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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. Nicola Angius & Guglielmo Tamburrini (2011). Scientific Theories of Computational Systems in Model Checking. Minds and Machines 21 (2):323-336.
    Model checking, a prominent formal method used to predict and explain the behaviour of software and hardware systems, is examined on the basis of reflective work in the philosophy of science concerning the ontology of scientific theories and model-based reasoning. The empirical theories of computational systems that model checking techniques enable one to build are identified, in the light of the semantic conception of scientific theories, with families of models that are interconnected by simulation relations. And the mappings between these (...)
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  6. 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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  7. Benedict Ashley & Eric Reitan (1997). On William A. Wallace, O.P., The Modeling of Nature. The Thomist 61:625-640.
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  8. 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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  9. 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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  10. Wolfgang Balzer & C. Ulises Moulines (1999). Structuralist Theory of Science. Erkenntnis 51 (2):353-356.
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  11. S. Bangu (2006). Steiner on the Applicability of Mathematics and Naturalism. Philosophia Mathematica 14 (1):26-43.
    Steiner defines naturalism in opposition to anthropocentrism, the doctrine that the human mind holds a privileged place in the universe. He assumes the anthropocentric nature of mathematics and argues that physicists' employment of mathematically guided strategies in the discovery of quantum mechanics challenges scientists' naturalism. In this paper I show that Steiner's assumption about the anthropocentric character of mathematics is questionable. I draw attention to mathematicians' rejection of what Maddy calls ‘definabilism’, a methodological maxim governing the development of mathematics. I (...)
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  12. Sorin Bangu (2012). The Applicability of Mathematics in Science: Indispensability and Ontology. Palgrave Macmillan.
  13. Sorin Bangu (2009). Wigner's Puzzle for Mathematical Naturalism. International Studies in the Philosophy of Science 23 (3):245-263.
    I argue that a recent version of the doctrine of mathematical naturalism faces difficulties arising in connection with Wigner's old puzzle about the applicability of mathematics to natural science. I discuss the strategies to solve the puzzle and I show that they may not be available to the naturalist.
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  14. 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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  15. 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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  16. 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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  17. 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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  18. Thomas William Barrett & Hans Halvorson (2016). Glymour and Quine on Theoretical Equivalence. Journal of Philosophical Logic 45 (5):467-483.
    Glymour and Quine propose two different formal criteria for theoretical equivalence. In this paper we examine the relationships between these criteria.
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  19. 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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  20. Mark A. Bedau (2014). Testing Bottom-Up Models of Complex Citation Networks. Philosophy of Science 81 (5):1131-1143.
    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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  21. Gregor Betz (2009). Underdetermination, Model-Ensembles and Surprises: On the Epistemology of Scenario-Analysis in Climatology. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 40 (1):3-21.
    As climate policy decisions are decisions under uncertainty, being based on a range of future climate change scenarios, it becomes a crucial question how to set up this scenario range. Failing to comply with the precautionary principle, the scenario methodology widely used in the Third Assessment Report of the International Panel on Climate Change (IPCC) seems to violate international environmental law, in particular a provision of the United Nations Framework Convention on Climate Change. To place climate policy advice on a (...)
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  22. R. Bhavnani, D. Backer & R. Riolo (2008). Simulating Closed Regimes with Agent Based Models. Complexity 14 (1):36-44.
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  23. 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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  24. Richard J. Blackwell (1982). Models: Representation and the Scientific Understanding. By Marx W. Wartofsky. Modern Schoolman 60 (1):69-69.
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  25. Richard J. Blackwell (1972). "Minnesota Studies in the Philosophy of Science, Volume 4: Analyses of Theories and Methods of Physics and Psychology," Ed. M. Radner and S. Winokur; and "Volume 5: Historical and Philosophical Perspectives of Science," Ed. R. Stuewer. [REVIEW] Modern Schoolman 49 (2):181-184.
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  26. Rens Bod, Explaining New Phenomena in Terms of Previous Phenomena.
