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  1. Peter Achinstein (1972). Models and Analogies: A Reply to Girill. Philosophy of Science 39 (2):235-240.
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  2. Peter Achinstein (1965). Theoretical Models. British Journal for the Philosophy of Science 16 (62):102-120.
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  3. Peter Achinstein (1964). Models, Analogies, and Theories. Philosophy of Science 31 (4):328-350.
    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 (...)
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  4. Anna Alexandrova (2008). Making Models Count. Philosophy of Science 75 (3):383-404.
    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 (...)
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  5. Eugen Altschul & Erwin Biser (1948). The Validity of Unique Mathematical Models in Science. Philosophy of Science 15 (1):11-24.
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  6. D. A. Anapolitanos (1989). Theories and Their Models. Journal for General Philosophy of Science 20 (2):201-211.
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  7. Rachel A. Ankeny (2000). Fashioning Descriptive Models in Biology: Of Worms and Wiring Diagrams. Philosophy of Science 67 (3):272.
    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 (...)
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  8. Daniela M. Bailer-Jones (2002). Scientists' Thoughts on Scientific Models. Perspectives on Science 10 (3):275-301.
    : 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.
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  9. Anouk Barberousse, Sara Franceschelli & Cyrille Imbert, Cellular Automata, Modeling, and Computation.
    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 (...)
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  10. Robert Batterman (2010). On the Explanatory Role of Mathematics in Empirical Science. British Journal for the Philosophy of Science 61 (1):1-25.
    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.
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  11. Robert W. Batterman (2009). Idealization and Modeling. Synthese 169 (3):427 - 446.
    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 (...)
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  12. Robert W. Batterman (2002). Asymptotics and the Role of Minimal Models. British Journal for the Philosophy of Science 53 (1):21-38.
    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.
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  13. William Bechtel, Some Virtues of Modeling with Both Hands.
    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 (...)
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  14. David Berlinski (1975). Mathematical Models of the World. Synthese 31 (2):211 - 227.
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  15. Alisa Bokulich (forthcoming). How Scientific Models Can Explain. Synthese:1--13.
    Scientific models invariably involve some degree of idealization, abstraction, or fictionalization 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 (...)
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  16. Agnes Bolinska (2013). Epistemic Representation, Informativeness and the Aim of Faithful Representation. Synthese 190 (2):219-234.
    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 (...)
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  17. Giovanni Boniolo, Theories and Models: Really Old Hat?
    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.
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  18. Giovanni Boniolo (1997). On a Unified Theory of Models and Thought Experiments in Natural Sciences. International Studies in the Philosophy of Science 11 (2):121 – 142.
    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.
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  19. Katherine Brading & Elaine Landry (2006). Scientific Structuralism: Presentation and Representation. Philosophy of Science 73 (5):571-581.
    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 (...)
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  20. Matthew J. Brown (2009). Models and Perspectives on Stage: Remarks on Giere's Scientific Perspectivism. Studies in History and Philosophy of Science Part A 40 (2):213-220.
    Ron Giere's recent book Scientific Perspectivism sets out an account of science that attempts to forge a via media between two popular extremes: absolutist, objectivist realism on the one hand, and social constructivism or skeptical anti-realism on the other. The key for Giere is to treat both scientific observation and scientific theories as perspectives, which are limited, partial, contingent, context-, agent- and purpose-dependent, and pluralism-friendly, while nonetheless world-oriented and modestly realist. Giere's perspectivism bears significant similarly to early writings by Paul (...)
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  21. Krzysztof Brzechczyn (2008). Polish Discussions on the Nature of Communism and Mechanisms of its Collapse: A Review Article. East European Politics and Societies 22 (4):828-855.
    The author, against the background of Communist Studies developed in Poland since World War I, reconstructs theoretical orientations that explained the communist system in that country. In this paper, the division of theoretical approaches into political, economic, and cultural ones is proposed. Each of them seeks factors responsible for nature, evolution, and final decline of the communist system in a different sphere of social life. An approach of the political type was Leszek Nowak’s theory of communism as a system of (...)
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  22. A. V. Bushkovitch (1974). Models, Theories, and Kant. Philosophy of Science 41 (1):86-88.
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  23. Jeremy Butterfield, Between Laws and Models: Some Philosophical Morals of Lagrangian Mechanics.
