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  1. Daniela M. Bailer-Jones (2003). When Scientific Models Represent. International Studies in the Philosophy of Science 17 (1):59 – 74.
    Scientific models represent aspects of the empirical world. I explore to what extent this representational relationship, given the specific properties of models, can be analysed in terms of propositions to which truth or falsity can be attributed. For example, models frequently entail false propositions despite the fact that they are intended to say something "truthful" about phenomena. I argue that the representational relationship is constituted by model users "agreeing" on the function of a model, on the fit with data and (...)
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  2. Vadim Batitsky (2000). A Realistic Look at Putnam's Argument Against Realism. Foundations of Science 5 (3):299-321.
    Putnam's ``model-theoretic'' argument against metaphysical realism presupposes that an ideal scientific theory is expressible in a first order language. The central aim of this paper is to show that Putnam's ``first orderization'' of science, although unchallenged by numerous critics, makes his argument unsound even for adequate theories, never mind an ideal one. To this end, I will argue that quantitative theories, which dominate the natural sciences, can be adequately interpreted and evaluated only with the help of so-called theories of measurement (...)
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  3. 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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  4. Giovanni Boniolo (2007). On Scientific Representations: From Kant to a New Philosophy of Science. Palgrave Macmillan.
    Scientific concepts, laws, theories, models and thought experiments are representations but uniquely different. In On Scientific Representation each is given a full philosophical exploration within an original, coherent philosophical framework that is strongly rooted in the Kantian tradition (Kant, Hertz, Vaihinger, Cassirer). Through a revisionist historical approach, Boniolo shows how the Kantian tradition can help us renew and rethink contemporary issues in epistemology and the philosophy of science.
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  5. Otavio Bueno & Steven French (2011). How Theories Represent. British Journal for the Philosophy of Science 62 (4):857-894.
    An account of scientific representation in terms of partial structures and partial morphisms is further developed. It is argued that the account addresses a variety of difficulties and challenges that have recently been raised against such formal accounts of representation. This allows some useful parallels between representation in science and art to be drawn, particularly with regard to apparently inconsistent representations. These parallels suggest that a unitary account of scientific and artistic representation is possible, and our article can be viewed (...)
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  6. 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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  7. Anjan Chakravartty, Truth and Representation in Science: Two Inspirations From Art.
    Realists regarding scientific knowledge – those who think that our best scientific representations truly describe both observable and unobservable aspects of the natural world – have special need of a notion of approximate truth. Since theories and models are rarely considered true simpliciter, the realist requires some means of making sense of the claim that they may be false and yet close to the truth, and increasingly so over time. In this paper, I suggest that traditional approaches to approximate truth (...)
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  8. Anjan Chakravatty (2010). Informational Versus Functional Theories of Scientific Representation. Synthese 172 (2):197 - 213.
    Recent work in the philosophy of science has generated an apparent conflict between theories attempting to explicate the nature of scientific representation. On one side, there are what one might call 'informational' views, which emphasize objective relations (such as similarity, isomorphism, and homomorphism) between representations (theories, models, simulations, diagrams, etc.) and their target systems. On the other side, there are what one might call 'functional' views, which emphasize cognitive activities performed in connection with these targets, such as interpretation and inference. (...)
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  9. Gabriele Contessa, Disentangling Scientific Representation.
    The main aim of this paper is to disentangle three senses in which we can say that a model represents a system—denotation epistemic representation, and successful epistemic representation--and to individuate what questions arise from each sense of the notion of representation as used in this context. Also, I argue that a model is an epistemic representation of a system only if a user adopts a general interpretation of the model in terms of a system. In the process, I hope to (...)
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  10. Gabriele Contessa, Scientific Representation, Smilarity and Prediction.
    In this paper, I consider how different versions of the similarity account of scientific representation might apply to a simple case of scientific representation, in which a model is used to predict the behaviour of a system. I will argue that the similarity account is potentially susceptible to the problem of accidental similarities between the model and the system and that, if it is to avoid this problem, one has to specify which similarities have to hold between a model and (...)
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  11. Gabriele Contessa (2011). Scientific Models and Representation. In Steven French & Juha Saatsi (eds.), The Continuum Companion to the Philosophy of Science. Continuum Press. 120--137.
