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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. Mark Balaguer, Elaine Landry, Sorin Bangu & Christopher Pincock (2013). Structures, Fictions, and the Explanatory Epistemology of Mathematics in Science. Metascience 22 (2):247-273.
  3. Jeffrey A. Barrett (2009). Review of Bas C. Van Fraassen: Scientific Representation: Paradoxes of Perspective. [REVIEW] Journal of Philosophy 106 (11).
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  4. 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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  5. 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 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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  6. 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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  7. Gordon Belot (2010). Transcendental Idealism Among the Jersey Metaphysicians. Philosophical Studies 150 (3):429 - 438.
    Some questions are posed for van Fraassen, concerning the role and status of metaphysics in his Scientific Representation.
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  8. Thomas Bittner & Barry Smith (2003). A Theory of Granular Partitions. In Foundations of Geographic Information Science. Taylor & Francis
    We have a variety of different ways of dividing up, classifying, mapping, sorting and listing the objects in reality. The theory of granular partitions presented here seeks to provide a general and unified basis for understanding such phenomena in formal terms that is more realistic than existing alternatives. Our theory has two orthogonal parts: the first is a theory of classification; it provides an account of partitions as cells and subcells; the second is a theory of reference or intentionality; it (...)
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  9. Brandon Boesch (2015). Representation, Scientific. Internet Encyclopedia of Philosophy.
    The article constitutes a detailed overview of the most important background literature on the topic of scientific representation. It gives a detailed outline of many of the important philosophical accounts of scientific representation. The primary division is between substantive accounts and deflationary/pragmatic accounts. Objections to each type of account are considered. Insights from the literature on modelling are discussed along with an overview of some of the insights from the sociology of science.
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  10. 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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  11. 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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  12. 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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  13. 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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  14. Otávio Bueno (2006). Representation at the Nanoscale. Philosophy of Science 73 (5):617-628.
    In this paper, I provide an account of scientific representation that makes sense of the notion both at the nanoscale and at the quantum level: the partial mappings account. The account offers an extension of a proposal developed by R. I. G. Hughes in terms of denotation, demonstration, and interpretation (DDI). I first argue that the DDI account needs some amendments to accommodate representation of nano and quantum phenomena. I then introduce a generalized framework with the notions of unsharp denotation, (...)
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  15. 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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  16. Charles Evan Cardwell (1972). Representation and Uncertainty: An Essay on Pierre Duhem's Philosophy of Science. Dissertation, The University of Rochester
  17. Roberto Casati & Achille C. Varzi (2002). Un Altro Mondo? Rivista di Estetica 19 (1):131-159.
    Alexandre Koyré wrote that Newton and the science that followed led to a splitting of the world: on the one hand is the “world of qualities and of sensible perceptions”, on the other is the “world of quantities and of reified geometry”. A comparison between facts held true by common sense and false by the scientific image of the world (or vice versa) seems to confirm this view. But is the dichotomy a real one? Is the world of common sense (...)
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  18. Anjan Chakravartty (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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  19. 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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  20. Cohen Jonathan & Craig Callender (2006). There is No Special Problem About Scientific Representation. Theoria 55 (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 the scien- tific special case. Construing scientific representation in this way makes the so-called “problem of scientific representation” look much less inter- esting than it has seemed to many, and suggests that some of the (hotly contested) debates in the literature are concerned with non-issues.
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  21. 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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  22. 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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  23. Gabriele Contessa (2013). Models and Maps: An Essay on Epistemic Representation. Carleton University.
    This book defends a two-tiered account of epistemic representation--the sort of representation relation that holds between representations such as maps and scientific models and their targets. It defends a interpretational account of epistemic representation and a structural similarity account of overall faithful epistemic representation.
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  24. 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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  25. 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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  26. 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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  27. P. Dicken (2011). Scientific Representation: Paradoxes of Perspective, by Bas C. Van Fraassen. Mind 120 (479):917-921.
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  28. Thomas J. Dohmen (2002). Building and Using Economic Models: A Case Study Analysis of the IS-LL Model. Journal of Economic Methodology 9 (2):191-212.
    This paper critically assesses several model accounts written in the 1990s by epistemologists and philosophers of science by relating them to a specific but crucial example of model building, namely Hicks's (1937) construction of the first version of the IS-LM model, and examining in how far these accounts apply to this case. Thereby the paper contributes to answering why and how economists build models. The view crystallizes that economists build models not only to facilitate the conceptual exploration of theory, but (...)
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  29. Carol Donnell-Kotrozo (1982). Representation as Denotation. Journal of Aesthetics and Art Criticism 40 (4):361-368.
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  30. Juan M. Durán (2008). Diagramas Hertzianos: lecturas de una interpretación. In Leticia Minhot & Leon Olivé (eds.), Representación en la ciencia y en el arte.
  31. Juan M. Durán (2007). Reinterpretando a Hertz. Algunas consideraciones en torno a una lectura de Hertz. Lektón. Revista de Filosofía 1 (1):61-73.
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  32. R. W. Fischer (2011). Scientific Representation: Paradoxes of Perspective. By Bas C. Van Fraassen. Heythrop Journal 52 (2):301-301.
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  33. Curtis Forbes (2009). REVIEW: Bas van Fraassen, Scientific Representation: Paradoxes of Perspective. [REVIEW] Spontaneous Generations 3 (1):236-238.
  34. Fraassen Bas C. Van (2010). Precis of Scientific Representation: Paradoxes of Perspective. [REVIEW] Philosophical Studies 150 (3):425 - 428.
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  35. 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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  36. 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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  37. 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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  38. Roman Frigg & James Nguyen (2016). Models and Representation. In .
    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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  39. 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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  40. Carlo Gabbani & Marc Lange (2011). Bas van Fraassen's Scientific Representation. Iris. European Journal of Philosophy and Public Debate 2 (3):245-256.
  41. Carlo Gabbani & Marc Lange (2009). Scientific Representation: Paradoxes of Perspective di Bas van Fraassen. [REVIEW] Iride: Filosofia e Discussione Pubblica 22 (3):707-720.
  42. Sergio A. Gallegos (2016). The Explanatory Role of Abstraction Processes in Models: The Case of Aggregations. Studies in History and Philosophy of Science Part A 56:161-167.
    Though it is held that some models in science have explanatory value, there is no conclusive agreement on what provides them with this value. One common view is that models have explanatory value vis-à-vis some target systems because they are developed using an abstraction process. Though I think this is correct, I believe it is not the whole picture. In this paper, I argue that, in addition to the well-known process of abstraction understood as an omission of features or information, (...)
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  43. Lucia Galovičová (2011). Fraassen, B. C. van: Scientific Representation: Paradoxes of Perspective. [REVIEW] Filozofia 66:386-390.
  44. 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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  45. 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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  46. 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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  47. Michel Ghins (2010). Bas Van Fraassen on Scientific Representation. Analysis 70 (3):524-536.
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  48. Ronald N. Giere (2009). Bas C. Van Fraassen: Scientific Representation: Paradoxes of Perspective,. [REVIEW] Philosophy of Science 76 (1).
  49. Ronald N. Giere (1994). No Representation Without Representation. Biology and Philosophy 9 (1):113-120.
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  50. Ronald N. Giere, Michael Lynch & Steve Woolgar (1994). Representation in Scientific Practice. Biology and Philosophy 9 (1):113-120.
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