    I extract some philosophical morals from some aspects of Lagrangian mechanics. (A companion paper will present similar morals from Hamiltonian mechanics and Hamilton-Jacobi theory.) One main moral concerns methodology: Lagrangian mechanics provides a level of description of phenomena which has been largely ignored by philosophers, since it falls between their accustomed levels---``laws of nature'' and ``models''. Another main moral concerns ontology: the ontology of Lagrangian mechanics is both more subtle and more problematic than philosophers often realize. The treatment of Lagrangian (...)
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  24. H. G. Callaway (forthcoming). Abduction, Competing Models and the Virtues of Hypotheses. In Lorenzo Magnani (ed.), (2013) Model-Based Reasoning in Science and Technology. Springer.
    This paper focuses on abduction as explicit or readily formulatable inference to possible explanatory hypotheses--as contrasted with inference to conceptual innovations or abductive logic as a cycle of hypotheses, deduction of consequences and inductive testing. Inference to an explanation is often a matter of projection or extrapolation of elements of accepted theory for the solution of outstanding problems in particular domains of inquiry. I say "projections or extrapolation" of accepted theory, but I mean to point to something broader and suggest (...)
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  25. Craig Callender & Jonathan Cohen (2006). There is No Special Problem About Scientific Representation. Theoria 21 (1):67-85.
    We propose that scientific representation is a special case of a more general notion of representation, and that the relatively well worked-out and plausible theories of the latter are directly applicable to thc scientific special case. Construing scientific representation in this way makes the so-called “problem of scientific representation” look much less interesting than it has seerned to many, and suggests that some of the (hotly contested) debates in the literature are concerned with non-issues.
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  26. Jack C. Carloye (1971). An Interpretation of Scientific Models Involving Analogies. Philosophy of Science 38 (4):562-569.
    In order to account for the actual function of analogue models in extending theories to new domains, we argue that it is necessary to analyze the inference involved into a complex two dimensional form. This form must go horizontally from descriptions of entities used as a model to redescriptions of entities in the new domain, and it must go vertically from an observation language to a theoretical language having a different and exclusive logical syntax. This complex inference can only be (...)
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  27. Nancy Cartwright (2009). If No Capacities Then No Credible Worlds. But Can Models Reveal Capacities? Erkenntnis 70 (1):45 - 58.
    This paper argues that even when simple analogue models picture parallel worlds, they generally still serve as isolating tools. But there are serious obstacles that often stop them isolating in just the right way. These are obstacles that face any model that functions as a thought-experiment but they are especially pressing for economic models because of the paucity of economic principles. Because of the paucity of basic principles, economic models are rich in structural assumptions. Without these no interesting conclusions can (...)
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  28. Nancy Cartwright (1997). Models: The Blueprints for Laws. Philosophy of Science 64 (4):303.
    In this paper the claim that laws of nature are to be understood as claims about what necessarily or reliably happens is disputed. Laws can characterize what happens in a reliable way, but they do not do this easily. We do not have laws for everything occurring in the world, but only for those situations where what happens in nature is represented by a model: models are blueprints for nomological machines, which in turn give rise to laws. An example from (...)
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  29. Jordi Cat (2005). Modeling Cracks and Cracking Models: Structures, Mechanisms, Boundary Conditions, Constraints, Inconsistencies and the Proper Domains of Natural Laws. Synthese 146 (3):447 - 487.
    The emphasis on models hasn’t completely eliminated laws from scientific discourse and philosophical discussion. Instead, I want to argue that much of physics lies beyond the strict domain of laws. I shall argue that in important cases the physics, or physical understanding, does not lie either in laws or in their properties, such as universality, consistency and symmetry. I shall argue that the domain of application commonly attributed to laws is too narrow. That is, laws can still play an important, (...)
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  30. A. Charles Catania (2000). Metaphors, Models, and Mathematics in the Science of Behavior. Behavioral and Brain Sciences 23 (1):94-95.
    Metaphors and models involve correspondences between events in separate domains. They differ in the form and precision of how the correspondences are expressed. Examples include correspondences between phylogenic and ontogenic selection, and wave and particle metaphors of the mathematics of quantum physics. An implication is that the target article's metaphors of resistance to change may have heuristic advantages over those of momentum.
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  31. C. Cazeaux (2012). Beyond Mimesis and Convention: Representation in Art and Science. British Journal of Aesthetics 52 (2):211-216.
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  32. Anjan Chakravartty (2001). The Semantic or Model-Theoretic View of Theories and Scientific Realism. Synthese 127 (3):325 - 345.