    My two daughters would love to go tobogganing down the hill by themselves, but they are just toddlers and I am an apprehensive parent, so, before letting them do so, I want to ensure that the toboggan won’t go too fast. But how fast will it go? One way to try to answer this question would be to tackle the problem head on. Since my daughters and their toboggan are initially at rest, according to classical mechanics, their final velocity will (...)
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  12. Gabriele Contessa (2007). Representing Reality: The Ontology of Scientific Models and Their Representational Function. Dissertation, University of London
    Today most philosophers of science believe that models play a central role in science and that one of the main functions of scientific models is to represent systems in the world. Despite much talk of models and representation, however, it is not yet clear what representation in this context amounts to nor what conditions a certain model needs to meet in order to be a representation of a certain system. In this thesis, I address these two questions. First, I will (...)
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  13. Gabriele Contessa (2007). Scientific Representation, Interpretation, and Surrogative Reasoning. Philosophy of Science 74 (1):48-68.
    In this paper, I develop Mauricio Suárez’s distinction between denotation, epistemic representation, and faithful epistemic representation. I then outline an interpretational account of epistemic representation, according to which a vehicle represents a target for a certain user if and only if the user adopts an interpretation of the vehicle in terms of the target, which would allow them to perform valid (but not necessarily sound) surrogative inferences from the model to the system. The main difference between the interpretational conception I (...)
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  14. P. Dicken (2011). Scientific Representation: Paradoxes of Perspective, by Bas C. Van Fraassen. Mind 120 (479):917-921.
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  15. R. W. Fischer (2011). Scientific Representation: Paradoxes of Perspective. By Bas C. Van Fraassen. Heythrop Journal 52 (2):301-301.
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  16. Steven French (2003). A Model-Theoretic Account of Representation (Or, I Don't Know Much About Art...But I Know It Involves Isomorphism). Philosophy of Science 70 (5):1472-1483.
    Recent discussions of the nature of representation in science have tended to import pre-established decompositions from analyses of representation in the arts, language, cognition and so forth. Which of these analyses one favours will depend on how one conceives of theories in the first place. If one thinks of them in terms of an axiomatised set of logico-linguistic statements, then one might be naturally drawn to accounts of linguistic representation in which notions of denotation, for example, feature prominently. If, on (...)
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  17. Steven French & Juha Saatsi (eds.) (2011). Continuum Companion to the Philosophy of Science. Continuum.
    A one volume reference guide To The latest research in Philosophy of Science, written by an international team of leading scholars in the field.
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  18. Steven French & Juha Saatsi (2006). Realism About Structure: The Semantic View and Nonlinguistic Representations. Philosophy of Science 73 (5):548-559.
    The central concern of this article is whether the semantic approach has the resources to appropriately capture the core tenets of structural realism. Chakravartty (2001) has argued that a realist notion of correspondence cannot be accommodated without introducing a linguistic component, which undermines the approach itself. We suggest that this worry can be addressed by an appropriate understanding of the role of language in this context. The real challenge, however, is how to incorporate the core notion of `explanatory approximate truth' (...)
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  19. Roman Frigg, Fiction and Scientific Representation.
    Scientific discourse is rife with passages that appear to be ordinary descriptions of systems of interest in a particular discipline. Equally, the pages of textbooks and journals are filled with discussions of the properties and the behavior of those systems. Students of mechanics investigate at length the dynamical properties of a system consisting of two or three spinning spheres with homogenous mass distributions gravitationally interacting only with each other. Population biologists study the evolution of one species procreating at a constant (...)
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  20. Joseph S. Fulda (1981). The Logistic Equation and Population Decline. Journal of Theoretical Biology 91 (2):255-259.
    A demonstration of two difficulties, both prevalent, in modeling. The first is scopal errors, which are often hard to detect because of their subtlety. The second is that two equations, though facially identical, are implicitly conjoined to /different/ inequalities, limiting the range of the variables or parameters in the equations, thereby changing the (here, ecological) interpretation of the equation, and thus its meaning, and therefore whether it is or is not an adequate model.
