Philosophers of science increasingly recognize the importance of idealization: the intentional introduction of distortion into scientific theories. Yet this recognition has not yielded consensus about the nature of idealization. e literature of the past thirty years contains disparate characterizations and justifications, but little evidence of convergence towards a common position.

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)

I present a novel approach to the scholarly debate that has arisen with respect to the philosophical import one should infer from scientific accounts of phase transitions by appealing to a distinction between representation understood as denotation, and faithful representation understood as a type of guide to ontology. It is argued that the entire debate is misguided, for it stems from a pseudo-paradox that does not license the type of claims made by scholars and that what is really interesting about (...) phase transitions is the manner by which they force us to rethink issues regarding scientific representation, idealizations, and realism. (shrink)

Renormalization group (RG) methods are an established strategy to explain how it is possible that microscopically different systems exhibit virtually the same macro behavior when undergoing phase-transitions. I argue – in agreement with Robert Batterman – that RG explanations are non-causal explanations. However, Batterman misidentifies the reason why RG explanations are non-causal: it is not the case that an explanation is non- causal if it ignores causal details. I propose an alternative argument, according to which RG explanations are non-causal explanations (...) because their explanatory power is due to the application mathematical operations, which do not serve the purpose of representing causal relations. (shrink)

Despite the close connection between the central limit theorem and renormalization group (RG) methods, the latter should be considered fundamentally distinct from the kind of probabilistic framework associated with statistical mechanics, especially the notion of averaging. The mathematics of RG is grounded in dynamical systems theory rather than probability, which raises important issues with respect to the way RG generates explanations of physical phenomena. I explore these differences and show why RG methods should be considered not just calculational tools but (...) the basis for a physical understanding of complex systems in terms of structural properties and relations. (shrink)

This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), and these relations are coordinated by (...) a model commentary. Surrogate models are often about single mechanisms. They are distinguishable from substitute models, which are examined without any concern about their connections with the target. Models as credible worlds are surrogate models that are believed to provide access to their targets on account of their credibility (of which a few senses are distinguished). (shrink)

This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), and these relations are coordinated by (...) a model commentary. Surrogate models are often about single mechanisms. They are distinguishable from substitute models, which are examined without any concern about their connections with the target. Models as credible worlds are surrogate models that are believed to provide access to their targets on account of their credibility (of which a few senses are distinguished). (shrink)

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)

Phase transitions are well-understood phenomena in thermodynamics (TD), but it turns out that they are mathematically impossible in finite SM systems. Hence, phase transitions are truly emergent properties. They appear again at the thermodynamic limit (TL), i.e., in infinite systems. However, most, if not all, systems in which they occur are finite, so whence comes the justification for taking TL? The problem is then traced back to the TD characterization of phase transitions, and it turns out that the characterization is (...) the result of serious idealizations which under suitable circumstances approximate actual conditions. (shrink)

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)

Batterman and Rice offer an account of “minimal model explanations” and argue against “common features accounts” of those explanations. This paper offers some objections to their proposals and arguments. It argues that their proposal cannot account for the apparent explanatory asymmetry of minimal model explanations. It argues that their account threatens ultimately to collapse into a “common features account.” Finally, it argues against their motivation for thinking that an explanation appealing to “common features” would have to explain the common features’ (...) own prevalence. (shrink)

Scientific representation: A long journey from pragmatics to pragmatics Content Type Journal Article DOI 10.1007/s11016-010-9465-5 Authors James Ladyman, Department of Philosophy, University of Bristol, 9 Woodland Rd, Bristol, BS8 1TB UK Otávio Bueno, Department of Philosophy, University of Miami, Coral Gables, FL 33124, USA Mauricio Suárez, Department of Logic and Philosophy of Science, Complutense University of Madrid, 28040 Madrid, Spain Bas C. van Fraassen, Philosophy Department, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA Journal Metascience Online (...) ISSN 1467-9981 Print ISSN 0815-0796. (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)

