Compared to the massive literature from other disciplinary perspectives on interdisciplinarity, philosophy of science is only slowly beginning to pay systematic attention to this powerful trend in contemporary science. The paper provides some metaphilosophical reflections on the emerging “Philosophy of Interdisciplinarity”. What? I propose a conception of PhID that has the qualities of being broad and neutral as well as stemming from within the agenda of philosophy of science. It will investigate features of science that reveal themselves when scientific disciplines (...) are viewed in comparison or in contact with one another. PhID will therefore generate two kinds of information: comparative and contactual. Comparative information is about the similarities and differences between disciplines, while contactual information is about what happens and why when disciplines get in contact with each other. Virtually all issues and resources within the philosophy of science can be mobilized to bear on the project, including philosophical accounts of models, explanations, justification, evidence, progress, values, demarcation, incommensurability, and so on. Given that scientific disciplines are institutional entities, resources available in social epistemology and social ontology will also have to be invoked. Why? Establishing PhID is presently an obvious step to take for several reasons, including the following two. First, ID is an increasingly powerful characteristic of contemporary science and its management, and so it would be inappropriate for an empirically informed philosophy of science to ignore it. Second, contemporary philosophy of science happens to be particularly well equipped for addressing issues of ID thanks to the recent massive work in the more specialized fields of philosophies of special disciplines. How? Given the breadth and heterogeneity of its domain and tasks, the practice of PhID must be heavily collective. It must mobilize multiple competences and it must keep elaborating a systematic agenda. While a lot of new conceptual work is needed, the approach is bound to be emphatically empirical, with a cumulative and mutually complementary series of case studies to be conducted. Among the methods to be employed, good old textual analysis of scientific publications will be supplemented with interviews, ‘experimental’ techniques, participant observation as well as various interventionist approaches. The published work in PhID will often be authored jointly by philosophers and other scholars in science studies as well as practitioners in various scientific disciplines. (shrink)

Computational methods such as computer simulations, Monte Carlo methods, and agent-based modeling have become the dominant techniques in many areas of science. Extending Ourselves contains the first systematic philosophical account of these new methods, and how they require a different approach to scientific method. Paul Humphreys draws a parallel between the ways in which such computational methods have enhanced our abilities to mathematically model the world, and the more familiar ways in which scientific instruments have expanded our access to the (...) empirical world. This expansion forms the basis for a new kind of empiricism, better suited to the needs of science than the older anthropocentric forms of empiricism. Human abilities are no longer the ultimate standard of epistemological correctness.Humphreys also includes arguments for the primacy of properties rather than objects, the need to consider technological constraints when appraising scientific methods, and a detailed account of how the path from computational template to scientific application is constructed. This last feature allows us to hold a form of selective realism in which anti-realist arguments based on formal reconstructions of theories can be avoided. One important consequence of the rise of computational methods is that the traditional organization of the sciences is being replaced by an organization founded on computational templates.Extending Ourselves will be of interest to philosophers of science, epistemologists, and to anyone interested in the role played by computers in modern science. (shrink)

The picture of synthetic biology as a kind of engineering science has largely created the public understanding of this novel field, covering both its promises and risks. In this paper, we will argue that the actual situation is more nuanced and complex. Synthetic biology is a highly interdisciplinary field of research located at the interface of physics, chemistry, biology, and computational science. All of these fields provide concepts, metaphors, mathematical tools, and models, which are typically utilized by synthetic biologists by (...) drawing analogies between the different fields of inquiry. We will study analogical reasoning in synthetic biology through the emergence of the functional meaning of noise, which marks an important shift in how engineering concepts are employed in this field. The notion of noise serves also to highlight the differences between the two branches of synthetic biology: the basic science-oriented branch and the engineering-oriented branch, which differ from each other in the way they draw analogies to various other fields of study. Moreover, we show that fixing the mapping between a source domain and the target domain seems not to be the goal of analogical reasoning in actual scientific practice. (shrink)

