Models are of central importance in many scientific contexts. The centrality of models such as inflationary models in cosmology, general-circulation models of the global climate, the double-helix model of DNA, evolutionary models in biology, agent-based models in the social sciences, and general-equilibrium models of markets in their respective domains is a case in point (the Other Internet Resources section at the end of this entry contains links to online resources that discuss these models). Scientists spend significant amounts of time building, (...) testing, comparing, and revising models, and much journal space is dedicated to interpreting and discussing the implications of models. In short, models are one of the principal instruments of modern science. (shrink)

Scientific realism is the optimistic view that modern science is on the right track: that the world really is the way our best scientific theories describe it. In his book, Stathis Psillos gives us a detailed and comprehensive study which restores the intuitive plausibility of scientific realism. We see that throughout the twentieth century, scientific realism has been challenged by philosophical positions from all angles: from reductive empiricism, to instrumentalism and to modern sceptical empiricism. _Scientific Realism_ explains that the history (...) of science does not undermine the arguments for scientific realism, but instead makes it reasonable to accept scientific realism as the best philosophical account of science, its empirical success, its progress and its practice. Anyone wishing to gain a deeper understanding of the state of modern science and why scientific realism is plausible, should read this book. (shrink)

Author Max Black argues that language should conform to the discovered regularities of experience it is radically mistaken to assume that the conception of language is a mirror of reality.

Author Max Black argues that language should conform to the discovered regularities of experience it is radically mistaken to assume that the conception of language is a mirror of reality.

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)

Bas C. van Fraassen presents an original exploration of how we represent the world.

This essay endorses the conclusion of Sklar’s “Dappled Theories in a Uniform World” that he announces in his abstract, that notwithstanding recent attacks foundational theories are universal in their scope. But Sklar’s rejection of a “pluralist ontology” is questioned. It is concluded that so called “foundational” and “phenomenological” theories are on a much more equal footing as sources of knowledge than Sklar would allow, that “giving an ontology” generally involves dealing in idealizations, and that a transfigured “ficitonalism” provides an (in (...) many respects) better model of scientific knowledge than the model of “foundational truths.”. (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)

Given that scientific realism is based on the assumption that there is a connection between a model's predictive success and its truth, and given the success of multiple incompatible models in scientific practice, the realist has a problem. When the different models can be shown to arise as different approximations to a unified theory, however, one might think the realist to be able to accommodate such cases. I discuss a special class of models and argue that a realist interpretation has (...) to understand these models of a system as ‘ perspectival ’, in close analogy to different spatial perspectives onto the same object. For this sort of case, I also respond to Morrison's recent claim that in the process of unifying models into an overarching theory, explanatory and descriptive power are lost, leaving the unified theory with less of a claim to a realist interpretation than the models themselves. Introduction Perspectival models from singular perturbation problems Unification of perspectives without losses of explanatory power Perspectives as different levels of a system Perspectival models, idealizations and pluralism. (shrink)

This book comes to the rescue of scientific realism, showing that reports of its death have been greatly exaggerated. Philosophical realism holds that the aim of a particular discourse is to make true statements about its subject matter. Ilkka Niiniluoto surveys different kinds of realism in various areas of philosophy and then sets out his own critical realist philosophy of science.

Although the recent emphasis on models in philosophy of science has been an important development, the consequence has been a shift away from more traditional notions of theory. Because the semantic view defines theories as families of models and because much of the literature on “scientific” modeling has emphasized various degrees of independence from theory, little attention has been paid to the role that theory has in articulating scientific knowledge. This paper is the beginning of what I hope will be (...) a redress of the imbalance. I begin with a discussion of some of the difficulties faced by various formulations of the semantic view not only with respect to their account of models but also with their definition of a theory. From there I go on to articulate reasons why a notion of theory is necessary for capturing the structure of scientific knowledge and how one might go about formulating such a notion in terms of different levels of representation and explanation. The context for my discussion is the BCS account of superconductivity, a `theory' that was, and still is, sometimes referred to as a `model'. BCS provides a nice focus for the discussion because it illuminates various features of the theory/model relationship that seem to require a robust notion of theory that is not easily captured by the semantic account. (shrink)

This essay contains a partial exploration of some key concepts associated with the epistemology of realist philosophies of science. It shows that neither reference nor approximate truth will do the explanatory jobs that realists expect of them. Equally, several widely-held realist theses about the nature of inter-theoretic relations and scientific progress are scrutinized and found wanting. Finally, it is argued that the history of science, far from confirming scientific realism, decisively confutes several extant versions of avowedly 'naturalistic' forms of scientific (...) realism. (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)