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  21. Carlo Gabbani & Marc Lange (2011). Bas van Fraassen's Scientific Representation. Iris. European Journal of Philosophy and Public Debate 2 (3):245-256.
  22. Alexander Gebharter (2014). A Formal Framework for Representing Mechanisms? Philosophy of Science 81 (1):138-153.
    In this article I tackle the question of how the hierarchical order of mechanisms can be represented within a causal graph framework. I illustrate an answer to this question proposed by Casini, Illari, Russo, and Williamson and provide an example that their formalism does not support two important features of nested mechanisms: (i) a mechanism’s submechanisms are typically causally interacting with other parts of said mechanism, and (ii) intervening in some of a mechanism’s parts should have some influence on the (...)
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  23. Alexander Gebharter & Marie I. Kaiser (2014). Causal Graphs and Biological Mechanisms. In Marie I. Kaiser, Oliver Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special sciences: The case of biology and history. Springer. 55-85.
    Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...)
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  24. Axel Gelfert (2011). Mathematical Formalisms in Scientific Practice: From Denotation to Model-Based Representation. Studies in History and Philosophy of Science 42 (2):272-286.
    The present paper argues that ‘mature mathematical formalisms’ play a central role in achieving representation via scientific models. A close discussion of two contemporary accounts of how mathematical models apply—the DDI account (according to which representation depends on the successful interplay of denotation, demonstration and interpretation) and the ‘matching model’ account—reveals shortcomings of each, which, it is argued, suggests that scientific representation may be ineliminably heterogeneous in character. In order to achieve a degree of unification that is compatible with successful (...)
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  25. Michel Ghins (2010). Bas Van Fraassen on Scientific Representation. Analysis 70 (3):524-536.
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  26. Ronald N. Giere (2009). Bas C. Van Fraassen: Scientific Representation: Paradoxes of Perspective,. [REVIEW] Philosophy of Science 76 (1).
  27. Ronald N. Giere (1994). No Representation Without Representation. Biology and Philosophy 9 (1):113-120.
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  28. Ronald N. Giere, Michael Lynch & Steve Woolgar (1994). Representation in Scientific Practice. Biology and Philosophy 9 (1):113-120.
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  29. Gary R. Gruber (1971). Quantization in Generalized Coordinates. Foundations of Physics 1 (3):227-234.
    The operator form of the generalized canonical momenta in quantum mechanics is derived by a new, instructive method and the uniqueness of the operator form is proven. If one wishes to find the correct representation of the generalized momentum operator, he finds the Hermitian part of the operator —iħ ∂/∂q, whereq q is the generalized coordinate. There are interesting philosophical implications involved in this: It is like saying that a physical structure is composed of two parts, one which is real (...)
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  30. Devin Henry (2012). A Sharp Eye for Kinds: Plato on Collection and Division. Oxford Studies in Ancient Philosophy 41 (January):229-55.
    This paper focuses on two methodological questions that arise from Plato’s account of collection and division. First, what place does the method of collection and division occupy in Plato’s account of philosophical inquiry? Second, do collection and division in fact constitute a formal “method” (as most scholars assume) or are they simply informal techniques that the philosopher has in her toolkit for accomplishing different philosophical tasks? I argue that Plato sees collection and division as useful tools for achieving two distinct (...)
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  31. Kate Hodesdon (2014). Mathematical Representation: Playing a Role. Philosophical Studies 168 (3):769-782.
    The primary justification for mathematical structuralism is its capacity to explain two observations about mathematical objects, typically natural numbers. Non-eliminative structuralism attributes these features to the particular ontology of mathematics. I argue that attributing the features to an ontology of structural objects conflicts with claims often made by structuralists to the effect that their structuralist theses are versions of Quine’s ontological relativity or Putnam’s internal realism. I describe and argue for an alternative explanation for these features which instead explains the (...)
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  32. F. Jenč (1979). The Conceptual Analysis (CA) Method in Theories of Microchannels: Application to Quantum Theory. Part III. Idealizations. Hilbert Space Representation. [REVIEW] Foundations of Physics 9 (11-12):897-928.