Philosophers of science studying scientific practice often consider it a methodological requirement that their conceptualization of "model" closely connects with the understanding and use of models by practicing scientists. Occasionally, this connection has been explicitly made (Hutten 1954, Suppes 1961, Morgan and Morrison 1999, Bailer-Jones 2002, Lehtinen and Kuorikoski 2007, Kuorikoski 2007, Morgan 2012a). These studies have been dominated by a focus on the—relatively similar forms of—mathematical models in physics and economics. Yet it has become increasingly evident that the way (...) models are conceptualized is very different in some other sciences, where philosophers' accounts of models' characteristics and .. (shrink)

The idea that research should become more interdisciplinary has become commonplace. According to influential commentators, the unprecedented complexity of problems such as climate change or the social implications of biomedicine demand interdisciplinary efforts integrating both the social and natural sciences. In this context, the question of whether a given knowledge practice is too disciplinary, or interdisciplinary, or not disciplinary enough has become an issue for governments, research policy makers and funding agencies. Interdisciplinarity, in short, has emerged as a key political (...) preoccupation; yet the term tends to obscure as much as illuminate the diverse practices gathered under its rubric. This volume offers a new approach to theorising interdisciplinarity, showing how the boundaries between the social and natural sciences are being reconfigured. It examines the current preoccupation with interdisciplinarity, notably the ascendance of a particular discourse in which it is associated with a transformation in the relations between science, technology and society. Contributors address attempts to promote collaboration between, on the one hand, the natural sciences and engineering and, on the other, the social sciences, arts and humanities. From ethnography in the IT industry to science and technology studies, environmental science to medical humanities, cybernetics to art-science, the collection interrogates how interdisciplinarity has come to be seen as a solution not only to enhancing relations between science and society, but the pursuit of accountability and the need to foster innovation. Interdisciplinarity is essential reading for scholars, students and policy makers across the social sciences, arts and humanities, including anthropology, geography, sociology, science and technology studies and cultural studies, as well as all those engaged in interdisciplinary research. It will have particular relevance for those concerned with the knowledge economy, science policy, environmental politics, applied anthropology, ELSI research, medical humanities, and art-science. (shrink)

Taking stock of interdisciplinarity as it nears its century mark, the Oxford Handbook of Interdisciplinarity constitutes a major new reference work on the topic of interdisciplinarity, a concept of growing academic and societal importance.

The paper seeks to offer [1] an explication of a concept of economics imperialism, focusing on its epistemic aspects; and [2] criteria for its normative assessment. In regard to [1], the defining notion is that of explanatory unification across disciplinary boundaries. As to [2], three kinds of constraints are proposed. An ontological constraint requires an increased degree of ontological unification in contrast to mere derivational unification. An axiological constraint derives from variation in the perceived relative significance of the facts explained. (...) An epistemological constraint requires strong fallibilism acknowledging a particularly severe epistemic uncertainty and proscribing against over-confident arrogance. (shrink)

Reprint of the original, first published in 1869.

Despite their best efforts, scientists may be unable to construct models that simultaneously exemplify every theoretical virtue. One explanation for this is the existence of tradeoffs: relationships of attenuation that constrain the extent to which models can have such desirable qualities. In this paper, we characterize three types of tradeoffs theorists may confront. These characterizations are then used to examine the relationships between parameter precision and two types of generality. We show that several of these relationships exhibit tradeoffs and discuss (...) what consequences those tradeoffs have for theoretical practice. (shrink)

: This paper contains the analysis of nine interviews with UK scientists on the topic of scientific models. Scientific models are an important, very controversially discussed topic in philosophy of science. A reasonable expectation is that philosophical conceptions of models ought to be in agreement with scientific practice. Questioning practicing scientists on their use of and views on models provides material against which philosophical positions can be measured.