    The semantic view of theoriesis one according to which theoriesare construed as models of their linguisticformulations. The implications of thisview for scientific realism have been little discussed. Contraryto the suggestion of various champions of the semantic view,it is argued that this approach does not makesupport for a plausible scientific realism anyless problematic than it might otherwise be.Though a degree of independence of theory fromlanguage may ensure safety frompitfalls associated with logical empiricism, realism cannot be entertained unless models or (abstractedand/or idealized) (...)
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  33. Gabriele Contessa, Scientific Representation, Smilarity and Prediction.
    In this paper, I consider how different versions of the similarity account of scientific representation might apply to a simple case of scientific representation, in which a model is used to predict the behaviour of a system. I will argue that the similarity account is potentially susceptible to the problem of accidental similarities between the model and the system and that, if it is to avoid this problem, one has to specify which similarities have to hold between a model and (...)
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  34. Gabriele Contessa, Disentangling Scientific Representation.
    The main aim of this paper is to disentangle three senses in which we can say that a model represents a system—denotation epistemic representation, and successful epistemic representation--and to individuate what questions arise from each sense of the notion of representation as used in this context. Also, I argue that a model is an epistemic representation of a system only if a user adopts a general interpretation of the model in terms of a system. In the process, I hope to (...)
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  35. Gabriele Contessa (2011). Scientific Models and Representation. In Steven French & Juha Saatsi (eds.), The Continuum Companion to the Philosophy of Science. Continuum Press.
    My two daughters would love to go tobogganing down the hill by themselves, but they are just toddlers and I am an apprehensive parent, so, before letting them do so, I want to ensure that the toboggan won’t go too fast. But how fast will it go? One way to try to answer this question would be to tackle the problem head on. Since my daughters and their toboggan are initially at rest, according to classical mechanics, their final velocity will (...)
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  36. Gabriele Contessa (2010). Introduction. Synthese 172 (2).
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  37. Gabriele Contessa (2010). Scientific Models and Fictional Objects. Synthese 172 (2).
    In this paper, I distinguish scientific models in three kinds on the basis of their ontological status—material models, mathematical models and fictional models, and develop and defend an account of fictional models as fictional objects—i.e. abstract objects that stand for possible concrete objects.
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  38. Gabriele Contessa (2007). Scientific Representation, Interpretation, and Surrogative Reasoning. Philosophy of Science 74 (1):48-68.
    In this paper, I develop Mauricio Suárez’s distinction between denotation, epistemic representation, and faithful epistemic representation. I then outline an interpretational account of epistemic representation, according to which a vehicle represents a target for a certain user if and only if the user adopts an interpretation of the vehicle in terms of the target, which would allow them to perform valid (but not necessarily sound) surrogative inferences from the model to the system. The main difference between the interpretational conception I (...)
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  39. Gabriele Contessa (2007). Representing Reality: The Ontology of Scientific Models and Their Representational Function. Dissertation, University of London
    Today most philosophers of science believe that models play a central role in science and that one of the main functions of scientific models is to represent systems in the world. Despite much talk of models and representation, however, it is not yet clear what representation in this context amounts to nor what conditions a certain model needs to meet in order to be a representation of a certain system. In this thesis, I address these two questions. First, I will (...)
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  40. Gabriele Contessa (2006). Scientific Models, Partial Structures and the New Received View of Theories. Studies in History and Philosophy of Science Part A 37 (2):370-377.
  41. Gregory Cooper (1996). Theoretical Modeling and Biological Laws. Philosophy of Science 63 (3):35.
    Recent controversy over the existence of biological laws raises questions about the cognitive aims of theoretical modeling in that science. If there are no laws for successful theoretical models to approximate, then what is it that successful theories do? One response is to regard theoretical models as tools. But this instrumental reading cannot accommodate the explanatory role that theories are supposed to play. Yet accommodating the explanatory function, as articulated by Brandon and Sober for example, seems to involve us once (...)
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  42. Carl F. Craver (2006). When Mechanistic Models Explain. Synthese 153 (3):355-376.
    Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to show what is (...)
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  43. Newton da Costa & Steven French (2000). Models, Theories, and Structures: Thirty Years On. Philosophy of Science 67 (3):127.
    Thirty years after the conference that gave rise to The Structure of Scientific Theories, there is renewed interest in the nature of theories and models. However, certain crucial issues from thirty years ago are reprised in current discussions; specifically: whether the diversity of models in the science can be captured by some unitary account; and whether the temporal dimension of scientific practice can be represented by such an account. After reviewing recent developments we suggest that these issues can be accommodated (...)
