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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. pp. 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. Andrew Adler (1972). Representation of Models of Full Theories. Zeitschrift fur mathematische Logik und Grundlagen der Mathematik 18 (12):183-188.
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  8. Evandro Agazzi (1998). Interpreting Reality: Models and Reference. Logique Et Analyse 41:343-363.
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  9. 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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  10. Gordon W. Allport (1947). Scientific Models and Human Morals. Psychological Review 54 (4):182-192.
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  11. Eugen Altschul & Erwin Biser (1948). The Validity of Unique Mathematical Models in Science. Philosophy of Science 15 (1):11-24.
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  12. 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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  13. 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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  14. 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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  15. Eckhart Arnold (1st ed. 2015). How Models Fail. In Catrin Misselhorn (ed.), Collective Agency and Cooperation in Natural and Artificial Systems. Springer Verlag.
    Simulation models of the Reiterated Prisoner's Dilemma (in the following: RPD-models) are since 30 years considered as one of the standard tools to study the evolution of cooperation (Rangoni 2013; Hoffmann 2000). A considerable number of such simulation models has been produced by scientists. Unfortunately, though, none of these models has empirically been verified and there exists no example of empirical research where any of the RPD-models has successfully been employed to a particular instance of cooperation. Surprisingly, this has not (...)
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  16. 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: Studies in History and Philosophy of Biological and Biomedical Sciences 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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  17. Tudor M. Baetu (2014). Models and the Mosaic of Scientific Knowledge. The Case of Immunology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 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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  18. Daniela M. Bailer-Jones (2013). Scientific Models in Philosophy of Science. University of Pittsburgh Press.
    Scientists have used models for hundreds of years as a means of describing phenomena and as a basis for further analogy. In _Scientific Models in Philosophy of Science, _Daniela Bailer-Jones assembles an original and comprehensive philosophical analysis of how models have been used and interpreted in both historical and contemporary contexts. Bailer-Jones delineates the many forms models can take, and how they are put to use. She examines early mechanical models employed by nineteenth-century physicists such as Kelvin and Maxwell, describes (...)
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  19. Daniela M. Bailer-Jones (2009). Scientific Models in Philosophy of Science. University of Pittsburgh Press.
    Scientists have used models for hundreds of years as a means of describing phenomena and as a basis for further analogy. In _Scientific Models in Philosophy of Science, _Daniela Bailer-Jones assembles an original and comprehensive philosophical analysis of how models have been used and interpreted in both historical and contemporary contexts. Bailer-Jones delineates the many forms models can take, and how they are put to use. She examines early mechanical models employed by nineteenth-century physicists such as Kelvin and Maxwell, describes (...)
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  20. 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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  21. 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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  22. Barry Barnes (1979). Models of Man. [REVIEW] British Journal for the History of Science 12 (1):104-104.
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  23. 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. pp. 85--88.
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  24. Ann-Sophie Barwich (2015). Bending Molecules or Bending the Rules? The Application of Theoretical Models in Fragrance Chemistry. Perspectives on Science 23 (4):443-465.
    What does it take for a scientific model to represent? Scientific models have received a great deal of attention in recent philosophical literature. Following Morgan and Morrison’s account of “Models as Mediators”, analysis of how models represent has changed from questioning what properties of models can be said to correlate with the world to asking how models are used to relate to an intended target-system. This turn to a practice-oriented approach of understanding models was a response to a general philosophical (...)
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  25. Ann-Sophie Barwich (2014). Fiction in Science? Exploring the Reality of Theoretical Entities. In Javier Cumpa, Greg Jesson & Guido Bonino (eds.), Defending Realism: Ontological and Epistemological Investigations. De Gruyter. pp. 291-310.
    This paper revisits the concept of fiction employed in recent debates about the reality of theoretical entities in the philosophy of science. From an anti-realist perspective the dependence of evidence for some scientific entities on mediated forms of observation and modelling strategies reflects a degree of construction that is argued to closely resemble fiction. As a realist’s response to this debate, this paper provides an analysis of fictional entities in comparison to real ones. I argue that the distinction between fictional (...)
