More Heat Than Light is a history of how physics has drawn some inspiration from economics and also how economics has sought to emulate physics, especially with regard to the theory of value. It traces the development of the energy concept in Western physics and its subsequent effect upon the invention and promulgation of neoclassical economics. Any discussion of the standing of economics as a science must include the historical symbiosis between the two disciplines. Starting with the philosopher Emile Meyerson's (...) discussion of the relationship between notions of invariance and causality in the history of science, the book surveys the history of conservation principles in the Western discussion of motion. Recourse to the metaphors of the economy are frequent in physics, and the concepts of value, motion, and body reinforced each other throughout the development of both disciplines, especially with regard to practices of mathematical formalisation. However, in economics subsequent misuse of conservation principles led to serious blunders in the mathematical formalisation of economic theory. The book attempts to provide the reader with sufficient background in the history of physics in order to appreciate its theses. The discussion is technically detailed and complex, and familiarity with calculus is required. (shrink)

Many standard philosophical accounts of scientific practice fail to distinguish between modeling and other types of theory construction. This failure is unfortunate because there are important contrasts among the goals, procedures, and representations employed by modelers and other kinds of theorists. We can see some of these differences intuitively when we reflect on the methods of theorists such as Vito Volterra and Linus Pauling on the one hand, and Charles Darwin and Dimitri Mendeleev on the other. Much of Volterra's and (...) Pauling's work involved modeling; much of Darwin's and Mendeleev's did not. In order to capture this distinction, I consider two examples of theory construction in detail: Volterra's treatment of post-WWI fishery dynamics and Mendeleev's construction of the periodic system. I argue that modeling can be distinguished from other forms of theorizing by the procedures modelers use to represent and to study real-world phenomena: indirect representation and analysis. This differentiation between modelers and non-modelers is one component of the larger project of understanding the practice of modeling, its distinctive features, and the strategies of abstraction and idealization it employs. (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)

This trenchant study analyzes the rise and decline in the quality and format of science in America since World War II. Science-Mart attributes this decline to a powerful neoliberal ideology in the 1980s which saw the fruits of scientific investigation as commodities that could be monetized, rather than as a public good.

Do the changes that have taken place in the structures and methods of the production of scientific knowledge and in our understanding of science over the past fifty years justify speaking of an epochal break in the development of science? Gregor Schiemann addresses this issues through the notion of a scientific revolution and claims that at present we are not witnessing a new scientific revolution. Instead, Schiemann argues that after the so-called Scientific Revolution in the sixteenth and seventeenth centuries, a (...) caesura occurred in the course of the nineteenth century that constituted a departure from the early modern origins of science. This change was characterized by the loss of certainty on the part of the scientists, by the steadily increasing importance of scientific communities (rather than individuals), and by the systematic intertwinement of scientific and societal development. As to present science, Schiemann admits that important changes have occurred, but he denies the conflation of nature and culture: even the OncoMouse is a natural organism, though a seriously damaged one. (shrink)

In this book, Donald Stokes challenges Bush's view and maintains that we can only rebuild the relationship between government and the scientific community when we understand what is wrong with that view.Stokes begins with an analysis of the ...

The question is discussed how noise gained a functional meaning in the context of biology. According to the common view, noise is considered a disturbance or perturbation. I analyze how this understanding changed and what kind of developments during the last 10 years contributed to the emergence of a new understanding of noise. Results gained during a field study in a synthetic biology laboratory show that the emergence of this new research discipline—its highly interdisciplinary character, its new technologies and novel (...) modeling strategies—provided essential impulses, which led to the observed change in the concept of noise. The laboratory study is combined with a historical analysis, which explores the general question as to how concepts travel between disciplines and, specifically, how noise was transferred from engineering and physics into biology. In the past, scientists, such as Lotka and Goodwin, tried to introduce a statistical mechanics into biology and discussed the problem of “unfitting” concepts. The change in the meaning of the concept can be interpreted as a way of making it fit to the novel context in which it is applied. (shrink)

