Scientific Models

Edited by Angela Potochnik (University of Cincinnati)
Assistant editor: Guilherme Sanches De Oliveira (University of Cincinnati)
About this topic
Summary Modeling is an increasingly important method in many fields of science. Scientific models are taken to be only partially similar to the phenomena they are used to study. Several philosophical questions result. For one, philosophers investigate how it is that models represent phenomena despite their differences, and what is responsible for models' epistemic success. This dovetails with questions about the nature of the representation relation. Philosophers also investigate abstraction and idealization in modeling, and some accord a further role to fictions. Finally, models are also significant in a different sense for the semantic view of theories. 
Key works Hesse 1963; Van Fraassen Bas 1980; Wimsatt (1987); Giere 1988; Morgan & Morrison 1999; Bailer-Jones 2009; Weisberg 2013.
Introductions Frigg and Hartmann (2006)
Related categories

567 found
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The Nature of Models
  1. Remarks on Scientific Metaphors.Sergio Volodia Marcello Cremaschi - 1988 - In Maria Luisa Dalla Chiara & Maria Clara Galavotti (eds.), Temi e prospettive della Logica e Filosofia della Scienza. Volume 2. Bologna, Italy: CLUEB. pp. 114-116.
    Recent contributions by Kuhn, Wartofsky, and Granger, converge in the direction of an extended view of models, one that acknowledges a metaphorical dimension in the language of science. Such a view is in some respects the opposite of the views of both Bachelard and the Logical Empiricists. A number of familiar puzzles of the philosophy of science, such as the problem of reference, the opposition of realism and instrumentalism, that between explanation and understanding, and the status of scientific objectivity, may (...)
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  2. Reasoning About Uncertain Conditionals.Niki Pfeifer - 2014 - Studia Logica 102 (4):849-866.
    There is a long tradition in formal epistemology and in the psychology of reasoning to investigate indicative conditionals. In psychology, the propositional calculus was taken for granted to be the normative standard of reference. Experimental tasks, evaluation of the participants’ responses and psychological model building, were inspired by the semantics of the material conditional. Recent empirical work on indicative conditionals focuses on uncertainty. Consequently, the normative standard of reference has changed. I argue why neither logic nor standard probability theory provide (...)
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  3. The Epistemic Virtue of Robustness in Climate Modeling (MA Dissertation).Parjanya Joshi - 2019 - Dissertation, Tata Institute of Social Sciences
    The aim of this dissertation is to comprehensively study various robustness arguments proposed in the literature from Levins to Lloyd as well as the opposition offered to them and pose enquiry into the degree of epistemic virtue that they provide to the model prediction results with respect to climate science and modeling. Another critical issue that this dissertation strives to examine is that of the actual epistemic notion that is operational when scientists and philosophers appeal to robustness. In attempting to (...)
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  4. Learning About Reality Through Models and Computer Simulations.Melissa Jacquart - 2018 - Science & Education 27 (7-8):805-810.
    Margaret Morrison, (2015) Reconstructing Reality: Models, Mathematics, and Simulations. Oxford University Press, New York. -/- Scientific models, mathematical equations, and computer simulations are indispensable to scientific practice. Through the use of models, scientists are able to effectively learn about how the world works, and to discover new information. However, there is a challenge in understanding how scientists can generate knowledge from their use, stemming from the fact that models and computer simulations are necessarily incomplete representations, and partial descriptions, of their (...)
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  5. Modelling with Words: Narrative and Natural Selection.Dominic K. Dimech - 2017 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 62:20-24.
    I argue that verbal models should be included in a philosophical account of the scientific practice of modelling. Weisberg (2013) has directly opposed this thesis on the grounds that verbal structures, if they are used in science, only merely describe models. I look at examples from Darwin's On the Origin of Species (1859) of verbally constructed narratives that I claim model the general phenomenon of evolution by natural selection. In each of the cases I look at, a particular scenario is (...)
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  6. Les notions de métaphore et d'analogie dans les épistémologies des modèles et des simulations.Franck Varenne - 2006 - Paris, France: Editions Petra.
    Le développement considérable des simulations informatiques dans les sciences contemporaines impose une remise à plat des épistémologies des modèles. Franck Varenne propose de revenir sur les limites des notions de métaphore et d'analogie pour penser les modèles, en particulier quand il s'agit des modèles composés, des pluri-modèles et des modèles de simulation (à objets ou à agents), tels qu'ils se développent depuis une dizaine d'années. Il suggère que le paradigme linguistique, à l'oeuvre aussi bien dans la pensée analytique anglo-saxonne que (...)