Debate over the nature of science has recently moved from the halls of academia into the public sphere, where it has taken shape as the "science wars." At issue is the question of whether scientific knowledge is objective and universal or socially mediated, whether scientific truths are independent of human values and beliefs. Ronald Giere is a philosopher of science who has been at the forefront of this debate from its inception, and Science without Laws offers a much-needed mediating perspective (...) on an increasingly volatile line of inquiry. Giere does not question the major findings of modern science: for example, that the universe is expanding or that inheritance is carried by DNA molecules with a double helical structure. But like many critics of modern science, he rejects the widespread notion of science--deriving ultimately from the Enlightenment--as a uniquely rational activity leading to the discovery of universal truths underlying all natural phenomena. In these highly readable essays, Giere argues that it is better to understand scientists as merely constructing more or less abstract models of limited aspects of the world. Such an understanding makes possible a resolution of the issues at stake in the science wars. The critics of science are seen to be correct in rejecting the Enlightenment idea of science, and its defenders are seen to be correct in insisting that science does produce genuine knowledge of the natural world. Giere is utterly persuasive in arguing that to criticize the Enlightenment ideal is not to criticize science itself, and that to defend science one need not defend the Enlightenment ideal. Science without Laws thus stakes out a middle ground in these debates by showing us how science can be better conceived in other ways. (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)

In this sequence of philosophical essays about natural science, the author argues that fundamental explanatory laws, the deepest and most admired successes of modern physics, do not in fact describe regularities that exist in nature. Cartwright draws from many real-life examples to propound a novel distinction: that theoretical entities, and the complex and localized laws that describe them, can be interpreted realistically, but the simple unifying laws of basic theory cannot.

At the center of quantum chaos research is a particular family of models known as quantum maps. These maps illustrate an important “horizontal” dimension to model construction that has been overlooked in the literature on models. Three ways in which quantum maps are being used to clarify the relationship between classical and quantum mechanics are examined. This study suggests that horizontal models may provide a new and fruitful framework for exploring intertheoretic relations.

This paper examines the role of mathematical idealization in describing and explaining various features of the world. It examines two cases: first, briefly, the modeling of shock formation using the idealization of the continuum. Second, and in more detail, the breaking of droplets from the points of view of both analytic fluid mechanics and molecular dynamical simulations at the nano-level. It argues that the continuum idealizations are explanatorily ineliminable and that a full understanding of certain physical phenomena cannot be obtained (...) through completely detailed, non-idealized representations. (shrink)

I. The function of models in the empirical sciencesII. Structure and purpose: conditions of a structural nature which models should satisfy in order to accomplish their function.III. Generalisation and specialisation of the classical definition of model, in view of the above requirements:the algebraic model conceptthe semantic model conceptthe syntactical model conceptIV. Attempt towards reunification: the concept of model on a pragmatic basis.

Model building in the social sciences can increasingly rely on well elaborated formal theories. At the same time inexpensive large computational capacities are now available. Both make computer-based model building and simulation possible in social science, whose central aim is in particular an understanding of social dynamics. Such social dynamics refer to public opinion formation, partner choice, strategy decisions in social dilemma situations and much more. In the context of such modelling approaches, novel problems in philosophy of science arise which (...) must be analysed - the main aim of this book. Interest in social simulation has recently been growing rapidly world- wide, mainly as a result of the increasing availability of powerful personal computers. The field has also been greatly influenced by developments in cellular automata theory (from mathematics) and in distributed artificial intelligence which provided tools readily applicable to social simulation. This book presents a number of modelling and simulation approaches and their relations to problems in philosophy of science. It addresses sociologists and other social scientists interested in formal modelling, mathematical sociology, and computer simulation as well as computer scientists interested in social science applications, and philosophers of social science. (shrink)

Da Costa and French explore the consequences of adopting a 'pragmatic' notion of truth in the philosophy of science. Their framework sheds new light on issues to do with belief, theory acceptance, and the realism-antirealism debate, as well as the nature of scientific models and their heuristic development.

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)

A leading scholar in the psychology of thinking and reasoning argues that the counterfactual imagination—the creation of "if only" alternatives to ...

