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  1. Peter Achinstein (1965). Scientific Theories and Empirical Significance. Review of Metaphysics 19:758-769.
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  2. Eckhart Arnold (2013). Simulation Models of the Evolution of Cooperation as Proofs of Logical Possibilities: How Useful Are They? Etica E Politica 15 (2):101-138.
    This paper discusses critically what simulation models of the evolution ofcooperation can possibly prove by examining Axelrod’s “Evolution of Cooperation” and the modeling tradition it has inspired. Hardly any of the many simulation models of the evolution of cooperation in this tradition have been applicable empirically. Axelrod’s role model suggested a research design that seemingly allowed to draw general conclusions from simulation models even if the mechanisms that drive the simulation could not be identified empirically. But this research design was (...)
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  3. Peter C. Austin (2009). Are (the Log‐Odds of) Hospital Mortality Rates Normally Distributed? Implications for Studying Variations in Outcomes of Medical Care. Journal of Evaluation in Clinical Practice 15 (3):514-523.
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  4. Daniela M. Bailer-Jones (1999). Tracing the Development of Models in the Philosophy of Science. In L. Magnani, N. J. Nersessian & P. Thagard (eds.), Model-Based Reasoning in Scientific Discovery. Kluwer/Plenum. 23--40.
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  5. Katerina Bantinaki (2012). Beyond Mimesis and Convention: Representation in Art and Science. International Studies in the Philosophy of Science 26 (1):114 - 118.
    International Studies in the Philosophy of Science, Volume 26, Issue 1, Page 114-118, March 2012.
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  6. Anouk Barberousse & Cyrille Imbert (2013). New Mathematics for Old Physics: The Case of Lattice Fluids. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 44 (3):231-241.
    We analyze the effects of the introduction of new mathematical tools on an old branch of physics by focusing on lattice fluids, which are cellular automata -based hydrodynamical models. We examine the nature of these discrete models, the type of novelty they bring about within scientific practice and the role they play in the field of fluid dynamics. We critically analyze Rohrlich's, Fox Keller's and Hughes' claims about CA-based models. We distinguish between different senses of the predicates “phenomenological” and “theoretical” (...)
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  7. Anouk Barberousse & Marion Vorms (2014). About the Warrants of Computer-Based Empirical Knowledge. Synthese 191 (15):3595-3620.
    Computer simulations are widely used in current scientific practice, as a tool to obtain information about various phenomena. Scientists accordingly rely on the outputs of computer simulations to make statements about the empirical world. In that sense, simulations seem to enable scientists to acquire empirical knowledge. The aim of this paper is to assess whether computer simulations actually allow for the production of empirical knowledge, and how. It provides an epistemological analysis of present-day empirical science, to which the traditional epistemological (...)
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  8. Sam Baron (forthcoming). The Explanatory Dispensability of Idealizations. Synthese:1-22.
    Enhanced indispensability arguments seek to establish realism about mathematics based on the explanatory role that mathematics plays in science. Idealizations pose a problem for such arguments. Idealizations, in a similar way to mathematics, boost the explanatory credentials of our best scientific theories. And yet, idealizations are not the sorts of things that are supposed to attract a realist attitude. I argue that the explanatory symmetry between idealizations and mathematics can potentially be broken as follows: although idealizations contribute to the explanatory (...)
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  9. Mark A. Bedau (forthcoming). Testing Bottom-Up Models of Complex Citation Networks. .
    The robust behavior of the patent citation network is a complex target of recent bottom-up models in science. This paper investigates the purpose and testing of three especially simple bottom-up models of the citation count distribution observed in the patent citation network. The complex causal webs in the models generate weakly emergent patterns of behavior, and this explains both the need for empirical observation of computer simulations of the models and the epistemic harmlessness of the resulting epistemic opacity.
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  10. R. Bhavnani, D. Backer & R. Riolo (2008). Simulating Closed Regimes with Agent Based Models. Complexity 14 (1):36-44.
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  11. Thomas Bittner & Barry Smith (2001). A Taxonomy of Granular Partitions. In Spatial Information Theory. Foundations of Geographic Information Science. Lecture Notes in Computer Science 2205.