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  7. Formaliser le vivant : lois, théories, modèles.Franck Varenne - 2010 - Paris, France: Hermann.
    Peut-on formaliser le vivant ? Peut-on réduire une plante à une simple formule mathématique ? Goethe ne l’aurait pas admis. Pour beaucoup encore, cette question ne se pose même pas tant elle peut sembler provocante et contre-nature. Dans une perspective à la fois historique et épistémologique, ce livre rend compte de travaux contemporains qui ont pourtant tous tenté de braver cet interdit. -/- C’est en grande partie sur ce terrain, hautement problématique, que, dans les premières décennies du XXe siècle, on (...)
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  8. Théorie, Réalité, Modèle.Franck Varenne - 2012 - Paris, France: Editions Matériologiques.
    Dans cet ouvrage, Franck Varenne pose la question du réalisme scientifique, essentiellement dans sa forme contemporaine, et ce jusqu’aux années 1980. Il s’est donné pour cela la contrainte de focaliser l’attention sur ce que devenaient sa formulation et les réponses diverses qu’on a pu lui apporter en réaction spécifique à l’évolution parallèle qu’ont subie les notions de théories et surtout de modèles dans les sciences, à la même époque. Même si, bien sûr, on ne peut pas attribuer le considérable essor (...)
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  9. Théories et modèles en sciences humaines. Le cas de la géographie.Franck Varenne - 2017 - Paris, France: Editions Matériologiques.
    Face à la diversité et à la complexification des modes de formalisation, une épistémologie des méthodes scientifiques doit confronter directement ses analyses à une pluralité d’études de cas comparatives. C’est l’objectif de cet ouvrage. -/- Aussi, dans une première partie, propose-t-il d’abord une classification large et raisonnée des différentes fonctions de connaissance des théories, des modèles et des simulations (de fait, cette partie constitue un panorama d’épistémologie générale particulièrement poussé). C’est ensuite à la lumière de cette classification que les deux (...)
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  10. From Models to Simulations.Franck Varenne - 2018 - London, UK: Routledge.
    This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. -/- Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how (...)
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  11. The Exploratory Role of Idealizations and Limiting Cases in Models.Elay Shech & Axel Gelfert - forthcoming - Studia Metodologiczne.
    In this article we argue that idealizations and limiting cases in models play an exploratory role in science. Four senses of exploration are presented: exploration of the structure and representational capacities of theory; proof-of-principle demonstrations; potential explanations; and exploring the suitability of target systems. We illustrate our claims through three case studies, including the Aharonov-Bohm effect, the emergence of anyons and fractional quantum statistics, and the Hubbard model of the Mott phase transitions. We end by reflecting on how our case (...)
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  12. Semantics as Model-Based Science.Seth Yalcin - 2018 - In Derek Ball & Brian Rabern (eds.), The Science of Meaning: Essays on the Metatheory of Natural Language Semantics. Oxford University Press. pp. 334-360.
    This paper critiques a number of standard ways of understanding the role of the metalanguage in a semantic theory for natural language, including the idea that disquotation plays a nontrivial role in any explanatory natural language semantics. It then proposes that the best way to understand the role of a semantic metalanguage involves recognizing that semantics is a model-based science. The metalanguage of semantics is language for articulating features of the theorist's model. Models are understood as mediating instruments---idealized structures used (...)
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  13. Getting Serious About Shared Features.Donal Khosrowi - forthcoming - British Journal for the Philosophy of Science.
    In Simulation and Similarity, Michael Weisberg offers a similarity-based account of the model-world relation, that is, the relation in virtue of which successful models are successful. Weisberg’s main idea is that models are similar to targets in virtue of sharing features. An important concern about Weisberg’s account is that it remains silent on what it means for models and targets to share features, and consequently on how feature-sharing contributes to models’ epistemic success. I consider three potential ways of concretizing the (...)
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  14. Successful Visual Epistemic Representation.Agnes Bolinska - 2016 - Studies in History and Philosophy of Science Part A 56:153-160.
    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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  15. Intuition and Awareness of Abstract Models: A Challenge for Realists.Dimitris Kilakos - 2018 - Philosophies 3 (1):3-0.