Science is popularly understood as being an ideal of impartial algorithmic objectivity that provides us with a realistic description of the world down to the last detail. The essays collected in this book—written by some of the leading experts in the field—challenge this popular image right at its heart, taking as their starting point that science trades not only in truth, but in fiction, too. With case studies that range from physics to economics and to biology, _Fictions in Science_ reveals (...) that fictions are as ubiquitous in scientific narratives and practice as they are in any other human endeavor, including literature and art. Of course scientific activity, most prominently in the formal sciences, employs logically precise algorithmic thinking. However, the key to the predictive and technological success of the empirical sciences might well lie elsewhere—perhaps even in scientists’ extraordinary creative imagination instead. As these essays demonstrate, within the bounds of what is empirically possible, a scientist’s capacity for invention and creative thinking matches that of any writer or artist. (shrink)

Scientific Realism is the optimistic view that modern science is on the right track: that the world really is the way our best scientific theories describe it to be. In his book, Stathis Psillos gives us a detailed and comprehensive study, which restores the intuitive plausibility of scientific realism. We see that throughout the twentieth century, scientific realism has been challenged by philosophical positions from all angles: from reductive empiricism, to instrumentalism and modern skeptical empiricism. Scientific Realism explains that the (...) history of science does not undermine the notion of scientific realism, and instead makes it reasonable to accept scientific as the best philosophical account of science, its empirical success, its progress and its practice. Anyone wishing to gain a deeper understanding of the state of modern science and why scientific realism is plausible, should read this book. (shrink)

It is often supposed that the spectacular successes of our modern mathematical sciences support a lofty vision of a world completely ordered by one single elegant theory. In this book Nancy Cartwright argues to the contrary. When we draw our image of the world from the way modern science works - as empiricism teaches us we should - we end up with a world where some features are precisely ordered, others are given to rough regularity and still others behave in (...) their own diverse ways. This patchwork makes sense when we realise that laws are very special productions of nature, requiring very special arrangements for their generation. Combining classic and newly written essays on physics and economics, The Dappled World carries important philosophical consequences and offers serious lessons for both the natural and the social sciences. (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)

Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...) These are some of the questions addressed in this article. I maintain that the most significant feature of a simulation is that it allows scientists to imitate one process by another process. "Process" here refers solely to a temporal sequence of states of a system. Given the observation that processes are dealt with by all sorts of scientists, it is apparent that simulations prove to be a powerful interdisciplinarily acknowledged tool. Accordingly, simulations are best suited to investigate the various research strategies in different sciences more carefully. To this end, I focus on the function of simulations in the research process. Finally, a somewhat detailed case-study from nuclear physics is presented which, in my view, illustrates elements of a typical simulation in physics. (shrink)

How does a predecessor theory relate to its successor? According to Heinz Post’s General Correspondence Principle, the successor theory has to account for the em- pirical success of its predecessor. After a critical discussion of this principle, I outline and discuss various kinds of correspondence relations that hold between successive scientific theories. I then look in some detail at a case study from contemporary physics: the various proposals for a theory of high-temperature superconductivity. The aim of this case study is (...) to understand better the prospects and the place of a methodological principle such as the Generalized Correspondence Principle. Generalizing from the case study, I will then argue that some such principle has to be considered, at best, as one tool that might guide scientists in their theoriz- ing. Finally I present a tentative account of why principles such as the Generalized Correspondence Principle work so often and why there is so much continuity in scientific theorizing. (shrink)

Theoretical models are an important tool for many aspects of scientific activity. They are used, i.a., to structure data, to apply theories or even to construct new theories. But what exactly is a model? It turns out that there is no proper definition of the term "model" that covers all these aspects. Thus, I restrict myself here to evaluate the function of models in the research process while using "model" in the loose way physicists do. To this end, I distinguish (...) four kinds of models. These are (1) models as special theories, (2) models as a substitute for a theory, (3) toy models and (4) developmental models. I argue that models of the types (3) and (4) are considerably useful in the process of theory construction. This will be demonstrated in an extended case-study from High-Energy Physics. (shrink)

Fundamental theories are hard to come by. But even if we had them, they would be too complicated to apply. Quantum chromodynamics is a case in point. This theory is supposed to govern all strong interactions, but it is extremely hard to apply and test at energies where protons, neutrons and ions are the effective degrees of freedom. Instead, scientists typically use highly idealized models such as the MIT Bag Model or the Nambu Jona-Lasinio Model to account for phenomena in (...) this domain, to explain them and to gain nderstanding. Based on these models, which typically isolate a single feature of QCD and disregard many others, scientists attempt to get a better understanding of the physics of strong interactions. But does this practice make sense? Is it justified to use these models for the purposes at hand? Interestingly, these models do not even provide an accurate description of the mass spectrum of protons, neutrons and pions and their lowest lying excitations well - despite several adjustable parameters. And yet, the models are heavily used. I'll argue that a qualitative story, which establishes an explanatory link between the fundamental theory and a model, plays an important role in model acceptance in these cases. (shrink)