    In this paper we propose a formal theory of partitions (ways of dividing up or sorting or mapping reality) and we show how the theory can be applied in the geospatial domain. We characterize partitions at two levels: as systems of cells (theory A), and in terms of their projective relation to reality (theory B). We lay down conditions of well-formedness for partitions and we define what it means for partitions to project truly onto reality. We continue by classifying well-formed (...)
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  12. Richard J. Blackwell (1982). Models: Representation and the Scientific Understanding. By Marx W. Wartofsky. Modern Schoolman 60 (1):69-69.
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  13. Christopher Boehm (2007). Modeling Our Human Ancestor. In Stephen G. Post (ed.), Altruism and Health: Perspectives From Empirical Research. Oup Usa. 332.
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  14. Mark A. Burgman (1988). Building Models. BioScience 38 (6):426-427.
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  15. Michel Callon (1997). 1995. Four Models for the Dynamics of Science. In Alfred I. Tauber (ed.), Science and the Quest for Reality. New York University Press. 249--292.
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  16. Jordi Cat (2012). Mauricio Suárez (Ed.): Fictions in Science. Philosophical Essays on Modeling and Idealization. [REVIEW] Journal for General Philosophy of Science 43 (1):187-194.
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  17. Roger J. S. Chaffin & Douglas J. Herrmann (1981). Comprehension of Semantic Relationships and the Generality of Categorization Models. Bulletin of the Psychonomic Society 17 (2):69-72.
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  18. Anjan Chakravartty (2010). Perspectivism, Inconsistent Models, and Contrastive Explanation. Studies in History and Philosophy of Science Part A 41 (4):405-412.
    It is widely recognized that scientific theories are often associated with strictly inconsistent models, but there is little agreement concerning the epistemic consequences. Some argue that model inconsistency supports a strong perspectivism, according to which claims serving as interpretations of models are inevitably and irreducibly perspectival. Others argue that in at least some cases, inconsistent models can be unified as approximations to a theory with which they are associated, thus undermining this kind of perspectivism. I examine the arguments for perspectivism, (...)
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  19. Michael L. Cohen (2000). Testing Scientific Theories Through Validating Computer Models. Dissertation, University of Maryland, College Park
    Attempts by 20th century philosophers of science to define inductive concepts and methods concerning the support provided to scientific theories by empirical data have been unsuccessful. Although 20th century philosophers of science largely ignored statistical methods for testing theories, when they did address them they argued against rather than for their use. In contrast, this study demonstrates that traditional statistical methods used for validating computer simulation models provide tests of the scientific theories that those models may embody. This study shows (...)
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  20. Carl F. Craver (2010). Prosthetic Models. Philosophy of Science 77 (5):840-851.
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  21. Newton C. A. da Costa & Francisco Antonio Doria (1992). On the Incompleteness of Axiomatized Models for the Empirical Sciences. Philosophica 50.
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  22. Newton C. A. da Costa & Francisco Antonio Doria (1992). On the Incompleteness of Axiomatized Models for the Empirical Sciences. Philosophica 50.
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  23. Newton C. A. da Costa & Steven French (2003). Science and Partial Truth a Unitary Approach to Models and Scientific Reasoning. Monograph Collection (Matt - Pseudo).
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  24. Peter de Clercq (2006). Science at Court: The Eighteenth-Century Cabinet of Scientific Instruments and Models of the Dutch Stadholders. Annals of Science 45 (2):113-152.
    (1988). Science at court: the eighteenth-century cabinet of scientific instruments and models of the Dutch stadholders. Annals of Science: Vol. 45, No. 2, pp. 113-152.
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  25. Xavier de Donato Rodríguez & Alfonso Arroyo Santos (2012). The Structure of Idealization in Biological Theories: The Case of the Wright-Fisher Model. Journal for General Philosophy of Science 43 (1):11-27.
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  26. Rogier De Langhe & Matthias Greiff (2010). Standards and the Distribution of Cognitive Labour: A Model of the Dynamics of Scientific Activity. Logic Journal of the Igpl 18 (2):278-294.
    We present a model of the distribution of labour in science. Such models tend to rely on the mechanism of the invisible hand . Our analysis starts from the necessity of standards in distributed processes and the possibility of multiple standards in science. Invisible hand models turn out to have only limited scope because they are restricted to describing the atypical single-standard case. Our model is a generalisation of these models to J standards; single-standard models such as Kitcher are a (...)