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  44. Lindley Darden (2007). Mechanisms and Models. In David L. Hull & Michael Ruse (eds.), The Cambridge Companion to the Philosophy of Biology. Cambridge University Press.
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  45. Michael A. Day (1990). The No-Slip Condition of Fluid Dynamics. Erkenntnis 33 (3):285 - 296.
    In many applications of physics, boundary conditions have an essential role. The purpose of this paper is to examine from both a historical and philosophical perspective one such boundary condition, namely, the no-slip condition of fluid dynamics. The historical perspective is based on the works of George Stokes and serves as the foundation for the philosophical perspective. It is seen that historically the acceptance of the no-slip condition was problematic. Philosophically, the no-slip condition is interesting since the use of the (...)
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  46. Ruth Dolado & Francesc S. Beltran (2012). Dominance Hierarchy and Spatial Distribution in Captive Red-Capped Mangabeys (iCercocebus Torquatus Torquatus/I): Testing Hemelrijks Agent-Based Model. Interaction Studies 12 (3):461-473.
    We empirically tested Hemelrijk's agent-based model (Hemelrijk 1998), in which dyadic agonistic interaction between primate-group subjects determines their spatial distribution and whether or not the dominant subject has a central position with respect to the other subjects. We studied a group of captive red-capped mangabeys ( Cercocebus torquatus torquatus ) that met the optimal conditions for testing this model (e.g. a linear dominance hierarchy). We analyzed the spatial distribution of the subjects in relation to their rank in the dominance hierarchy (...)
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  47. Stephen M. Downes (2009). Models, Pictures, and Unified Accounts of Representation: Lessons From Aesthetics for Philosophy of Science. Perspectives on Science 17 (4):417-428.
    Several prominent philosophers of science, most notably Ron Giere, propose that scientific theories are collections of models and that models represent the objects of scientific study. Some, including Giere, argue that models represent in the same way that pictures represent. Aestheticians have brought the picturing relation under intense scrutiny and presented important arguments against the tenability of particular accounts of picturing. Many of these arguments from aesthetics can be used against accounts of representation in philosophy of science. I rely on (...)
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  48. Brian Epstein (2011). Agent-Based Modeling and the Fallacies of Individualism. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.
    Agent-based modeling is starting to crack problems that have resisted treatment by analytical methods. Many of these are in the physical and biological sciences, such as the growth of viruses in organisms, flocking and migration patterns, and models of neural interaction. In the social sciences, agent-based models have had success in such areas as modeling epidemics, traffic patterns, and the dynamics of battlefields. And in recent years, the methodology has begun to be applied to economics, simulating such phenomena as energy (...)
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  49. Jan Faye, Models, Theories, and Language.
    The semantic view on theories has been much in vogue over four decades as the successor of the syntactic view. In the present paper, I take issue with this approach by arguing that theories and models must be separated and that a theory should be considered to be a linguistic systems consisting of a vocabulary and a set of rules for the use of that vocabulary.
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  50. S. French & J. Ladyman (1997). Superconductivity and Structures: Revisiting the London Account. Studies in History and Philosophy of Science Part B 28 (3):363-393.
    Cartwright and her collaborators have elaborated a provocative view of science which emphasises the independence from theory &unknown;in methods and aims&unknown; of phenomenological model building. This thesis has been supported in a recent paper by an analysis of the London and London model of superconductivity. In the present work we begin with a critique of Cartwright's account of the relationship between theoretical and phenomenological models before elaborating an alternative picture within the framework of the partial structures version of the semantic (...)
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  51. Steven French (2010). Keeping Quiet on the Ontology of Models. Synthese 172 (2).
    Stein once urged us not to confuse the means of representation with that which is being represented. Yet that is precisely what philosophers of science appear to have done at the meta-level when it comes to representing the practice of science. Proponents of the so-called ‘syntactic’ view identify theories as logically closed sets of sentences or propositions and models as idealised interpretations, or ‘theoruncula, as Braithwaite called them. Adherents of the ‘semantic’ approach, on the other hand, are typically characterised as (...)
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  52. Roman Frigg, Models and Representation: Why Structures Are Not Enough.