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  26. 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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  27. Robert 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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  28. 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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  29. Bert Baumgaertner (2014). Yes, No, Maybe So: A Veritistic Approach to Echo Chambers Using a Trichotomous Belief Model. Synthese 191 (11):2549-2569.
    I approach the study of echo chambers from the perspective of veritistic social epistemology. A trichotomous belief model is developed featuring a mechanism by which agents will have a tendency to form agreement in the community. The model is implemented as an agent-based model in NetLogo and then used to investigate a social practice called Impartiality, which is a plausible means for resisting or dismantling echo chambers. The implementation exposes additional factors that need close consideration in an evaluation of Impartiality. (...)
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  30. 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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  31. 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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  32. 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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  33. David Berlinski (1975). Mathematical Models of the World. Synthese 31 (2):211 - 227.
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  34. Tommaso Bertolotti (2015). On Biological and Verbal Camouflage: The Strategic Use of Models in Non-Scientific Thinking. In Patterns of Rationality. Springer Verlag.
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  35. Tommaso Bertolotti (2015). Proto-Models, Mental Models and Scientific Models. In Patterns of Rationality. Springer Verlag.
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  36. Gregor Betz (2015). Are Climate Models Credible Worlds? Prospects and Limitations of Possibilistic Climate Prediction. European Journal for Philosophy of Science 5 (2):191-215.
    Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would allow one to (...)
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  37. 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. pp. 255-264.
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  38. Richard J. Blackwell (1967). "Models and Analogies in Science," by Mary B. Hesse. Modern Schoolman 44 (4):404-405.
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  39. 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. pp. 115--128.
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  40. 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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  41. Lawrence A. Boland (2014). Model Building in Economics: Its Purposes and Limitations. Cambridge University Press.
    Concern about the role and the limits of modeling has heightened after repeated questions were raised regarding the dependability and suitability of the models that were used in the run-up to the 2008 financial crash. In this book, Lawrence Boland provides an overview of the practices of and the problems faced by model builders to explain the nature of models, the modeling process, and the possibility for and nature of their testing. In a reflective manner, the author raises serious questions (...)
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  42. Agnes Bolinska (forthcoming). Successful Visual Epistemic Representation. Studies in History and Philosophy of Science Part A.
    In this paper, I characterize visual epistemic representations as concrete two- or three-dimensional tools for conveying information about aspects of their target systems or phenomena of interest. I outline two features of successful visual epistemic representation: that the vehicle of representation contain sufficiently accurate information about the phenomenon of interest for the user’s purpose, and that it convey this information to the user in a manner that makes it readily available to her. I argue that actual epistemic representation may involve (...)
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  43. 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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  44. 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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  45. 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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  46. Thomas Boyer-Kassem (2014). Layers of Models in Computer Simulations. International Studies in the Philosophy of Science 28 (4):417-436.
    I discuss here the definition of computer simulations, and more specifically the views of Humphreys, who considers that an object is simulated when a computer provides a solution to a computational model, which in turn represents the object of interest. I argue that Humphreys's concepts are not able to analyse fully successfully a case of contemporary simulation in physics, which is more complex than the examples considered so far in the philosophical literature. I therefore modify Humphreys's definition of simulation. I (...)
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  47. 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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  48. Eric Brandstedt (2016). The Savings Problem in the Original Position: Assessing and Revising a Model. Canadian Journal of Philosophy:1-21.
    The common conception of justice as reciprocity seemingly is inapplicable to relations between non-overlapping generations. This is a challenge also to John Rawls’s theory of justice as fairness. This text responds to this by way of reinterpreting and developing Rawls’s theory. First, by examining the original position as a model, some revisions of it are shown to be wanting. Second, by drawing on the methodology of constructivism, an alternative solution is proposed: an amendment to the primary goods named ‘sustainability of (...)
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  49. 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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  50. 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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