    We illustrate the application of the conceptual analysis (CA) method outlined in Part I by the example of quantum mechanics. In the present part the Hilbert space structure of conventional quantum mechanics is deduced as a consequence of postulates specifying further idealized concepts. A critical discussion of the idealizations of quantum mechanics is proposed. Quantum mechanics is characterized as a “statistically complete” theory and a simple and elegant formal recipe for the construction of the fundamental mathematical apparatus of quantum mechanics (...)
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  33. F. Jenč (1979). The Conceptual Analysis (CA) Method in Theories of Microchannels: Application to Quantum Theory. Part I. Fundamental Concepts. [REVIEW] Foundations of Physics 9 (7-8):589-608.
    A method is proposed that should facilitate the construction of theories of “submicroscopic particles” (denoted as “theories of microchannels”) in a way similar to the use of group-theoretical methods. The “conceptual analysis” (CA) method is based on the analysis of the basic concepts of a theory; it permits a determination of necessary conditions imposed on the mathematical apparatus (of the theory) which then appear as a mathematical representation of the structures obtained in a formal scheme of a theory. A pertinent (...)
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  34. Nicholaos Jones (forthcoming). Bowtie Structures, Pathway Diagrams, and Topological Explanation. Erkenntnis:1-21.
    While mechanistic explanation and, to a lesser extent, nomological explanation are well-explored topics in the philosophy of biology, topological explanation is not. Nor is the role of diagrams in topological explanations. These explanations do not appeal to the operation of mechanisms or laws, and extant accounts of the role of diagrams in biological science explain neither why scientists might prefer diagrammatic representations of topological information to sentential equivalents nor how such representations might facilitate important processes of explanatory reasoning unavailable to (...)
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  35. Tarja Knuuttila (2011). Modelling and Representing: An Artefactual Approach to Model-Based Representation. Studies in History and Philosophy of Science 42 (2):262-271.
    The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on the (...)
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  36. James Ladyman, Otávio Bueno, Mauricio Suárez & Bas van Fraassen (2011). Scientific Representation: A Long Journey From Pragmatics to Pragmatics. [REVIEW] Metascience 20 (3):417-442.
    Scientific representation: A long journey from pragmatics to pragmatics Content Type Journal Article DOI 10.1007/s11016-010-9465-5 Authors James Ladyman, Department of Philosophy, University of Bristol, 9 Woodland Rd, Bristol, BS8 1TB UK Otávio Bueno, Department of Philosophy, University of Miami, Coral Gables, FL 33124, USA Mauricio Suárez, Department of Logic and Philosophy of Science, Complutense University of Madrid, 28040 Madrid, Spain Bas C. van Fraassen, Philosophy Department, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA Journal Metascience Online (...)
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  37. T. Levin (ed.) (2008). Violence: Mercurial Gestalt.
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  38. Arnon Levy (forthcoming). Modeling Without Models. Philosophical Studies:1-18.
    Modeling is an important scientific practice, yet it raises significant philosophical puzzles. Models are typically idealized, and they are often explored via imaginative engagement and at a certain “distance” from empirical reality. These features raise questions such as what models are and how they relate to the world. Recent years have seen a growing discussion of these issues, including a number of views that treat modeling in terms of indirect representation and analysis. Indirect views treat the model as a bona (...)
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  39. M. Liston (2013). Christopher Pincock. Mathematics and Scientific Representation. Oxford University Press, 2012. ISBN 978-0-19-975710-7. Pp. Xv + 330. [REVIEW] Philosophia Mathematica 21 (3):371-385.
  40. Chuang Liu, Fictional Models in Science.
    In this paper, I begin with a discussion of Giere’s recent work arguing against taking models as works of fiction. I then move on to explore a spectrum of scientific models that goes from the obviously fictional to the not so obviously fictional. And then I discuss the modeling of the unobservable and make a case for the idea that despite difficulties of defining them, unobservable systems are modeled in a fundamentally different way than the observable systems. While idealization and (...)
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  41. Chuang LIu, Re-Inflating the Conception of Scientific Representation.
    This paper argues for an anti-deflationist view of scientific representation. Our discussion begins with an analysis of the recent Callender-Cohen deflationary view on scientific representation. We then argue that there are at least two radically different ways in which a thing can be used to represent: one is purely symbolic and therefore conventional, and the other is epistemic. The failure to recognize that scientific models are epistemic vehicles rather than symbolic ones has led to the mistaken (deflationary) view that whatever (...)