    Models occupy a central role in the scientific endeavour. Among the many purposes they serve, representation is of great importance. Many models are representations of something else; they stand for, depict, or imitate a selected part of the external world (often referred to as target system, parent system, original, or prototype). Well-known examples include the model of the solar system, the billiard ball model of a gas, the Bohr model of the atom, the Gaussian-chain model of a polymer, the MIT (...)
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  53. Roman Frigg, Models in Physics.
    In its most common use, the term ‘model’ refers to a simplified and stylised version of the socalled target system, the part or aspect of the world that we are interested in. For instance, in order to determine the orbit of a planet moving around the sun we model the planet and the sun as perfect homogenous spheres that gravitationally interact with each other but nothing else in the universe, and then apply Newtonian mechanics to this system, which reveals that (...)
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  54. Roman Frigg (2010). Models and Fiction. Synthese 172 (2).
    Most scientific models are not physical objects, and this raises important questions. What sort of entity are models, what is truth in a model, and how do we learn about models? In this paper I argue that models share important aspects in common with literary fiction, and that therefore theories of fiction can be brought to bear on these questions. In particular, I argue that the pretence theory as developed by Walton (1990, Mimesis as make-believe: on the foundations of (...)
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  55. Roman Frigg (2008). Models in Science. In Edward N. Zalta (ed.), The Stanford Encyclopedia of Philosophy.
    Models are of central importance in many scientific contexts. The centrality of models such as the billiard ball model of a gas, the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, or general equilibrium models of markets in their respective domains are cases in point. (...)
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  56. Roman Frigg & Matthew Hunter (eds.) (2010). Beyond Mimesis and Convention: Representation in Art and Science. Boston Studies in Philosophy of Science.
    Featuring contributions from leading experts, this book represents the first collection of essays on the topic of art and science in the analytic tradition of ...
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  57. Alan Garnham (1994). March of the Models. International Studies in the Philosophy of Science 8 (1):37 – 39.
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  58. Axel Gelfert (2009). Rigorous Results, Cross-Model Justification, and the Transfer of Empirical Warrant: The Case of Many-Body Models in Physics. Synthese 169 (3):497 - 519.
    This paper argues that a successful philosophical analysis of models and simulations must accommodate an account of mathematically rigorous results. Such rigorous results may be thought of as genuinely model-specific contributions, which can neither be deduced from fundamental theory nor inferred from empirical data. Rigorous results provide new indirect ways of assessing the success of models and simulations and are crucial to understanding the connections between different models. This is most obvious in cases where rigorous results map different models on (...)
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  59. Axel Gelfert, Simulating Many-Body Models in Physics: Rigorous Results, 'Benchmarks', and Cross-Model Justification.
    This paper argues that, for a prospective philosophical analysis of models and simulations to be successful, it must accommodate an account of mathematically rigorous results. Such rigorous results are best thought of as genuinely model-specific contributions, which can neither be deduced from fundamental theory nor inferred from empirical data. Rigorous results often provide new indirect ways of assessing the success of computer simulations of individual models. This is most obvious in cases where rigorous results map different models on to one (...)
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  60. Emch Gérard G. (2007). Models and the Dynamics of Theory-Building in Physics. Part I—Modeling Strategies. Studies in History and Philosophy of Science Part B.
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  61. Emch Gérard G. (2007). Models and the Dynamics of Theory-Building in Physics. Part II—Case Studies. Studies in History and Philosophy of Science Part B.
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  62. Allan Gibbard & Hal R. Varian (1978). Economic Models. Journal of Philosophy 75 (11):664-677.
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  63. Ronald Giere, Models as Parts of Distributed Cognitive Systems.
    Recent work on the role of models in science has revealed a great many kinds of models performing many different roles. In this paper I suggest that one can find much unity among all this diversity by thinking of many models as being components of distributed cognitive systems. I begin by distinguishing the relevant notion of a distributed cognitive system and then give examples of different kinds of models that can be thought of as functioning as components of such systems. (...)
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  64. Ronald Giere, Models, Metaphysics, and Methodology.
    This paper constitutes my first attempt publicly to comment on Nancy Cartwright’s philosophy of science. That I have not done this earlier is primarily due to the great similarities in our views on topics where our interests overlap.2 But Cartwright’s work also covers topics I have never seriously considered, such as the use of linear models in economics and the measurement problem in quantum mechanics. Even the subject of probabilistic causation, to which I once contributed, is not one I now (...)
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  65. Ronald Giere, Using Models to Represent Reality.