    It is plausible to think that, in order to actively employ models in their inquiries, scientists should be aware of their existence. The question is especially puzzling for realists in the case of abstract models, since it is not obvious how this is possible. Interestingly, though, this question has drawn little attention in the relevant literature. Perhaps the most obvious choice for a realist is appealing to intuition. In this paper, I argue that if scientific models were abstract entities, one (...)
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  16. Models as Signs: Extending Kralemann and Lattman’s Proposal on Modeling Models Within Peirce’s Theory of Signs.Sergio A. Gallegos - forthcoming - Synthese.
    In recent decades, philosophers of science have devoted considerable efforts to understand what models represent. One popular position is that models represent fictional situations. Another position states that, though models often involve fictional elements, they represent real objects or scenarios. Though these two positions may seem to be incompatible, I believe it is possible to reconcile them. Using a threefold distinction between different signs proposed by Peirce, I develop an argument based on a proposal recently made by Kralemann and Lattman (...)
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  17. The Topological Realization.Daniel Kostić - 2018 - Synthese (1).
    In this paper, I argue that the newly developed network approach in neuroscience and biology provides a basis for formulating a unique type of realization, which I call topological realization. Some of its features and its relation to one of the dominant paradigms of realization and explanation in sciences, i.e. the mechanistic one, are already being discussed in the literature. But the detailed features of topological realization, its explanatory power and its relation to another prominent view of realization, namely the (...)
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  18. Philosophy of Modeling: Neglected Pages of History.Karlis Podnieks - 2018 - Baltic Journal of Modern Computing 6 (3):279–303.
    The work done in the philosophy of modeling by Vaihinger (1876), Craik (1943), Rosenblueth and Wiener (1945), Apostel (1960), Minsky (1965), Klaus (1966) and Stachowiak (1973) is still almost completely neglected in the mainstream literature. However, this work seems to contain original ideas worth to be discussed. For example, the idea that diverse functions of models can be better structured as follows: in fact, models perform only a single function – they are replacing their target systems, but for different purposes. (...)
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  19. Theories and Their Models.D. A. Anapolitanos - 1989 - 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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  20. James and Dewey on Abstraction. Winther - 2014 - The Pluralist 9 (2):1.
    Reification is to abstraction as disease is to health. Whereas abstraction is singling out, symbolizing, and systematizing, reification is neglecting abstractive context, especially functional, historical, and analytical-level context. William James and John Dewey provide similar and nuanced arguments regarding the perils and promises of abstraction. They share an abstraction-reification account. The stages of abstraction and the concepts of “vicious abstractionism,” “/the/ psychologist’s fallacy,” and “the philosophic fallacy” in the works of these pragmatists are here analyzed in detail. For instance, in (...)
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  21. What Was the Syntax‐Semantics Debate in the Philosophy of Science About?Sebastian Lutz - 2017 - Philosophy and Phenomenological Research 95 (2):319-352.
    The debate between critics of syntactic and semantic approaches to the formalization of scientific theories has been going on for over 50 years. I structure the debate in light of a recent exchange between Hans Halvorson, Clark Glymour, and Bas van Fraassen and argue that the only remaining disagreement concerns the alleged difference in the dependence of syntactic and semantic approaches on languages of predicate logic. This difference turns out to be illusory.
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  22. Similarity, Adequacy, and Purpose: Understanding the Success of Scientific Models.Melissa Jacquart - 2016 - Dissertation, University of Western Ontario
    A central component to scientific practice is the construction and use of scientific models. Scientists believe that the success of a model justifies making claims that go beyond the model itself. However, philosophical analysis of models suggests that drawing inferences about the world from successful models is more complex. In this dissertation I develop a framework that can help disentangle the related strands of evaluation of model success, model extendibility, and the ability to draw ampliative inferences about the world from (...)
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  23. Essay Review: Models and Exploratory Models.Fiora Salis - 2017 - Studies in History and Philosophy of Science Part A 63:58-61.
    Review essay of How to do science with models. A philosophical primer. Springer briefs in philosophy, Axel Gelfert., 129, Price € 49,99 softcover, ISBN: 978-3-319-27954-1.
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  24. Model-Based Theorizing in Cognitive Neuroscience.Elizabeth Irvine - 2016 - British Journal for the Philosophy of Science 67 (1):143-168.
    Weisberg and Godfrey-Smith distinguish between two forms of theorising: data-driven ‘abstract direct representation’ and modeling. The key difference is that when using a data-driven approach, theories are intended to represent specific phenomena, so directly represent them, while models may not be intended to represent anything, so represent targets indirectly, if at all. The aim here is to compare and analyse these practices, in order to outline an account of model-based theorising that involves direct representational relationships. This is based on the (...)