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  42. Chuang Liu, Symbols Versus Models.
    In this paper I argue against a deflationist view that as representational vehicles symbols and models do their jobs in essentially the same way. I argue that symbols are conventional vehicles whose chief function is denotation while models are epistemic vehicles whose chief function is showing what their targets are like in the relevant aspects. It is further pointed out that models usually do not rely on similarity or some such relations to relate to their targets. For that referential relation (...)
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  43. Sebastian Lutz, The Semantics of Scientific Theories.
    Marian Przełęcki’s semantics for the Received View is a good explication of Carnap’s position on the subject, anticipates many discussions and results from both proponents and opponents of the Received View, and can be the basis for a thriving research program.
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  44. Sebastian Lutz & Stephan Hartmann (2010). Conventional and Objective Invariance: Debs and Redhead on Symmetry. [REVIEW] Metascience 19:15-23.
    This review is a critical discussion of three main claims in Debs and Redhead’s thought-provoking book Objectivity, Invariance, and Convention. These claims are: (i) Social acts impinge upon formal aspects of scientific representation; (ii) symmetries introduce the need for conventional choice; (iii) perspectival symmetry is a necessary and sufficient condition for objectivity, while symmetry simpliciter fails to be necessary.
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  45. Michael Lynch (1991). Science in the Age of Mechanical Reproduction: Moral and Epistemic Relations Between Diagrams and Photographs. [REVIEW] Biology and Philosophy 6 (2):205-226.
    Sociologists, philosophers and historians of science are gradually recognizing the importance of visual representation. This is part of a more general movement away from a theory-centric view of science and towards an interest in practical aspects of observation and experimentation. Rather than treating science as a matter of demonstrating the logical connection between theoretical and empirical statements, an increasing number of investigations are examining how scientists compose and use diagrams, graphs, photographs, micrographs, maps, charts, and related visual displays. This paper (...)
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  46. Sam Mitchell (2010). Scientific Representation: Paradoxes of Perspective. By Bas C. Van Fraassen. Metaphilosophy 41 (5):717-722.
  47. Nicola Mößner (2012). Können Bilder Argumente Sein? In Manfred Harth & Jakob Steinbrenner (eds.), Bilder als Gründe. Herbert von Halem Verlag.
    In the following text we will consider the question whether pictures can be regarded as arguments or not. Obviously, classical philosophical approaches lead to a different opinion about this topic than our common sense intuitions, which are fostered by our usage of ordinary language. On the one hand, logicians defend the thesis that arguments are of a linguistic kind. On the other hand, there are everyday contexts where, nevertheless, it seems appropriate to ascribe pictures the status of arguments. It is (...)
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  48. F. A. Muller (2009). The Insidiously Enchanted Forrest. Essay Review of 'Scientific Representation' by Bas C. Van Fraassen. Studies in History and Philosophy of Science Part B 40 (3):268-272.
  49. Lydia Patton (2009). Signs, Toy Models, and the A Priori. Studies in History and Philosophy of Science 40 (3):281-289.
    The Marburg neo-Kantians argue that Hermann von Helmholtz's empiricist account of the a priori does not account for certain knowledge, since it is based on a psychological phenomenon, trust in the regularities of nature. They argue that Helmholtz's account raises the 'problem of validity' (Gueltigkeitsproblem): how to establish a warranted claim that observed regularities are based on actual relations. I reconstruct Heinrich Hertz's and Ludwig Wittgenstein's Bild theoretic answer to the problem of validity: that scientists and philosophers can depict the (...)
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  50. Isabelle Peschard (2011). Making Sense of Modeling: Beyond Representation. [REVIEW] European Journal for Philosophy of Science 1 (3):335-352.
    Making sense of modeling: beyond representation Content Type Journal Article Category Original paper in Philosophy of Science Pages 335-352 DOI 10.1007/s13194-011-0032-8 Authors Isabelle Peschard, Philosophy Department, San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132, USA Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912 Journal Volume Volume 1 Journal Issue Volume 1, Number 3.
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