    It has become increasingly clear that natural phenomena cannot be formally deduced from laws but that almost every phenomenon has its own particular way of being linked to higher-level generalizations, usually via approximations, normalizations and corrections. This article deals with the following problem: if there are no general principles to link laws to phenomena, and if each phenomenon has its own way of being explained, how can we -- or how can a theory -- explain any new phenomenon? I will (...)
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  27. 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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  28. Ingo Brigandt (2015). Social Values Influence the Adequacy Conditions of Scientific Theories: Beyond Inductive Risk. Canadian Journal of Philosophy 45 (3):326-356.
    The ‘death of evidence’ issue in Canada raises the spectre 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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  29. Ingo Brigandt, Sara Green & Maureen A. O'Malley (forthcoming). Systems Biology and Mechanistic Explanation. In Stuart Glennan & Phyllis Illari (eds.), The Routledge Handbook of Mechanisms and Mechanical Philosophy.
    We address the question of whether and to what extent explanatory and modelling strategies in systems biology are mechanistic. After showing how dynamic mathematical models are actually required for mechanistic explanations of complex systems, we caution readers against expecting all systems biology to be about mechanistic explanations. Instead, the aim may be to generate topological explanations that are not standardly mechanistic, or to arrive at design principles that explain system organization and behaviour in general, but not specific mechanisms. These abstraction (...)
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  30. A. V. Bushkovitch (1977). The Concept of Model in Scientific Theory. International Logic Review 8 (1):24-31.
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  31. 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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  32. 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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  33. 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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  34. 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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  35. Chuanfei Chin (2011). Models as Interpreters. 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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  36. F. J. Clendinnen (1978). SUPPE, F. : "The Structure of Scientific Theories". [REVIEW] Australasian Journal of Philosophy 56:271.
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  37. 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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  38. 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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  39. Carl F. Craver (2010). Prosthetic Models. Philosophy of Science 77 (5):840-851.
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  40. 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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  41. 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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  42. 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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  43. Lorraine Daston (2008). Science Without Laws: Model Systems, Cases, Exemplary Narratives. Common Knowledge 14 (3):494-494.
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  44. Edoardo Datteri & Federico Laudisa (2012). Model Testing, Prediction and Experimental Protocols in Neuroscience: A Case Study. Studies in History and Philosophy of Science Part C 43 (3):602-610.
    In their theoretical and experimental reflections on the capacities and behaviours of living systems, neuroscientists often formulate generalizations about the behaviour of neural circuits. These generalizations are highly idealized, as they omit reference to the myriads of conditions that could perturb the behaviour of the modelled system in real-world settings. This article analyses an experimental investigation of the behaviour of place cells in the rat hippocampus, in which highly idealized generalizations were tested by comparing predictions flowing from them with real-world (...)
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  45. Richard David-Rus (2012). Explanation and Understanding Through Scientific Models. Institutul European.
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  46. Todd Newman Davis (2001). Science and the Constitutive a Priori: Ian Hacking's Philosophy of Scientific Practice in the History of Philosophy of Science. Dissertation, Duke University
    In this dissertation, I consider the relations between the broadly Kantian idea of the constitutive a priori, understood as the conditions of the possibility for scientific judgement, and the appeal to actual scientific practice in recent history and philosophy of science. Philosophies of scientific practice, particularly Ian Hacking's, are critical of philosophy of science as the analysis of scientific theories. Using a reading of Hacking's philosophical interpretation of scientific practice to motivate the construction of a conceptual genealogy of the epistemological (...)
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  47. Henk de bij Weg (2001). The Commonsense Conception and its Relation to Scientific Theory. Philosophical Explorations 1 (1):17-30.
    In studying what people do two points of view can be distinguished: We can choose the perspective of the actors themselves (the actor’s perspective), or we can look at what is going on from the outside, from a distance (the researcher’s perspective). Regarding the relation between both points of view three standpoints have been defended.
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  48. 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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  49. 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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  50. Rogier de Langhe (2010). Models in Science: Essays on Scientific Virtues, Scientific Pluralism and the Distribution of Labour in Science. Erasmus Journal for Philosophy and Economics 3 (2):146-147.
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