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  27. Wolfgang Degen, Barbara Heller, Heinrich Herre & Barry Smith (2001). GOL: A General Ontological Language. In C. Welty B. Smith (ed.), Formal Ontology and Information Systems. Acm Press.
    Every domain-specific ontology must use as a framework some upper-level ontology which describes the most general, domain-independent categories of reality. In the present paper we sketch a new type of upper-level ontology, which is intended to be the basis of a knowledge modelling language GOL (for: 'General Ontological Language'). It turns out that the upper- level ontology underlying standard modelling languages such as KIF, F-Logic and CycL is restricted to the ontology of sets. Set theory has considerable mathematical power and (...)
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  28. Vernon G. Dobson & David Rose (1985). Models and Metaphysics: The Nature of Explanation Revisited. In David Rose & Vernon Dobson (eds.), Models of the Visual Cortex. New York: John Wiley & Sons. 22--36.
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  29. Alkistis Elliott‐Graves & Michael Weisberg (2014). Idealization. Philosophy Compass 9 (3):176-185.
    This article reviews the recent literature on idealization, specifically idealization in the course of scientific modeling. We argue that idealization is not a unified concept and that there are three different types of idealization: Galilean, minimalist, and multiple models, each with its own justification. We explore the extent to which idealization is a permanent feature of scientific representation and discuss its implications for debates about scientific realism.
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  30. Curt F. Fey (1961). An Investigation of Some Mathematical Models for Learning. Journal of Experimental Psychology 61 (6):455.
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  31. Bas C. Van Fraassen & Pérez Ransanz (1985). On the Question of Identification of a Scientific Theory (A Reply to "Van Fraassen's Concept of Empirical Theory" by Pérez Ransanz). Critica 17 (51):21 - 29.
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  32. Roman Frigg & Stephan Hartmann (2005). Scientific Models. In Sahotra Sarkar et al (ed.), The Philosophy of Science: An Encyclopedia, Vol. 2. Routledge.
    Models are of central importance in many scientific contexts. The roles the MIT bag model of the nucleon, the billiard ball model of a gas, the Bohr model of the atom, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka- Volterra model of predator-prey interaction, agent-based and evolutionary models of social interaction, or general equilibrium models of markets play in their respective domains are cases in point.
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  33. Roman Frigg, Stephan Hartmann & Cyrille Imbert (2009). Models and Simluations. Synthese 169 (3).
    Special issue. With contributions by Anouk Barberouse, Sarah Francescelli and Cyrille Imbert, Robert Batterman, Roman Frigg and Julian Reiss, Axel Gelfert, Till Grüne-Yanoff, Paul Humphreys, James Mattingly and Walter Warwick, Matthew Parker, Wendy Parker, Dirk Schlimm, and Eric Winsberg.
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  34. Andrew J. B. Fugard & Keith Stenning (2013). Statistical Models as Cognitive Models of Individual Differences in Reasoning. Argument and Computation 4 (1):89 - 102.
    (2013). Statistical models as cognitive models of individual differences in reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 89-102. doi: 10.1080/19462166.2012.674061.
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  35. Ulrich Gähde, Stephan Hartmann & Jörn Henning Wolf (eds.) (2013). Models, Simulations, and the Reduction of Complexity. De Gruyter.
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  36. Axel Gelfert (2013). Strategies of Model-Building in Condensed Matter Physics: Trade-Offs as a Demarcation Criterion Between Physics and Biology? Synthese 190 (2):253-272.
    This paper contrasts and compares strategies of model-building in condensed matter physics and biology, with respect to their alleged unequal susceptibility to trade-offs between different theoretical desiderata. It challenges the view, often expressed in the philosophical literature on trade-offs in population biology, that the existence of systematic trade-offs is a feature that is specific to biological models, since unlike physics, biology studies evolved systems that exhibit considerable natural variability. By contrast, I argue that the development of ever more sophisticated experimental, (...)
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  37. Axel Gelfert (2011). Model-Based Representation in Scientific Practice: New Perspectives. Studies in History and Philosophy of Science 42 (2):251-252.
    Editorial introduction to special issue on 'Model-based representation in scientific practice'.
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  38. Michel Ghins (2012). Scientific Representation and Realism. Principia 15 (3):461-474.