This paper examines two recent approaches to the nature and functioning of economic models: models as isolating representations and models as credible constructions. The isolationist view conceives of economic models as surrogate systems that isolate some of the causal mechanisms or tendencies of their respective target systems, while the constructionist approach treats them rather like pure constructions or fictional entities that nevertheless license different kinds of inferences. I will argue that whereas the isolationist view is still tied to the representationalist (...) understanding of models that takes the model-target dyad as the basic unit of analysis, the constructionist perspective can better accommodate the way we actually acquire knowledge through them. Using the example of Tobin’s ultra-Keynesian model I will show how many of the epistemic characteristics of modelling tend to go unrecognised if too much focus is placed on the model-target dyad. (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)

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)

A different way of thinking about how the sciences are organized is suggested by the use of cross-disciplinary computational methods as the organizing unit of science, here called computational templates. The structure of computational models is articulated using the concepts of construction assumptions and correction sets. The existence of these features indicates that certain conventionalist views are incorrect, in particular it suggests that computational models come with an interpretation that cannot be removed as well as a prior justification. A form (...) of selective realism is described which denies that one can simply read the ontological commitments from the theory itself. (shrink)

A different way of thinking about how the sciences are organized is suggested by the use of cross‐disciplinary computational methods as the organizing unit of science, here called computational templates. The structure of computational models is articulated using the concepts of construction assumptions and correction sets. The existence of these features indicates that certain conventionalist views are incorrect, in particular it suggests that computational models come with an interpretation that cannot be removed as well as a prior justification. A form (...) of selective realism is described which denies that one can simply read the ontological commitments from the theory itself. (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)

Discussions of representation in science tend to draw on examples from art. However, such examples need to be handled with care given a) the differences between works of art and scientific theories and b) the accommodation of these examples within certain philosophies of art. I shall examine the claim that isomorphism is neither necessary nor sufficient for representation and I shall argue that there exist accounts of representation in both art and science involving isomorphism which accommodate the apparent counterexamples and, (...) moreover, allow us to understand how “impossible” artistic objects and inconsistent scientific theories can be said to represent. (shrink)

Recent discussions of the nature of representation in science have tended to import pre-established decompositions from analyses of representation in the arts, language, cognition and so forth. Which of these analyses one favours will depend on how one conceives of theories in the first place. If one thinks of them in terms of an axiomatised set of logico-linguistic statements, then one might be naturally drawn to accounts of linguistic representation in which notions of denotation, for example, feature prominently. If, on (...) the other hand, one conceives of theories in non-linguistic terms, as in the model-theoretic approach, then one might look to analyses of representation in the arts where notions of resemblance tend to be brought to the fore. Thus van Fraassen, for example, has imported such an analysis into his discussion of representation in science and argued that an appropriate account of resemblance can be given in terms of the set-theoretic relation of isomorphism. This has been strongly criticised by Suarez, who argues that just as isomorphism cannot capture representation in art, so it is inappropriate in the scientific context as well. Similarly Hughes draws on Goodman`s rejection of resemblance in art in favour of denotation and, rather confusingly perhaps, favours the latter whilst also maintaining the model-theoretic view of theories. In this paper, I shall examine the debate in terms of four claims: 1. Isomorphism is not sufficient for representation; 2. Isomorphism is not necessary for representation; 3. Models represent but theories do not; 4. Models denote and do not resemble. Each of these claims will be questioned and I will conclude by suggesting that, through appropriate modifications, a form of isomorphism can serve to underpin representation in both the arts and science. (shrink)

Ever since David Hume, empiricists have barred powers and capacities from nature. In this book Cartwright argues that capacities are essential in our scientific world, and, contrary to empiricist orthodoxy, that they can meet sufficiently strict demands for testability. Econometrics is one discipline where probabilities are used to measure causal capacities, and the technology of modern physics provides several examples of testing capacities (such as lasers). Cartwright concludes by applying the lessons of the book about capacities and probabilities to the (...) explanation of the role of causality in quantum mechanics. (shrink)

This paper examines contemporary attempts to explicate the explanatory role of mathematics in the physical sciences. Most such approaches involve developing so-called mapping accounts of the relationships between the physical world and mathematical structures. The paper argues that the use of idealizations in physical theorizing poses serious difficulties for such mapping accounts. A new approach to the applicability of mathematics is proposed.

