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  1. Paulo Abrantes (2007). Models and the Dynamics of Theories. Philósophos - Revista de Filosofia 9 (2).
    : This paper gives a historical overview of the ways various trends in the philosophy of science dealt with models and their relationship with the topics of heuristics and theoretical dynamics. First of all, N. Campbell’s account of analogies as components of scientific theories is presented. Next, the notion of ‘model’ in the reconstruction of the structure of scientific theories proposed by logical empiricists is examined. This overview finishes with M. Hesse’s attempts to develop Campbell’s early ideas in terms of (...)
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  2. Peter Achinstein (1972). Models and Analogies: A Reply to Girill. Philosophy of Science 39 (2):235-240.
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  3. Peter Achinstein (1965). Theoretical Models. British Journal for the Philosophy of Science 16 (62):102-120.
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  4. 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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  5. Explanatory Adequacy (2013). Models of God. In Jeanine Diller & Asa Kasher (eds.), Models of God and Alternative Ultimate Realities. Springer. 43.
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  6. Andrew Adler (1972). Representation of Models of Full Theories. Mathematical Logic Quarterly 18 (12):183-188.
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  7. Evandro Agazzi (1998). Interpreting Reality: Models and Reference. Logique Et Analyse 41:343-363.
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  8. 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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  9. Eugen Altschul & Erwin Biser (1948). The Validity of Unique Mathematical Models in Science. Philosophy of Science 15 (1):11-24.
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  10. D. A. Anapolitanos (1989). Theories and Their Models. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 20 (2):201-211.
    Diese Abhandlung diskutiert und kritisiert einige Aspekte der syntaktischen Auffassung der wissenschaftlichen Theorien und tritt dafür ein, daß die einzig mögliche Alternative eine modell-theoretische Annäherung ist.
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  11. 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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  12. Rachel A. Ankeny & Sabina Leonelli (2011). What's so Special About Model Organisms? Studies in History and Philosophy of Science Part A 42 (2):313-323.
    This paper aims to identify the key characteristics of model organisms that make them a specific type of model within the contemporary life sciences: in particular, we argue that the term “model organism” does not apply to all organisms used for the purposes of experimental research. We explore the differences between experimental and model organisms in terms of their material and epistemic features, and argue that it is essential to distinguish between their representational scope and representational target. We also examine (...)
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  13. Jan Baedke (2013). The Epigenetic Landscape in the Course of Time: Conrad Hal Waddington's Methodological Impact on the Life Sciences. Studies in History and Philosophy of Science Part C 44 (4):756-773.
    It seems that the reception of Conrad Hal Waddington’s work never really gathered speed in mainstream biology. This paper, offering a transdisciplinary survey of approaches using his epigenetic landscape images, argues that (i) Waddington’s legacy is much broader than is usually recognized—it is widespread across the life sciences (e.g. stem cell biology, developmental psychology and cultural anthropology). In addition, I will show that (ii) there exist as yet unrecognized heuristic roles, especially in model building and theory formation, which Waddington’s images (...)
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  14. Tudor M. Baetu (2014). Models and the Mosaic of Scientific Knowledge. The Case of Immunology. Studies in History and Philosophy of Science Part C 45 (1):49-56.
    A survey of models in immunology is conducted and distinct kinds of models are characterized based on whether models are material or conceptual, the distinctiveness of their epistemic purpose, and the criteria for evaluating the goodness of a model relative to its intended purpose. I argue that the diversity of models in interdisciplinary fields such as immunology reflects the fact that information about the phenomena of interest is gathered from different sources using multiple methods of investigation. To each model is (...)
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  15. Daniela M. Bailer-Jones (2013). Scientific Models in Philosophy of Science. University of Pittsburgh Press.
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  16. 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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  17. 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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  18. Barry Barnes (1979). Models of Man. [REVIEW] British Journal for the History of Science 12 (1):104-104.
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  19. L. F. Barrett & J. A. Russell (2009). Circumplex Models. In David Sander & Klaus R. Scherer (eds.), The Oxford Companion to Emotion and the Affective Sciences. Oxford University Press. 85--88.
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  20. 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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  21. 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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  22. 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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  23. 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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  24. Jonas R. Becker Arenhart & Fernando Tf Moraes (2013). Structures, Languages and Models: A Unifying Approach. Logique Et Analyse 221:67-84.
