves. The functional equivalence between machine-based gathering, filtering, and processing of data and human interpretation and assessment is the crucial factor in deciding whether evidence has been gathered, rather than the distinction between intra- and extracranial processes or individual and social processes. To prioritize biological processes here, and to assert the superiority of human cognitive capacities seems both arbitrary and unwarranted with respect to gathering evidence, and ultimately would lead to an unattractive skepticism about many of the methods used in science to gather evidence. In other words, conceiving of scientific evidence as ‘impersonal’ not only better captures the character of evidence-gathering in practice, but also makes sense of a large amount of evidence-gathering that ‘personal’ accounts fail to either acknowledge or accurately describe. Whilst we suggest it is likely that all internally-distributed evidence-gathering processes are merely contingently internal processes, a significant number of externally-distributed evidence-gathering processes are necessarily externally-distributed. Some evidence can only be gathered by extended epistemic agents