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  25. Measurement in Science.Eran Tal - 2015 - Stanford Encyclopedia of Philosophy.
  26. The Savings Problem in the Original Position: Assessing and Revising a Model.Eric Brandstedt - 2017 - Canadian Journal of Philosophy 47 (2):269-289.
    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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  27. Against the New Fictionalism: A Hybrid View of Scientific Models.Chuang Liu - 2016 - International Studies in the Philosophy of Science 30 (1):39-54.
    This article develops an approach to modelling and models in science—the hybrid view—that is against model fictionalism of a recent stripe. It further argues that there is a version of fictionalism about models to which my approach is neutral and which makes sense only if one adopts a special sort of antirealism. Otherwise, my approach strongly suggests that one stay away from fictionalism and embrace realism directly.
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  28. The Epigenetic Landscape in the Course of Time: Conrad Hal Waddington’s Methodological Impact on the Life Sciences.Jan Baedke - 2013 - 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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  29. Credibility, Idealisation, and Model Building: An Inferential Approach.Xavier Donato Rodríguez & Jesús Zamora Bonilla - 2009 - Erkenntnis 70 (1):101-118.
    In this article we defend the inferential view of scientific models and idealisation. Models are seen as "inferential prostheses" construed by means of an idealisation-concretisation process, which we essentially understand as a kind of counterfactual deformation procedure . The value of scientific representation is understood in terms not only of the success of the inferential outcomes arrived at with its help, but also of the heuristic power of representation and their capacity to correct and improve our models. This provides us (...)
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  30. Scientific Models in Philosophy of Science.Daniela M. Bailer-Jones - 2009 - 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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  31. The Crucial Role of Models in Science.Sabina Leonelli - 2017 - Metascience 26 (1):99-101.
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  32. Theories, Models And Constraints.Friedel Weinert - 1999 - Studies in History and Philosophy of Science Part A 30 (2):303-333.
  33. Models and Perspectives on Stage: Remarks on Giere’s Scientific Perspectivism.Matthew J. Brown - 2009 - 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 similarity to earlier ideas of Paul (...)
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  34. Models and the Mosaic of Scientific Knowledge. The Case of Immunology.Tudor M. Baetu - 2014 - 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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  35. Scientific Representation: Paradoxes of Perspective. [REVIEW]Jeffrey A. Barrett - 2009 - Journal of Philosophy 106 (11):634-639.
  36. Making a Useful 'Model' for Managers: A Projective Constructivist Account.Derek M. Eriksson - 2004 - Philosophy of Management 4 (2):19-34.
    Models and modelling are central not only to management but to all human affairs. Models provide grounds for decision making and action-taking. This paper investigates the concept of model', showing that the conventional notion of a model as understood in management science, a notion founded on positivism and realism, is insufficient for the complex practical needs of management models. To remedy this situation, an alternative notion founded on Projective Constructivist Epistemology is proposed. Some of the implications of the new notion (...)
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  37. There Is No Special Problem About Scientific Representation.Craig Callender & Jonathan Cohen - 2006 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 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 debates in the literature are concerned with non-issues.
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  38. Imagination in Scientific Modeling.Adam Toon - 2016 - In Amy Kind (ed.), The Routledge Handbook of Philosophy of Imagination. Routledge. pp. 451-462.
    Modeling is central to scientific inquiry. It also depends heavily upon the imagination. In modeling, scientists seem to turn their attention away from the complexity of the real world to imagine a realm of perfect spheres, frictionless planes and perfect rational agents. Modeling poses many questions. What are models? How do they relate to the real world? Recently, a number of philosophers have addressed these questions by focusing on the role of the imagination in modeling. Some have also drawn parallels (...)
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  39. Out of the Fog: Catalyzing Integrative Capacity in Interdisciplinary Research.Zachary Piso, Michael O'Rourke & Kathleen C. Weathers - 2016 - Studies in History and Philosophy of Science Part A 56:84-94.
    Social studies of interdisciplinary science investigate how scientific collaborations approach complex challenges that require multiple disciplinary perspectives. In order for collaborators to meet these complex challenges, interdisciplinary collaborations must develop and maintain integrative capacity, understood as the ability to anticipate and weigh tradeoffs in the employment of different disciplinary approaches. Here we provide an account of how one group of interdisciplinary fog scientists intentionally catalyzed integrative capacity. Through conversation, collaborators negotiated their commitments regarding the ontology of fog systems and the (...)