Scientific representation is a currently booming topic, both in analytical philosophy and in history and philosophy of science. The analytical inquiry attempts to come to terms with the relation between theory and world; while historians and philosophers of science aim to develop an account of the practice of model building in the sciences. This article provides a review of recent work within both traditions, and ultimately argues for a practice-based account of the means employed by scientists to effectively achieve representation (...) in the modelling sciences. (shrink)

The semantic, or model-theoretic, approach to theories has recently come under criticism on two fronts: (i) it is claimed that it cannot account for the wide diversity of models employed in scientific practice—a claim which has led some to propose a “deflationary” account of models; (ii) it is further contended that the sense of “model” used by the approach differs from that given in model theory. Our aim in the present work is to articulate a possible response to these claims, (...) drawing on recent developments within the semantic approach itself. Thus, the first is answered by utilizing the notion of a “partial structure”, first introduced in this context by da Costa and French in 1990. The second claim is undermined by consideration of van Fraassen's understanding of “model” which corresponds well with that evinced by modem mathematicians. This latter discussion, in particular, has an impact on the continuing debate regarding the relative merits of the semantic and syntactic views and the developments presented here can be taken to provide further support to the former. (shrink)

Scientists and 'anti-scientists' alike need a more realistic image of science. The traditional mode of research, academic science, is not just a 'method': it is a distinctive culture, whose members win esteem and employment by making public their findings. Fierce competition for credibility is strictly regulated by established practices such as peer review. Highly specialized international communities of independent experts form spontaneously and generate the type of knowledge we call 'scientific' - systematic, theoretical, empirically-tested, quantitative, and so on. Ziman shows (...) that these familiar 'philosophical' features of scientific knowledge are inseparable from the ordinary cognitive capabilities and peculiar social relationships of its producers. This wide-angled close-up of the natural and human sciences recognizes their unique value, whilst revealing the limits of their rationality, reliability, and universal applicability. It also shows how, for better or worse, the new 'post-academic' research culture of teamwork, accountability, etc. is changing these supposedly eternal philosophical characteristics. (shrink)

As we approach the end of the twentieth century, the ways in which knowledge--scientific, social, and cultural--is produced are undergoing fundamental changes. In The New Production of Knowledge, a distinguished group of authors analyze these changes as marking the transition from established institutions, disciplines, practices, and policies to a new mode of knowledge production. Identifying such elements as reflexivity, transdisciplinarity, and heterogeneity within this new mode, the authors consider their impact and interplay with the role of knowledge in social relations. (...) While the knowledge produced by research and development in science and technology is accorded central focus, the authors also outline the changing dimensions of social scientific and humanities knowledge and the relations between the production of knowledge and its dissemination through education. Placing science policy and scientific knowledge within the broader context of contemporary society, this book will be essential reading for all those concerned with the changing nature of knowledge, with the social study of science, with educational systems, and with the correlation between research and development and social, economic, and technological development. "Thought-provoking in its identification of issues that are global in scope; for policy makers in higher education, government, or the commercial sector." --Choice "By their insightful identification of the recent social transformation of knowledge production, the authors have been able to assert new imperatives for policy institutions. The lessons of the book are deep." --Alexis Jacquemin, Universite Catholique de Louvain and Advisor, Foreign Studies Unit, European Commission "Should we celebrate the emergence of a 'post-academic' mode of postmodern knowledge production of the post-industrial society of the 21st Century? Or should we turn away from it with increasing fear and loathing as we also uncover its contradictions. A generation of enthusiasts and/or critics will be indebted to the team of authors for exposing so forcefully the intimate connections between all the cognitive, educational, organizational, and commercial changes that are together revolutionizing the sciences, the technologies, and the humanities. This book will surely spark off a vigorous and fruitful debate about the meaning and purpose of knowledge in our culture." --Professor John Ziman, (Wendy, Janey at Ltd. is going to provide affiliation. Contact if you don't hear from her.) "Jointly authored by a team of distinguished scholars spanning a number of disciplines, The New Production of Knowledge maps the changes in the mode of knowledge production and the global impact of such transformations. . . . The authors succeed . . . at sketching out, in very large strokes, the emerging trends in knowledge production and their implications for future society. The macro focus of the book is a welcome change from the micro obsession of most sociologists of science, who have pretty much deconstructed institutions and even scientific knowledge out of existence." --Contemporary Sociology "This book is a timely contribution to current discussion on the breakdown of and need to renegotiate the social contract between science and society that Vannevar Bush and likeminded architects of science policy constructed immediately after World War II. It goes far beyond the usual scattering of fragmentary insights into changing institutional landscapes, cognitive structures, or quality control mechanisms of present day science, and their linkages with society at large. Tapping a wide variety of sources, the authors provide a coherent picture of important new characteristics that, taken altogether, fundamentally challenge our traditional notions of what academic research is all about. This well-founded analysis of the social redistribution of knowledge and its associated power patterns helps articulate what otherwise tends to remain an--albeit widespread--intuition. Unless they adapt to the new situation, universities in the future will find the centers of gravity of knowledge production moving even further beyond their ken. Knowledge of the social and cognitive dynamics of science in research is much needed as a basis of science and technology policymaking. The New Production of Knowledge does a lot to fill this gap. Another unique feature is its discussion of the humanities, which are usually left out in works coming out of the social studies of science." --Aant Elzinga, University od Goteborg. (shrink)

