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  1. Peter Achinstein (1965). Scientific Theories and Empirical Significance. Review of Metaphysics 19:758-769.
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  2. Samuel Alberti (2003). Embryos in Wax: Models From the Ziegler Studio. With a Reprint of Embryological Wax Models by Friedrich Ziegler. [REVIEW] British Journal for the History of Science 36 (3):372-373.
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  3. Michael Weisberg Alkistis Elliott‐Graves (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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  4. Colin Allen (2014). Models, Mechanisms, and Animal Minds. Southern Journal of Philosophy 52 (S1):75-97.
    In this paper, I describe grounds for dissatisfaction with certain aspects of the sciences of animal cognition and argue that a turn toward mathematical modeling of animal cognition is warranted. I consider some objections to this call and argue that the implications of such a turn are not as drastic for ordinary, commonsense understanding of animal minds as they might seem.
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  5. 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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  6. Benedict Ashley & Eric Reitan (1997). On William A. Wallace, O.P., The Modeling of Nature. The Thomist 61:625-640.
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  7. 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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  8. 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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  9. 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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  10. Anouk Barberousse & Cyrille Imbert (2013). New Mathematics for Old Physics: The Case of Lattice Fluids. Studies in History and Philosophy of Science Part B 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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  11. 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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  12. 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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  13. Paul Frank Andrew Bartha (1994). Analogical Reasoning and Plausibility in the Sciences. Dissertation, University of Pittsburgh
    Analogical reasoning plays a significant role in the evolution of scientific thought. Not only is analogy extensively used in the early stages of investigation to demonstrate the plausibility of hypotheses, but in some fields, such as archaeology and evolutionary biology, it is often the strongest possible form of theoretical confirmation. This widely used form of reasoning, however, has seldom been subjected to rigorous examination by philosophers of science. Not surprisingly, there is a notable absence of standards for distinguishing between 'good' (...)
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  14. 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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  15. R. Bhavnani, D. Backer & R. Riolo (2008). Simulating Closed Regimes with Agent Based Models. Complexity 14 (1):36-44.
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  16. 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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  17. Richard J. Blackwell (1982). Models: Representation and the Scientific Understanding. By Marx W. Wartofsky. Modern Schoolman 60 (1):69-69.
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  18. 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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  19. Ingo Brigandt (forthcoming). Social Values Influence the Adequacy Conditions of Scientific Theories: Beyond Inductive Risk. Canadian Journal of Philosophy.
    The ‘death of evidence’ issue in Canada raises the specter of politicized science, and thus the question of what role social values may have in science and how this meshes with objectivity and evidence. I first criticize philosophical accounts that have to separate different steps of research to restrict the influence of social and other non-epistemic values. A prominent account that social values may play a role even in the context of theory acceptance is the argument from inductive risk. It (...)
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  20. Mark A. Burgman (1988). Building Models. BioScience 38 (6):426-427.
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  21. A. V. Bushkovitch (1977). The Concept of Model in Scientific Theory. International Logic Review 8 (1):24-31.
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  22. 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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  23. Jordi Cat (2012). Mauricio Suárez (Ed.): Fictions in Science. Philosophical Essays on Modeling and Idealization. [REVIEW] Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 43 (1):187-194.
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  24. 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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  25. 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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  26. Chuanfei Chin (2011). Models as Interpreters (with a Case Study From Pain Science). Studies in History and Philosophy of Science 42 (2):303-312.
    Most philosophical accounts of scientific models assume that models represent some aspect, or some theory, of reality. They also assume that interpretation plays only a supporting role. This paper challenges both assumptions. It proposes that models can be used in science to interpret reality. (a) I distinguish these interpretative models from representational ones. They find new meanings in a target system’s behaviour, rather than fit its parts together. They are built through idealisation, abstraction and recontextualisation. (b) To show how interpretative (...)
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  27. F. J. Clendinnen (1978). SUPPE, F. : "The Structure of Scientific Theories". [REVIEW] Australasian Journal of Philosophy 56:271.
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  28. 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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  29. Newton C. A. Costaa & Steven French (1990). The Model-Theoretic Approach in the Philosophy of Science. Philosophy of Science 57 (2):248-265.
    An introduction to the model-theoretic approach in the philosophy of science is given and it is argued that this program is further enhanced by the introduction of partial structures. It is then shown that this leads to a natural and intuitive account of both "iconic" and mathematical models and of the role of the former in science itself.
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  30. Carl F. Craver (2010). Prosthetic Models. Philosophy of Science 77 (5):840-851.
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  31. 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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  32. 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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  33. 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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  34. Richard David-Rus (2012). Explanation and Understanding Through Scientific Models. Institutul European.
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  35. 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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  36. 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 / Zeitschrift für Allgemeine Wissenschaftstheorie 43 (1):11-27.
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  37. 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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  38. 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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  39. 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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  40. S. Ducheyne (2008). Towards an Ontology of Scientific Models. Metaphysica 9 (1):119-127.
    Scientific models occupy centre stage in scientific practice. Correspondingly, in recent literature in the philosophy of science, scientific models have been a focus of research. However, little attention has been paid so far to the ontology of scientific models. In this essay, I attempt to clarify the issues involved in formulating an informatively rich ontology of scientific models. Although no full-blown theory—containing all ontological issues involved—is provided, I make several distinctions and point to several characteristic properties exhibited by scientific models (...)
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  41. A. R. E. (1967). Pattern Recognition: Theory, Experiment, Computer Simulations, and Dynamic Models of Form Perception and Discovery. [REVIEW] Review of Metaphysics 20 (4):743-743.
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  42. Arnold Eckhart, Simulation Models of the Evolution of Cooperation as Proofs of Logical Possibilities. How Useful Are They?
    This paper discusses critically what simulation models of the evolution of cooperation can possibly prove by examining Axelrod’s “Evolution of Cooperation” (1984) 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 (...)
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  43. Bruce Edmonds (2013). Complexity and Context-Dependency. Foundations of Science 18 (4):745-755.
    It is argued that given the “anti-anthropomorphic” principle—that the universe is not structured for our benefit—modelling trade-offs will necessarily mean that many of our models will be context-specific. It is argued that context-specificity is not the same as relativism. The “context heuristic”—that of dividing processing into rich, fuzzy context-recognition and crisp, conscious reasoning and learning—is outlined. The consequences of accepting the impact of this human heuristic in the light of the necessity of accepting context-specificity in our modelling of complex systems (...)
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  44. 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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  45. Curt F. Fey (1961). An Investigation of Some Mathematical Models for Learning. Journal of Experimental Psychology 61 (6):455.
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  46. 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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  47. 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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  48. 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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  49. 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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  50. Ulrich Gähde, Stephan Hartmann & Jörn Henning Wolf (eds.) (2013). Models, Simulations, and the Reduction of Complexity. De Gruyter.
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