The Nature of Models

: This paper gives a historical overview of the ways various trends in the philosophy of science dealt with models and their relationship with the topics of heuristics and theoretical dynamics. First of all, N. Campbell’s account of analogies as components of scientific theories is presented. Next, the notion of ‘model’ in the reconstruction of the structure of scientific theories proposed by logical empiricists is examined. This overview finishes with M. Hesse’s attempts to develop Campbell’s early ideas in terms of (...) an analogical inference. The final part of the paper points to contemporary developments on these issues which adopt a cognitivist perspective. It is indicated how discussions in the cognitive sciences might help to flesh out some of the insights philosophers of science had concerning the role models and analogies play in actual scientific theorizing. Key words: models, analogical reasoning, metaphors in science, the structure of scientific theories, theoretical dynamics, heuristics, scientific discovery. (shrink)

Recent accounts of scientific method suggest that a model, or analogy, for an axiomatized theory is another theory, or postulate set, with an identical calculus. The present paper examines five central theses underlying this position. In the light of examples from physical science it seems necessary to distinguish between models and analogies and to recognize the need for important revisions in the position under study, especially in claims involving an emphasis on logical structure and similarity in form between theory and (...) analogy. While formal considerations are often relevant in the employment of an analogy they are neither as extensive as proponents of this viewpoint suggest, nor are they in most cases sufficient for allowing analogies to fulfill the roles imputed to them. Of major importance, and what these authors generally fail to consider, are physical similarities between analogue and theoretical object. Such similarities, which are characteristic in varying degrees of most analogies actually employed, play an important role in affording a better understanding of concepts in the theory and also in the development of the theoretical assumptions. (shrink)

What sort of claims do scientific models make and how do these claims then underwrite empirical successes such as explanations and reliable policy interventions? In this paper I propose answers to these questions for the class of models used throughout the social and biological sciences, namely idealized deductive ones with a causal interpretation. I argue that the two main existing accounts misrepresent how these models are actually used, and propose a new account. *Received July 2006; revised August 2008. †To contact (...) the author, please write to: Department of Philosophy, University of Missouri, St. Louis, 599 Lucas Hall (MC 73), One University Blvd., St. Louis, MO 63121-4400; e-mail: alexandrovaa@umsl.edu. (shrink)

Diese Abhandlung diskutiert und kritisiert einige Aspekte der syntaktischen Auffassung der wissenschaftlichen Theorien und tritt dafür ein, daß die einzig mögliche Alternative eine modell-theoretische Annäherung ist.

The biological sciences have become increasingly reliant on so-called 'model organisms'. I argue that in this domain, the concept of a descriptive model is essential for understanding scientific practice. Using a case study, I show how such a model was formulated in a preexplanatory context for subsequent use as a prototype from which explanations ultimately may be generated both within the immediate domain of the original model and in additional, related domains. To develop this concept of a descriptive model, I (...) focus on use of the nematode worm Caenorhabditis elegans and the wiring diagrams that were developed as models of its neural structure. In addition, implications of the concept of a descriptive model, particularly its relevance for the data-phenomena distinction as well as its relation to long-standing debates on realism, are briefly examined. (shrink)

This paper aims to identify the key characteristics of model organisms that make them a specific type of model within the contemporary life sciences: in particular, we argue that the term “model organism” does not apply to all organisms used for the purposes of experimental research. We explore the differences between experimental and model organisms in terms of their material and epistemic features, and argue that it is essential to distinguish between their representational scope and representational target. We also examine (...) the characteristics of the communities who use these two types of models, including their research goals, disciplinary affiliations, and preferred practices to show how these have contributed to the conceptualization of a model organism. We conclude that model organisms are a specific subgroup of organisms that have been standardized to fit an integrative and comparative mode of research, and that it must be clearly distinguished from the broader class of experimental organisms. In addition, we argue that model organisms are the key components of a unique and distinctively biological way of doing research using models.Keywords: Experimental organism; Genetics; Model organism; Modeling; Philosophy of biology; Representation. (shrink)