I argue for an intentional conception of representation in science that requires bringing scientific agents and their intentions into the picture. So the formula is: Agents (1) intend; (2) to use model, M; (3) to represent a part of the world, W; (4) for some purpose, P. This conception legitimates using similarity as the basic relationship between models and the world. Moreover, since just about anything can be used to represent anything else, there can be no unified ontology of models. (...) This whole approach is further supported by a brief exposition of some recent work in cognitive, or usage-based, linguistics. Finally, with all the above as background, I criticize the recently much discussed idea that claims involving scientific models are really fictions. (shrink)

It is now part and parcel of the official philosophical wisdom that models are essential to the acquisition and organisation of scientific knowledge. It is also generally accepted that most models represent their target systems in one way or another. But what does it mean for a model to represent its target system? I begin by introducing three conundrums that a theory of scientific representation has to come to terms with and then address the question of whether the semantic view (...) of theories, which is the currently most widely accepted account of theories and models, provides us with adequate answers to these questions. After having argued in some detail that it does not, I conclude by pointing out in what direction a tenable account of scientific representation might be sought. (shrink)

This paper defends an inferential conception of scientific representation. It approaches the notion of representation in a deflationary spirit, and minimally characterizes the concept as it appears in science by means of two necessary conditions: its essential directionality and its capacity to allow surrogate reasoning and inference. The conception is defended by showing that it successfully meets the objections that make its competitors, such as isomorphism and similarity, untenable. In addition the inferential conception captures the objectivity of the cognitive representations (...) used by science, it sheds light on their truth and completeness, and it explains the source of the analogy between scientific and artistic modes of representation. (shrink)

Scientific models represent aspects of the empirical world. I explore to what extent this representational relationship, given the specific properties of models, can be analysed in terms of propositions to which truth or falsity can be attributed. For example, models frequently entail false propositions despite the fact that they are intended to say something "truthful" about phenomena. I argue that the representational relationship is constituted by model users "agreeing" on the function of a model, on the fit with data and (...) on the aspects of a phenomenon that are modelled. Model users weigh the propositions entailed by a model and from this decide which of these propositions are crucial to the acceptance and continued use of the model. Thus, models represent phenomena when certain propositions they entail are true, but this alone does not exhaust the representational relationship. Therefore, the constraints that produce the choice of the relevant propositions in a model must also be examined and their analysis contributes to understanding the relationship between models and phenomena. (shrink)

Morrison and Morgan argue for a view of models as 'mediating instruments' whose role in scientific theorising goes beyond applying theory. Models are partially independent of both theories and the world. This autonomy allows for a unified account of their role as instruments that allow for exploration of both theories and the world.