    There has recently been an increase in interest in the role of models in science, of which the Pavia workshop on model-based reasoning is a manifestation. One result of this increased attention has been a proliferation of views on what models are and how they are used in science. In this presentation I will develop a unified interpretation of the nature and role of models in science. Central to this interpretation is an understanding of the relationships between models and other (...)
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  66. Ronald Giere (2010). An Agent-Based Conception of Models and Scientific Representation. Synthese 172 (2).
    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. (...)
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  67. Ronald N. Giere, Why Scientific Models Are Not Works of Fiction.
    The usual question, “Are models fictions?” is replaced by the question, “Should scientific models be regarded as works of fiction?” This makes it clear that the issue is not one of definition but of interpretation. First one must distinguish between the ontology of scientific models and their function in the practice of science. Theoretical models and works of fiction are ontologically on a par, their both being creations of human imagination. It is their differing functions in practice that makes it (...)
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  68. Ronald N. Giere (2004). How Models Are Used to Represent Reality. Philosophy of Science 71 (5):742-752.
    Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language-like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms "law" and "theory," I distinguish principles, specific conditions, models, (...)
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  69. Ronald N. Giere, How Models Are Used to Represent Physical Reality.
    What are models that they may be used to represent reality? Here is a first pass. Models are objects that can be used to represent reality by exhibiting a designated similarity to physical objects. To be more specific, I need to indicate the kinds of objects models may be and how they may exhibit a designated similarity to real objects. My prototype for a model is a standard road map. This is a physical object (usually made of paper) that I (...)
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  70. Ronald N. Giere (2001). The Nature and Function of Models. Behavioral and Brain Sciences 24 (6):1060-1060.
    There is no best scientific model of anything; there are only models more or less good for different purposes. Thus, there is no general answer to the question of whether one should model biological behavior using computer simulations or robots. It all depends on what one wants to learn. This is not a question about models, but about scientific goals.
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  71. Alessandro Giordani & Luca Mari (forthcoming). Modeling Measurement: Error and Uncertainty. In Marcel Boumans, Giora Hon & Arthur Petersen (eds.), Error and Uncertainty in Scientific Practice. Pickering & Chatto.
    In the last few decades the role played by models and modeling activities has become a central topic in the scientific enterprise. In particular, it has been highlighted both that the development of models constitutes a crucial step for understanding the world and that the developed models operate as mediators between theories and the world. Such perspective is exploited here to cope with the issue as to whether error-based and uncertainty-based modeling of measurement are incompatible, and thus alternative with one (...)
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  72. T. R. Girill (1972). Analogies and Models Revisited. Philosophy of Science 39 (2):241-244.
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  73. T. R. Girill (1971). Formal Models and Achinstein's "Analogies". Philosophy of Science 38 (1):96-104.
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  74. Stuart Glennan, A Model of Models.
    Although many philosophers of science have recognized the importance of modeling in contemporary science, relatively little work has been done in developing a general account of models. The most widely accepted account, put forth by advocates of the semantic conception of theories, misleadingly identifies scientific models with the models of mathematical logic. I present an alternative theory of scientific models in which models are defined by their representational relation to a physical system. I explore in some detail a particular sort (...)
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  75. Peter Godfrey-Smith (2009). Models and Fictions in Science. Philosophical Studies 143 (1):101 - 116.
    Non-actual model systems discussed in scientific theories are compared to fictions in literature. This comparison may help with the understanding of similarity relations between models and real-world target systems. The ontological problems surrounding fictions in science may be particularly difficult, however. A comparison is also made to ontological problems that arise in the philosophy of mathematics.
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  76. Till Grüne-Yanoff, Game-Theoretic Models, Stories, and Their Assessment.
    Ever since game theory has become a dominant mode of investigation in economics, critics have pointed out that it is a formally strong but empirically weak, if not empty, practice.1 We argue against the empirical irrelevance of game theory by investigating the architecture of game theoretic explanations more closely. In particular, we study the role of game models, and find that they assume the role of mediators as autonomous relaters of theory and phenomena. We further argue that stories play an (...)
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  77. Till Grüne-Yanoff (2009). Learning From Minimal Economic Models. Erkenntnis 70 (1):81 - 99.
    It is argued that one can learn from minimal economic models. Minimal models are models that are not similar to the real world, do not resemble some of its features, and do not adhere to accepted regularities. One learns from a model if constructing and analysing the model affects one’s confidence in hypotheses about the world. Economic models, I argue, are often assessed for their credibility. If a model is judged credible, it is considered to be a relevant possibility. Considering (...)