It seems that the reception of Conrad Hal Waddington’s work never really gathered speed in mainstream biology. This paper, offering a transdisciplinary survey of approaches using his epigenetic landscape images, argues that (i) Waddington’s legacy is much broader than is usually recognized—it is widespread across the life sciences (e.g. stem cell biology, developmental psychology and cultural anthropology). In addition, I will show that (ii) there exist as yet unrecognized heuristic roles, especially in model building and theory formation, which Waddington’s images (...) play within his work. These different methodological facets envisioned by Waddington are used as a natural framework to analyze and classify the manners of usage of epigenetic landscape images in post-Waddingtonian ‘landscape approaches’. This evaluation of Waddington’s pictorial legacy reveals that there are highly diverse lines of traditions in the life sciences, which are deeply rooted in Waddington’s methodological work. (shrink)

A survey of models in immunology is conducted and distinct kinds of models are characterized based on whether models are material or conceptual, the distinctiveness of their epistemic purpose, and the criteria for evaluating the goodness of a model relative to its intended purpose. I argue that the diversity of models in interdisciplinary fields such as immunology reflects the fact that information about the phenomena of interest is gathered from different sources using multiple methods of investigation. To each model is (...) attached a description specifying how information about a phenomenon of interest has been acquired, highlighting points of commonality and difference between the methodological and epistemic histories of the information encapsulated in different models. These points of commonality and difference allow investigators to integrate findings from different models into more comprehensive explanatory accounts, as well as to troubleshoot anomalies and faulty accounts by going back to the original building blocks. (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, 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)

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, 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)

: 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.

Cellular Automata (CA) based simulations are widely used in a great variety of domains, fromstatistical physics to social science. They allow for spectacular displays and numerical predictions. Are they forall that a revolutionary modeling tool, allowing for “direct simulation”, or for the simulation of “the phenomenon itself”? Or are they merely models "of a phenomenological nature rather than of a fundamental one”? How do they compareto other modeling techniques? In order to answer these questions, we present a systematic exploration of (...) CA’s various uses. (shrink)

What does it take for a scientific model to represent? Scientific models have received a great deal of attention in recent philosophical literature. Following Morgan and Morrison’s account of “Models as Mediators”, analysis of how models represent has changed from questioning what properties of models can be said to correlate with the world to asking how models are used to relate to an intended target-system. This turn to a practice-oriented approach of understanding models was a response to a general philosophical (...) problem that attends the empirical application of scientific representations such as models. This problem is twofold. Models, on the one hand, are often said to make false claims about the world.. (shrink)

This paper revisits the concept of fiction employed in recent debates about the reality of theoretical entities in the philosophy of science. From an anti-realist perspective the dependence of evidence for some scientific entities on mediated forms of observation and modelling strategies reflects a degree of construction that is argued to closely resemble fiction. As a realist’s response to this debate, this paper provides an analysis of fictional entities in comparison to real ones. I argue that the distinction between fictional (...) and real entities is reflected in their different relations toward their representations. This is particularly evident when it comes to the investigation of properties not explicitly given in a representation but that rely on knowledge external to it. A comparison of the resulting difference in the interpretation of fictional and real entities is then shown to provide guidelines for the assessment of when a realist claim can be made for model-based inferences to theoretical entities in science. At the end of this paper I advocate a pluralistic view on scientific realism by showing that representational pluralism, far from posing a problem for a realist interpretation of scientific practice, serves as an indicator for the reality of scientific entities. (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 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)

A traditional view of mathematical modeling holds, roughly, that the more details of the phenomenon being modeled that are represented in the model, the better the model is. This paper argues that often times this ‘details is better’ approach is misguided. One ought, in certain circumstances, to search for an exactly solvable minimal model—one which is, essentially, a caricature of the physics of the phenomenon in question.