This paper concerns what Jerry Fodor calls a 'metaphysical mystery': How can there by macroregularities that are realized by wildly heterogeneous lower level mechanisms? But the answer to this question is not as mysterious as many, including Jaegwon Kim, Ned Block, and Jerry Fodor might think. The multiple realizability of the properties of the special sciences such as psychology is best understood as a kind of universality, where 'universality' is used in the technical sense one finds in the physics literature. (...) It is argued that the same explanatory strategy used by physicists to provide understanding of universal behavior in physics can be used to explain how special science properties can be heterogeneously multiply realized. (shrink)

This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story (...) explains patterns across extremely diverse systems and shows how minimal models can be used to understand real systems. (shrink)

Scientists have used models for hundreds of years as a means of describing phenomena and as a basis for further analogy. In Scientific Models in Philosophy of Science, Daniela Bailer-Jones assembles an original and comprehensive philosophical analysis of how models have been used and interpreted in both historical and contemporary contexts. Bailer-Jones delineates the many forms models can take (ranging from equations to animals; from physical objects to theoretical constructs), and how they are put to use. She examines early mechanical (...) models employed by nineteenth-century physicists such as Kelvin and Maxwell, describes their roots in the mathematical principles of Newton and others, and compares them to contemporary mechanistic approaches. Bailer-Jones then views the use of analogy in the late nineteenth century as a means of understanding models and to link different branches of science. She reveals how analogies can also be models themselves, or can help to create them. The first half of the twentieth century saw little mention of models in the literature of logical empiricism. Focusing primarily on theory, logical empiricists believed that models were of temporary importance, flawed, and awaiting correction. The later contesting of logical empiricism, particularly the hypothetico-deductive account of theories, by philosophers such as Mary Hesse, sparked a renewed interest in the importance of models during the 1950s that continues to this day. Bailer-Jones analyzes subsequent propositions of: models as metaphors; Kuhn's concept of a paradigm; the Semantic View of theories; and the case study approaches of Cartwright and Morrison, among others. She then engages current debates on topics such as phenomena versus data, the distinctions between models and theories, the concepts of representation and realism, and the discerning of falsities in models. (shrink)

one takes to be the most salient, any pair could be judged more similar to each other than to the third. Goodman uses this second problem to showthat there can be no context-free similarity metric, either in the trivial case or in a scientifically ...

This book offers a comprehensive overview of the structure, strategy and methods of assessment of orthodox theoretical economics. In Part I Professor Hausman explains how economists theorise, emphasising the essential underlying commitment of economists to a vision of economics as a separate science. In Part II he defends the view that the basic axioms of economics are 'inexact' since they deal only with the 'major' causes; unlike most writers on economic methodology, the author argues that it is the rules that (...) economists espouse rather than their practice that is at fault. Part III links the conception of economics as a separate science to the fact that economic theories offer reasons and justifications for human actions, not just their causes. With its lengthy appendix introducing relevant issues in philosophy of science, this book is a major addition to philosophy of economics and of social science. (shrink)

Approaches to explanation -- Causal and explanatory relevance -- The kairetic account of /D making -- The kairetic account of explanation -- Extending the kairetic account -- Event explanation and causal claims -- Regularity explanation -- Abstraction in regularity explanation -- Approaches to probabilistic explanation -- Kairetic explanation of frequencies -- Kairetic explanation of single outcomes -- Looking outward -- Looking inward.

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)

One of the most conspicuous features of contemporary modeling practices is the dissemination of mathematical and computational methods across disciplinary boundaries. We study this process through two applications of the Ising model: the Sherrington-Kirkpatrick model of spin glasses and the Hopfield model of associative memory. The Hopfield model successfully transferred some basic ideas and mathematical methods originally developed within the study of magnetic systems to the field of neuroscience. As an analytical resource we use Paul Humphreys's discussion of computational and (...) theoretical templates. We argue that model templates are crucial for the intra- and interdisciplinary theoretical transfer. A model template is an abstract conceptual idea associated with particular mathematical forms and computational methods. (shrink)