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  25. Karim Joost Benammar (1993). Pictures of Thought: The Representational Function of Visual Models. Dissertation, The Pennsylvania State University
    Scientific inquiry makes use of visual models to represent empirical systems. Many philosophers claim that models function only as analogies, and that their role is limited to a didactic or heuristic role. I analyze four families of visual models: maps, graphical images from the study of turbulence, fractal images, and strange attractors. I show that these models are projected from data; that they are dynamic pictures, which can be manipulated by the researcher; that they are necessary for the theories in (...)
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  26. David Berlinski (1975). Mathematical Models of the World. Synthese 31 (2):211 - 227.
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  27. Gregor Betz (2013). Chaos, Plurality and Model Metrics in Climate Science. In Ulrich V. Gähde & Stephan Hartmann (eds.), Models, Simulation, and the Reduction of Complexity. de Gruyter. 255-264.
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  28. Richard J. Blackwell (1967). "Models and Analogies in Science," by Mary B. Hesse. Modern Schoolman 44 (4):404-405.
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  29. Alisa Bokulich (2013). Explanatory Models Versus Predictive Models: Reduced Complexity Modeling in Geomorphology. In Vassilios Karakostas & Dennis Dieks (eds.), Epsa11 Perspectives and Foundational Problems in Philosophy of Science. Springer. 115--128.
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  30. Alisa Bokulich (2011). How Scientific Models Can Explain. Synthese 180 (1):33 - 45.
    Scientific models invariably involve some degree of idealization, abstraction, or nationalization of their target system. Nonetheless, I argue that there are circumstances under which such false models can offer genuine scientific explanations. After reviewing three different proposals in the literature for how models can explain, I shall introduce a more general account of what I call model explanations, which specify the conditions under which models can be counted as explanatory. I shall illustrate this new framework by applying it to the (...)
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  31. 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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  32. 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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  33. 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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  34. 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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  35. 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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  36. Matthew J. Brown (2009). Science and Experience: A Deweyan Pragmatist Philosophy of Science. Dissertation, University of California, San Diego
    I resolve several pressing and recalcitrant problems in contemporary philosophy of science using resources from John Dewey's philosophy of science. I begin by looking at Dewey's epistemological and logical writings in their historical context, in order to understand better how Dewey's philosophy disappeared from the limelight, and I provide a reconstruction of his views. Then, I use that reconstruction to address problems of evidence, the social dimensions of science, and pluralism. Generally, mainstream philosophers of science with an interest in Dewey (...)
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  37. 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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  38. Otávio Bueno, Steven French & James Ladyman (2012). Models and Structures: Phenomenological and Partial. Studies in History and Philosophy of Science Part B 43 (1):43-46.
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  39. Otávio Bueno & Décio Krause (2010). Scientific Theories, Models, and the Semantic Approach. Principia 11 (2):187-201.
    According to the semantic view, a theory is characterized by a class of models. In this paper, we examine critically some of the assumptions that underlie this approach. First, we recall that models are models of something. Thus we cannot leave completely aside the axiomatization of the theories under consideration, nor can we ignore the metamathematics used to elaborate these models, for changes in the metamathematics often impose restrictions on the resulting models. Second, based on a parallel between van Fraassen’s (...)
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  40. A. V. Bushkovitch (1974). Models, Theories, and Kant. Philosophy of Science 41 (1):86-88.
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  41. 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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  42. H. G. Callaway (2014). Abduction, Competing Models and the Virtues of Hypotheses. In Lorenzo Magnani (ed.), (2014) Model-Based Reasoning in Science and Technology. Springer. 263-280.
    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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  43. 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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  44. 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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  45. 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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  46. Nancy Cartwright, Models and the Limits of Theory: Quantum Hamiltonians and the BCS Model of Superconductivity.
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  47. 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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  48. Nancy Cartwright, 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, there is neither a systematic exposition of Cartwright’s philosophy of science nor a collection of articles that contains in-depth discussions of the major themes of her philosophy. This book is devoted to a critical assessment of Cartwright’s philosophy of science and contains contributions from Cartwright's champions and critics. Broken into three parts, the book begins by addressing Cartwright's views on (...)
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  49. L. Casini (2014). Not-So-Minimal Models: Between Isolation and Imagination. Philosophy of the Social Sciences 44 (5):646-672.
    What can we learn from “minimal” economic models? I argue that learning from such models is not limited to conceptual explorations—which show how something could be the case—but may extend to explanations of real economic phenomena—which show how something is the case. A model may be minimal qua certain world-linking properties, and yet “not-so-minimal” qua learning, provided it is externally valid. This, in turn, depends on using the right principles for model building and not necessarily “isolating” principles. My argument is (...)
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  50. 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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