    After a brief presentation of what I take to be the representational démarche in science, I stress the fundamental role of true judgements in model construction. The success and correctness of a representation rests on the truth of judgements which attribute properties to real targeted entities, called “ontic judgements”. I then present what van Fraassen calls “the Loss of Reality objection”. After criticizing his dissolution of the objection, I offer an alternative way of answering the Loss of Reality objection by (...)
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  39. Ronald N. Giere (1984). Causal Models with Frequency Dependence. Journal of Philosophy 81 (7):384-391.
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  40. Clark Glymour (2013). Theoretical Equivalence and the Semantic View of Theories. Philosophy of Science 80 (2):286-297.
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  41. Patrick Grim & Nicholas Rescher (2013). How Modeling Can Go Wrong. Philosophy and Technology 26 (1):75-80.
    Modeling and simulation clearly have an upside. My discussion here will deal with the inevitable downside of modeling — the sort of things that can go wrong. It will set out a taxonomy for the pathology of models — a catalogue of the various ways in which model contrivance can go awry. In the course of that discussion, I also call on some of my past experience with models and their vulnerabilities.
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  42. H. J. Groenewold (1956). Quantum Mechanics and its Models. Synthese 10 (1):203 - 209.
  43. Till Grüne-Yanoff, Appraising Non-Representational Models.
    Many scientific models are non-representational in that they refer to merely possible processes, background conditions and results. The paper shows how such non-representational models can be appraised, beyond the weak role that they might play as heuristic tools. Using conceptual distinctions from the discussion of how-possibly explanations, six types of models are distinguished by their modal qualities of their background conditions, model processes and model results. For each of these types, an actual model example – drawn from economics, biology, psychology (...)
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  44. Ç Gülçiçek & B. Güneş (2004). Materializing the Concepts During Science Instruction: Modeling Strategy, Computer Simulations and Analogies. Science and Education 29 (134):36-48.
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  45. Joseph Y. Halpern & Christopher Hitchcock (2013). Compact Representations of Extended Causal Models. Cognitive Science 37 (6):986-1010.
    Judea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of normality. In Halpern and Hitchcock (2011), we offer a definition of actual causation using extended causal models, which include information about both causal structure and normality. Extended causal models are potentially very complex. In this study, we show how it (...)
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  46. Bert Hamminga & Neil B. De Marchi (eds.) (1994). Idealization Vi: Idealization in Economics. Rodopi.
    Introduction. Bert HAMMINGA and Neil DE MARCHI: Préface. Bert HAMMINGA and Neil DE MARCHI: Idealization and the Defence of Economics: Notes Toward a History. Part I: General Observations on Idealization in Economics. Kevin D. HOOVER: Six Queries about Idealization in an Empirical Context. Bernard WALLISER: Three Generalization Processes for Economic Models. Steven COOK and David HENDRY: The Theory of Reduction in Econometrics. Maarten C.W. JANSSEN: Economic Models and Their Applications. Adolfo GARCÍA DE LA SIENRA: Idealization and Empirical Adequacy in Economic (...)
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  47. Stephan Hartmann (1996). The World as a Process: Simulations in the Natural and Social Sciences. In Rainer Hegselmann (ed.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View.
    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? (...)
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  48. Stephan Hartmann (1995). Modelle Und Forschungsdynamik: Strategien der Zeitgenössischen Physik. Praxis der Naturwissenschaften - Physik 1:33-41.
    An Beispielen aus der Entwicklung der Elementarteilchenphysik wird aufgezeigt, welche Rolle Modelle im Entstehungsprozess einer physikalischen Theorie spielen.
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  49. Stephan Hartmann (1995). Simulation. In Jürgen Mittelstrass (ed.), Enzyklopädie Philosophie und Wissenschaftstheorie, Vol. 3. Metzler.
    Simulation (von lat. simulare, engl. simulation, franz. simulation, ital. simulazione), Bezeichnung für die Nachahmung eines Prozesses durch einen anderen Prozeß. Beide Prozesse laufen auf einem bestimmten System ab. Simuliertes u. simulierendes System (der Simulator in der Kybernetik) können dabei auf gleichen oder unterschiedlichen Substraten realisiert sein.
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  50. Stephan Hartmann & Roman Frigg (2006). Models in Science. In Ed Zalta (ed.), The Stanford Encyclopedia of Philosophy. Stanford.
    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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