The development of evolutionary game theory is closely linked with two interdisciplinary exchanges: the import of game theory into biology, and the import of biologists’ version of game theory into economics. This paper traces the history of these two import episodes. In each case the investigation covers what exactly was imported, what the motives for the import were, how the imported elements were put to use, and how they related to existing practices in the respective disciplines. Two conclusions emerged from (...) this study. First, concepts derived from the unity of science discussion or the unification accounts of explanation are too strong and too narrow to be useful for analysing these interdisciplinary exchanges. Secondly, biology and economics—at least in relation to EGT—show significant differences in modelling practices: biologists seek to link EGT models to concrete empirical situations, whereas economists pursue conceptual exploration and possible explanation.Keywords: Models; Evolutionary game theory; Interdisciplinarity; Unification; Unity of science; Theory import; Biology; Economics. (shrink)

Some scholars see interdisciplinarity as a special case of a broader unificationist program. They accept the unification of the sciences as a regulative ideal, and derive from this the normative justification of interdisciplinary research practices. The crucial link for this position is the notion of integration: integration increases the cohesion of concepts and practices, and more specifically of explanations, ontologies, methods and data. Interdisciplinary success then consists in the integration of fields or disciplines, and this constitutes success in the sense (...) that unification is epistemically desirable. In contrast to this account, I defend the thesis that successful interdisciplinary interaction does not necessarily imply the integration of these disciplines. I show this at the hand of two cases. In both the case of evolutionary game theory and the case of hyperbolic discounting, genuine interdisciplinary exchange took place. From both exchanges, the respective economic fields emerged substantially altered – it wasn’t just a juxtaposition of disciplines in which disciplinary identities remained unchanged. Yet in neither case did the disciplines integrate. Rather, they developed their own concepts and methods, their own explanations, own ontologies, and their own views of what proper data standards were. Furthermore, the fields that emerged from these exchanges were very successful, if measured at the hand of properties like explanatory success, increase of control, bibliometrics and grant yields. Thus, I argue, there are cases of interdisciplinary success without integration. (shrink)

Much of biological and economic theorizing takes place by modeling, the indirect study of real-world phenomena by the construction and examination of models. Books and articles about biological and economic theory are often books and articles about models, many of which are highly idealized and chosen for their explanatory power and analytical convenience rather than for their fit with known data sets. Philosophers of science have recognized these facts and have developed literatures about the nature of models, modeling, idealization, as (...) well as testing of models and explanation by models, for both biology and economics. The impetus for this special issue came from our recognition that there is remarkably little overlap between the “modeling in biology” and “modeling in economics” literatures, despite many of the same themes appearing in these literatures. (shrink)

Explanatory unification—the urge to “explain much by little”—serves as an ideal of theorizing not only in natural sciences but also in the social sciences, most notably in economics. The ideal is occasionally challenged by appealing to the complexity and diversity of social systems and processes in space and time. This article proposes to accommodate such doubts by making a distinction between two kinds of unification and suggesting that while such doubts may be justified in regard to mere derivational unification (which (...) serves as a formal constraint on theories), it is less justified in the case of ontological unification (which is a result of factual discovery of the actual degree of underlying unity in the world). (shrink)

: The main contribution of this paper to current philosophical and sociological studies on modeling is to analyze modeling as an object-oriented interdisciplinary activity and thus to bring new insights into the wide, heterogeneous discourse on tools, forms and organization of interdisciplinary research. A detailed analysis of interdisciplinarity in the making of models is presented, focusing on long-standing interdisciplinary collaboration between specialists in infectious diseases, mathematicians and computer scientists. The analysis introduces a novel way of studying the elements of the (...) models as carriers of interdisciplinarity. These elements, being functionally interdependent building blocks, evolve during the modeling work and carry the disciplinary tensions in the process. This shows how the long and challenging process of defining and reformulating the object of research is crucial for understanding the dynamics of interdisciplinarity in the making. (shrink)

This book discusses the many issues involved in going beyond disciplinary research practices in science, politics and society, and addresses the complexities of their interface. Polar research is used to show how politics may intermingle with science to safeguard national interests in times of dramatic international change.

Network theory is applied across the sciences to study phenomena as diverse as the spread of SARS, the topology of the cell, the structure of the Internet and job search behaviour. Underlying the study of networks is graph theory. Whether the graph represents a network of neurons, cells, friends or firms, it displays features that exclusively depend on the mathematical properties of the graph itself. However, the way in which graph theory is implemented to the modelling of networks differs significantly (...) across scientific fields. This article compares the economics variant of network theory with those of other fields. It shows how the methodology employed by economists to model networks is shaped by two explanatory desiderata: that the explanandum phenomenon is based on micro-economic foundations and that the explanation is general. (shrink)