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  40. Scientific Representation.Roman Frigg & James Nguyen - 2016 - Stanford Encyclopedia of Philosophy.
    Science provides us with representations of atoms, elementary particles, polymers, populations, genetic trees, economies, rational decisions, aeroplanes, earthquakes, forest fires, irrigation systems, and the world’s climate. It's through these representations that we learn about the world. This entry explores various different accounts of scientific representation, with a particular focus on how scientific models represent their target systems. As philosophers of science are increasingly acknowledging the importance, if not the primacy, of scientific models as representational units of science, it's important to (...)
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  41. Modal Science.Timothy Williamson - 2016 - Canadian Journal of Philosophy 46 (4-5):453-492.
    This paper explains and defends the idea that metaphysical necessity is the strongest kind of objective necessity. Plausible closure conditions on the family of objective modalities are shown to entail that the logic of metaphysical necessity is S5. Evidence is provided that some objective modalities are studied in the natural sciences. In particular, the modal assumptions implicit in physical applications of dynamical systems theory are made explicit by using such systems to define models of a modal temporal logic. Those assumptions (...)
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  42. Models as Mediators: Perspectives on Natural and Social Science.Mary S. Morgan & Margaret Morrison (eds.) - 1999 - Cambridge University Press.
    Models as Mediators discusses the ways in which models function in modern science, particularly in the fields of physics and economics. Models play a variety of roles in the sciences: they are used in the development, exploration and application of theories and in measurement methods. They also provide instruments for using scientific concepts and principles to intervene in the world. The editors provide a framework which covers the construction and function of scientific models, and explore the ways in which they (...)
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  43. Reply to Sider.Timothy Williamson - 2016 - Canadian Journal of Philosophy 46 (4-5):699-708.
  44. Model Building in Economics: Its Purposes and Limitations.Lawrence A. Boland - 2014 - 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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  45. The Fiction View of Models Reloaded.Roman Frigg & James Nguyen - 2016 - The Monist 99 (3):225-242.
    In this paper we explore the constraints that our preferred account of scientific representation places on the ontology of scientific models. Pace the Direct Representation view associated with Arnon Levy and Adam Toon we argue that scientific models should be thought of as imagined systems, and clarify the relationship between imagination and representation.
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  46. Models as Mediating Instruments.Margaret Morrison & Mary S. Morgan - 1999 - In Mary S. Morgan & Margaret Morrison (eds.), Models as Mediators: Perspectives on Natural and Social Science. Cambridge University Press.
    Morrison and Morgan argue for a view of models as 'mediating instruments' whose role in scientific theorising goes beyond applying theory. Models are partially independent of both theories and the world. This autonomy allows for a unified account of their role as instruments that allow for exploration of both theories and the world.
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  47. Models, Fiction and Fictional Models.Liu Chuang - unknown
    The use of models to scientifically represent and study reality is widely recognized with good reasons as indispensable for the practice of science. Because models, unlikely pure verbal representation, are justifiably regarded as vehicles of representation that are not truth-apt, philosophical questions are natural raised concerning the nature of such vehicles and how they represent. A sizeable literature generated in recent years explores the possibility that ''scientific models are works of fiction''. Idealization and other similar strategies are often taken to (...)
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  48. Models: From Exploration to Prediction: Bad Reputation of Modeling in Some Disciplines Results From Nebulous Goals.Peter Schuster - 2016 - Complexity 21 (1):6-9.
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  49. Programming the Emergence in Morphogenetically Architected Complex Systems.Franck Varenne, Pierre Chaigneau, Jean Petitot & René Doursat - 2015 - Acta Biotheoretica 63 (3):295-308.
    Large sets of elements interacting locally and producing specific architectures reliably form a category that transcends the usual dividing line between biological and engineered systems. We propose to call them morphogenetically architected complex systems (MACS). While taking the emergence of properties seriously, the notion of MACS enables at the same time the design (or “meta-design”) of operational means that allow controlling and even, paradoxically, programming this emergence. To demonstrate our claim, we first show that among all the self-organized systems studied (...)
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  50. Angela N. H. Creager, Elizabeth Lunbeck and M. Norton Wise , Science Without Laws: Model Systems, Cases, Exemplary Narratives. Durham, NC and London: Duke University Press, 2007. ISBN 978-0-8223-4068-3. £12.99. [REVIEW]Jacob Stegenga - 2009 - British Journal for the History of Science 42 (4):626.
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