I approach the study of echo chambers from the perspective of veritistic social epistemology. A trichotomous belief model is developed featuring a mechanism by which agents will have a tendency to form agreement in the community. The model is implemented as an agent-based model in NetLogo and then used to investigate a social practice called Impartiality, which is a plausible means for resisting or dismantling echo chambers. The implementation exposes additional factors that need close consideration in an evaluation of Impartiality. (...) In particular, resisting or dismantling echo chambers requires the selection of sufficiently low levels of doxastic entrenchment, but this comes with other tradeoffs. (shrink)

Webb distinguishes two endeavors she calls animal modeling and animat modeling and advocates for the former. I share her preference and point to additional virtues of modeling actual biological mechanisms (animal modeling). As Webb argues, animat modeling should be regarded as modeling of specific, but madeup, biological mechanisms. I contend that modeling made-up mechanisms in situations in which we have some knowledge of the actual mechanisms involved is modeling with one hand—the good one—tied behind one’s back.1 The hand that is (...) used in animat modeling is constructing and evaluating models by whether they behave in the right way—do they exhibit the particular phenomenon one is trying to understand? The good hand that is disavowed seeks to use evidence about the mechanism employed in real living systems both for inspiration in designing the model and for evaluating the model. Denying oneself use of one’s good hand both limits one’s access to valuable evidence for evaluating a model and denies oneself access to a potent discovery strategy. Webb draws attention to one reason to employ the good hand—if models are to be relevant to biology (and not just characterize hypothetical mechanisms), then the component parts and operations specified in the model must in some way map onto those in actual biological organisms. Especially if one accepts the possibility of multiple realizations, then if one only uses behavior to evaluate the model one may well have described an alternative realization than that found in real organisms. To determine that one has modeled the actual realization, it is necessary to compare the proposed mechanism with the actual mechanism—does it.. (shrink)

Scientific inquiry makes use of visual models to represent empirical systems. Many philosophers claim that models function only as analogies, and that their role is limited to a didactic or heuristic role. I analyze four families of visual models: maps, graphical images from the study of turbulence, fractal images, and strange attractors. I show that these models are projected from data; that they are dynamic pictures, which can be manipulated by the researcher; that they are necessary for the theories in (...) which they function; and finally that they interact at many levels with the theory. I call models possessing these qualities generative, because they have contributed to the elaboration and formulation of theories. Strange attractors, visual models produced in the study of dynamical systems, are the best example of generative models. ;I show the limitation of conceiving of models as analogies, and restrict the scope of concepts such as similarity, resemblance, correspondence, and analogy. The study of generative visual models shows that visualization takes us beyond the perceptively visible, but that the representational function of the model is retained. Visual models represent the world, even if they do not resemble or look like the world. The objects of scientific inquiry are represented through our constructed worldview. This position sides with a constructivist account of reality. (shrink)

Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would allow one to (...) use models for possibilistic prediction and salvage widespread scientific practice. (shrink)

Although predictive power and explanatory insight are both desiderata of scientific models, these features are often in tension with each other and cannot be simultaneously maximized. In such situations, scientists may adopt what I term a ‘division of cognitive labor’ among models, using different models for the purposes of explanation and prediction, respectively, even for the exact same phenomenon being investigated. Adopting this strategy raises a number of issues, however, which have received inadequate philosophical attention. More specifically, while one implication (...) may be that it is inappropriate to judge explanatory models by the same standards of quantitative accuracy as predictive models, there still needs to be some way of either confirming or rejecting these model explanations. Here I argue that robustness analyses have a central role to play in testing highly idealized explanatory models. I illustrate these points with two examples of explanatory models from the field of geomorphology. (shrink)

Scientific models invariably involve some degree of idealization, abstraction, or nationalization of their target system. Nonetheless, I argue that there are circumstances under which such false models can offer genuine scientific explanations. After reviewing three different proposals in the literature for how models can explain, I shall introduce a more general account of what I call model explanations, which specify the conditions under which models can be counted as explanatory. I shall illustrate this new framework by applying it to the (...) case of Bohr's model of the atom, and conclude by drawing some distinctions between phenomenological models, explanatory models, and fictional models. (shrink)