The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on the (...) relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation. (shrink)

The ArgumentThe development of modern mathematical biology took place in the 1920s in three main directions: population dynamics, population genetics, and mathematical theory of epidemics. This paper focuses on the first trend which is considered the most significant. Modern mathematical theory of population dynamics is characterized by three aspects (the first two being in a somewhat critical relationship): the emergence of the mathematical modeling approach, the attempt at establishing it in a reductionist-mechanist conceptual framework, and the revival of Darwinism. The (...) first section is devoted to the analysis of the concept of mathematical model and the second one presents an example of a mathematical model (Van der Pol's model of heartbeat) which is a good prototype of that concept. In section 3 the main trends of mathematization of biology and the cultural and scientific contexts in which they found their development are discussed. Sections 4 and 5 are devoted to the contributions of V. Volterra and A. J. Lotka, to the analysis of the differences of their scientific conceptions, and to a discussion of a case study: the priority dispute concerning the discovery of the Volterra-Lotka equations. The historical analysis developed in this paper is also intended to detect the roots of some recent trends of mathematization of biology. (shrink)

This paper distinguishes between causal isolation robustness analysis and independent determination robustness analysis and suggests that the triangulation of the results of different epistemic means or activities serves different functions in them. Circadian clock research is presented as a case of causal isolation robustness analysis: in this field researchers made use of the notion of robustness to isolate the assumed mechanism behind the circadian rhythm. However, in contrast to the earlier philosophical case studies on causal isolation robustness analysis (Weisberg and (...) Reisman in Philos Sci 75:106–131, 2008 ; Kuorikoski et al. in Br J Philos Sci 61:541–567, 2010 ), robustness analysis in the circadian clock research did not remain in the level of mathematical modeling, but it combined it with experimentation on model organisms and a new type of model, a synthetic model. (shrink)

“Basic research” is often used in science policy. It is commonly thought to refer to research that is directed solely toward acquiring new knowledge rather than any more practical objective. Recently, there has been considerable concern about the future of basic research because of purported changes in the nature of knowledge production and increasing pressures on scientists to demonstrate the social and economic benefits of their work. But is there really something special about basic research? The author argues here that (...) “basic research” is a flexible and ambiguous concept that is drawn on by scientists to acquire prestige and resources. She shows that it is used for boundary work and gives examples of the work it does in different situations by drawing on interviews with scientists and policy makers on the category of basic research and the changes they have seen in it over time. (shrink)

Our concern is in explaining how and why models give us useful knowledge. We argue that if we are to understand how models function in the actual scientific practice the representational approach to models proves either misleading or too minimal. We propose turning from the representational approach to the artefactual, which implies also a new unit of analysis: the activity of modelling. Modelling, we suggest, could be approached as a specific practice in which concrete artefacts, i.e., models, are constructed with (...) the help of specific representational means and used in various ways, for example, for the purposes of scientific reasoning, theory construction and design of experiments and other artefacts. Furthermore, in this activity of modelling the model construction is intertwined with the construction of new phenomena, theoretical principles and new scientific concepts. We will illustrate these claims by studying the construction of the ideal heat engine by Sadi Carnot. (shrink)

Scientific representation is a booming field nowadays within the philosophy of science, with many papers published regularly on the topic every year, and several yearly conferences and workshops held on related topics. Historically, the topic originates in two different strands in 20th-century philosophy of science. One strand begins in the 1950s, with philosophical interest in the nature of scientific theories. As the received or “syntactic” view gave way to a “semantic” or “structural” conception, representation progressively gained the center stage. Yet, (...) there is another, older, strand that links representation to fin de siècle modeling debates, particularly in the emerging Bildtheorie of Boltzmann and Hertz, and to the ensuing discussion among philosophers thereafter. Both strands feed into present-day philosophical work on scientific representation. There are a number of different orthogonal questions that philosophers ask regarding representation. One set of questions concerns the nature of the representational relation between theories or models, on the one hand, and the real-world systems they purportedly represent. Such questions lie at the more metaphysical and abstract end of the spectrum—and they are often addressed with the abstract tools of the analytical metaphysician. They constitute what we may refer to as the “analytical inquiry” into representation. On the other hand there are questions regarding the use that scientists put some representations to in practice—these are questions that are best addressed by means of some of the favorite tools of the philosopher of science, including descriptive analysis, illustration by means of case studies, induction, exemplification, inference from practice, etc., and are best referred to as the “practical inquiry” into representation. The notion of representation invoked in such inquiries may be “deflationary” or “substantive”—depending on whether it construes representation as a primitive notion, or as susceptible to further reduction or analysis in terms of something else. (shrink)