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  78. R. Harre (1988). Where Models and Analogies Really Count. International Studies in the Philosophy of Science 2 (2):118 – 133.
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  79. Henry Harris (ed.) (1979). Scientific Models and Man. Oxford University Press.
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  80. Todd Harris (2003). Data Models and the Acquisition and Manipulation of Data. Philosophy of Science 70 (5):1508-1517.
    This paper offers an account of data manipulation in scientific experiments. It will be shown that in many cases raw, unprocessed data is not produced, but rather a form of processed data that will be referred to as a data model. The language of data models will be used to provide a framework within which to understand a recent debate about the status of data and data manipulation. It will be seen that a description in terms of data models allows (...)
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  81. Stephan Hartmann (2008). Modeling in Philosophy of Science. In W. K. Essler & M. Frauchiger (eds.), Representation, Evidence, and Justification: Themes From Suppes. Ontos Verlag.
    Models are a principle instrument of modern science. They are built, applied, tested, compared, revised and interpreted in an expansive scientific literature. Throughout this paper, I will argue that models are also a valuable tool for the philosopher of science. In particular, I will discuss how the methodology of Bayesian Networks can elucidate two central problems in the philosophy of science. The first thesis I will explore is the variety-of-evidence thesis, which argues that the more varied the supporting evidence, the (...)
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  82. Stephan Hartmann (1999). Models and Stories in Hadron Physics. In Margaret Morrison & Mary Morgan (eds.), Models as Mediators.
    Fundamental theories are hard to come by. But even if we had them, they would be too complicated to apply. Quantum chromodynamics (QCD) 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 (...)
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  83. Stephan Hartmann (1995). Models as a Tool for Theory Construction: Some Strategies of Preliminary Physics. In William Herfel et al (ed.), Theories and Models in Scientific Processes. Rodopi.
    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 (...)
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  84. Stephan Hartmann, Carl Hoefer & Luc Bovens (eds.) (2008). Nancy Cartwright's Philosophy of Science. Routledge.
    Nancy Cartwright is one of the most distinguished and influential contemporary philosophers of science. Despite the profound impact of her work, until now there has not been 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 (...)
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  85. Michael Heidelberger, Models in Fluid Dynamics.
    In this paper, I would like to show that considering technological models as they arise in engineering disciplines can greatly enrich the philosophical perspective on models. In fluid mechanics, (at least) three types of models are distinguished: mathematical, computer and physical models. Very often, the choice of a particular mathematical, computer or physical model highly affects the type of solutions and the computational time needed for it. Technological models not only aim at a correct description of the physical phenomena, but (...)
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  86. R. F. Hendry & Stathis Psillos, How to Do Things with Theories: An Interactive View of Language and Models in Science.
    There are two major approaches to the individuation of scientific theories, that have been called syntactic and semantic. We prefer to call them the linguistic and non-linguistic conceptions. On the linguistic view, also known as the received view, theories are identified with (pieces of) languages. On the non-linguistic view, theories are identified with extra-linguistic structures, known as models. We would like to distinguish between strong and weak formulations of each approach. On the strong version of the linguistic approach, theories are (...)
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  87. Mary B. Hesse (1966). Models and Analogies in Science. University of Notre Dame Press.
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  88. Mary B. Hesse (1953). Models in Physics. British Journal for the Philosophy of Science 4 (15):198-214.
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  89. James Horgan (1994). Icon and Bild: A Note on the Analogical Structure of Models--The Role of Models in Experiment and Theory. British Journal for the Philosophy of Science 45 (2):599-604.
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  90. R. I. G. Hughes (1997). Models and Representation. Philosophy of Science 64 (4):336.
    A general account of modeling in physics is proposed. Modeling is shown to involve three components: denotation, demonstration, and interpretation. Elements of the physical world are denoted by elements of the model; the model possesses an internal dynamic that allows us to demonstrate theoretical conclusions; these in turn need to be interpreted if we are to make predictions. The DDI account can be readily extended in ways that correspond to different aspects of scientific practice.
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  91. E. H. Hutten (1954). The Rôle of Models in Physics. British Journal for the Philosophy of Science 4 (16):284-301.
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  92. M. Jeuken (1968). A Note on Models and Explanation in Biology. Acta Biotheoretica 18 (1-4).