Concern about the role and the limits of modeling has heightened after repeated questions were raised regarding the dependability and suitability of the models that were used in the run-up to the 2008 financial crash. In this book, Lawrence Boland provides an overview of the practices of and the problems faced by model builders to explain the nature of models, the modeling process, and the possibility for and nature of their testing. In a reflective manner, the author raises serious questions (...) about the assumptions and judgments that model builders make in constructing models. In making his case, he examines the traditional microeconomics-macroeconomics separation with regard to how theoretical models are built and used and how they interact, paying particular attention to the use of equilibrium concepts in macroeconomic models and game theory and to the challenges involved in building empirical models, testing models, and using models to test theoretical explanations. (shrink)

In this paper, I characterize visual epistemic representations as concrete two- or three-dimensional tools for conveying information about aspects of their target systems or phenomena of interest. I outline two features of successful visual epistemic representation: that the vehicle of representation contain sufficiently accurate information about the phenomenon of interest for the user’s purpose, and that it convey this information to the user in a manner that makes it readily available to her. I argue that actual epistemic representation may involve (...) tradeoffs between these and is successful to the extent that they are present. (shrink)

In this paper, I take scientific models to be epistemic representations of their target systems. I define an epistemic representation to be a tool for gaining information about its target system and argue that a vehicle’s capacity to provide specific information about its target system—its informativeness—is an essential feature of this kind of representation. I draw an analogy to our ordinary notion of interpretation to show that a user’s aim of faithfully representing the target system is necessary for securing this (...) feature. (shrink)

In this paper the topic of the relations between scientific theories and scientific models is tackled by considering the former as hypothetical scientific representations and the latter as fictive scientific representations. A classification of the models is also proposed.

In this paper a unified theory of models and thought experiments is proposed by considering them as fictions, la Vaihinger. In order to reach this aim, the Hertzian and Botzmannian interpretation of theories as Bilder is reconsidered.

Nancy Cartwright is one of the most distinguished and influential contemporary philosophers of science. Despite the profound impact of her work, there is neither a systematic exposition of Cartwright’s philosophy of science nor a collection of articles that contains in-depth discussions of the major themes of her philosophy. This book is devoted to a critical assessment of Cartwright’s philosophy of science and contains contributions from Cartwright's champions and critics. Broken into three parts, the book begins by addressing Cartwright's views on (...) the practice of model building in science and the question of how models represent the world before moving on to a detailed discussion of methodologically and metaphysically challenging problems. Finally, the book addresses Cartwright's original attempts to clarify profound questions concerning the metaphysics of science. With contributions from leading scholars, such as Ronald N. Giere and Paul Teller, this unique volume will be extremely useful to philosophers of science the world over. (shrink)

I discuss here the definition of computer simulations, and more specifically the views of Humphreys, who considers that an object is simulated when a computer provides a solution to a computational model, which in turn represents the object of interest. I argue that Humphreys's concepts are not able to analyse fully successfully a case of contemporary simulation in physics, which is more complex than the examples considered so far in the philosophical literature. I therefore modify Humphreys's definition of simulation. I (...) allow for several successive layers of computational models, and I discuss the relations that exist between these models, the computer, and the object under study. An aim of my proposal is to clarify the distinction between computational models and numerical methods, and to better understand the representational and the computational functions of models in simulations. (shrink)

This paper explores varieties of scientific structuralism. Central to our investigation is the notion of `shared structure'. We begin with a description of mathematical structuralism and use this to point out analogies and disanalogies with scientific structuralism. Our particular focus is the semantic structuralist's attempt to use the notion of shared structure to account for the theory-world connection, this use being crucially important to both the contemporary structural empiricist and realist. We show why minimal scientific structuralism is, at the very (...) least, a powerful methodological standpoint. Our investigation also makes explicit what more must be added to this minimal structuralist position in order to address the theory-world connection, namely, an account of representation. (shrink)

The common conception of justice as reciprocity seemingly is inapplicable to relations between non-overlapping generations. This is a challenge also to John Rawls’s theory of justice as fairness. This text responds to this by way of reinterpreting and developing Rawls’s theory. First, by examining the original position as a model, some revisions of it are shown to be wanting. Second, by drawing on the methodology of constructivism, an alternative solution is proposed: an amendment to the primary goods named ‘sustainability of (...) values’. This revised original position lends support to intergenerational justice as fairness. (shrink)