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  93. Jonathan Michael Kaplan & Rasmus Grønfeldt Winther (2012). Prisoners of Abstraction? The Theory and Measure of Genetic Variation, and the Very Concept of "Race". Biological Theory 7 (1).
    It is illegitimate to read any ontology about "race" off of biological theory or data. Indeed, the technical meaning of "genetic variation" is fluid, and there is no single theoretical agreed-upon criterion for defining and distinguishing populations (or groups or clusters) given a particular set of genetic variation data. Thus, by analyzing three formal senses of "genetic variation"—diversity, differentiation, and heterozygosity—we argue that the use of biological theory for making epistemic claims about "race" can only seem plausible when it relies (...)
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  94. Joel Katzav (2013). Hybrid Models, Climate Models, and Inference to the Best Explanation. British Journal for the Philosophy of Science 64 (1):107-129.
    I examine the warrants we have in light of the empirical successes of a kind of model I call ‘hybrid models’, a kind that includes climate models among its members. I argue that these warrants’ strengths depend on inferential virtues that are not just explanatory virtues, contrary to what would be the case if inference to the best explanation (IBE) provided the warrants. I also argue that the warrants in question, unlike those IBE provides, guide inferences only to model implications (...)
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  95. Evelyn Fox Keller (2000). Models of and Models For: Theory and Practice in Contemporary Biology. Philosophy of Science 67 (3):86.
    Two decades of critique have sensitized historians and philosophers of science to the inadequacies of conventional dichotomies between theory and practice, thereby prompting the search for new ways of writing about science that are less beholden than the old ways to the epistemological mores of theoretical physics, and more faithful to the actual practices not only of physics but of all the natural sciences. The need for alternative descriptions seems particularly urgent if one is to understand the place of theory (...)
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  96. Scott A. Kleiner (2003). Explanatory Coherence and Empirical Adequacy: The Problem of Abduction, and the Justification of Evolutionary Models. Biology and Philosophy 18 (4).
    Foundationalist theories of justification for science were undermined by the theory-ladeness thesis, which has affinities with coherentist epistemologies. A challenge for defenders of coherentist theories of scientific justification is to specify coherence relations relevant to science and to show how these relations make the truth of their bearers likely. Coherence relations include characteristics that pick out better explanations in the implementation of abductive arguments. Empiricist philosophers have attacked abductive reasoning by claiming that explanatory virtues are pragmatic, having no implications regarding (...)
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  97. Tarja Knuuttila (2005). Models, Representation, and Mediation. Philosophy of Science 72 (5):1260-1271.
    Representation has been one of the main themes in the recent discussion of models. Several authors have argued for a pragmatic approach to representation that takes users and their interpretations into account. It appears to me, however, that this emphasis on representation places excessive limitations on our view of models and their epistemic value. Models should rather be thought of as epistemic artifacts through which we gain knowledge in diverse ways. Approaching models this way stresses their materiality and media-specificity. Focusing (...)
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  98. Tarja Knuuttila & Atro Voutilainen (2003). A Parser as an Epistemic Artifact: A Material View on Models. Philosophy of Science 70 (5):1484-1495.
    The purpose of this paper is to suggest that models in scientific practice can be conceived of as epistemic artifacts. Approaching models this way accommodates many such things that working scientists themselves call models but that the semantic conception of models does not duly recognize as such. That models are epistemic artifacts implies, firstly, that they cannot be understood apart from purposeful human activity; secondly, that they are somehow materialized inhabitants of the intersubjective field of that activity; and thirdly, that (...)
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  99. Jeffrey Koperski, Models. Internet Encyclopedia of Philosophy.
    The word “model” is highly ambiguous, and there is no uniform terminology used by either scientists or philosophers. Here, a model is considered to be a representation of some object, behavior, or system that one wants to understand. This article presents the most common type of models found in science as well as the different relations—traditionally called “analogies”—between models and between a given model and its subject. Although once considered merely heuristic devices, they are now seen as indispensable to modern (...)
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  100. Jeffrey Koperski (1998). Models, Confirmation, and Chaos. Philosophy of Science 65 (4):624-648.
    The use of idealized models in science is by now well-documented. Such models are typically constructed in a “top-down” fashion: starting with an intractable theory or law and working down toward the phenomenon. This view of model-building has motivated a family of confirmation schemes based on the convergence of prediction and observation. This paper considers how chaotic dynamics blocks the convergence view of confirmation and has forced experimentalists to take a different approach to model-building. A method known as “phase space (...)
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