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Models and fiction

Synthese 172 (2):251-268 (2010)

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  1. What is a Target System?Alkistis Elliott-Graves - 2020 - Biology and Philosophy 35 (2):1-22.
    Many phenomena in the natural world are complex, so scientists study them through simplified and idealised models. Philosophers of science have sought to explain how these models relate to the world. On most accounts, models do not represent the world directly, but through target systems. However, our knowledge of target systems is incomplete. First, what is the process by which target systems come about? Second, what types of entity are they? I argue that the basic conception of target systems, on (...)
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  • Formal Semantics and Applied Mathematics: An Inferential Account.Ryan M. Nefdt - 2020 - Journal of Logic, Language and Information 29 (2):221-253.
    In this paper, I utilise the growing literature on scientific modelling to investigate the nature of formal semantics from the perspective of the philosophy of science. Specifically, I incorporate the inferential framework proposed by Bueno and Colyvan : 345–374, 2011) in the philosophy of applied mathematics to offer an account of how formal semantics explains and models its data. This view produces a picture of formal semantic models as involving an embedded process of inference and representation applying indirectly to linguistic (...)
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  • Introduction: The Plurality of Modeling.Huneman Philippe & Lemoine Maël - 2014 - History and Philosophy of the Life Sciences 36 (1):5-15.
    Philosophers of science have recently focused on the scientific activity of modeling phenomena, and explicated several of its properties, as well as the activities embedded into it. A first approach to modeling has been elaborated in terms of representing a target system: yet other epistemic functions, such as producing data or detecting phenomena, are at least as relevant. Additional useful distinctions have emerged, such as the one between phenomenological and mechanistic models. In biological sciences, besides mathematical models, models now come (...)
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  • What Good Are Abstract and What-If Models? Lessons From the Gaïa Hypothesis.Sébastien Dutreuil - 2014 - History and Philosophy of the Life Sciences 36 (1):16-41.
    This article on the epistemology of computational models stems from an analysis of the Gaia hypothesis (GH). It begins with James Kirchner’s criticisms of the central computational model of GH: Daisyworld. Among other things, the model has been criticized for being too abstract, describing fictional entities (fictive daisies on an imaginary planet) and trying to answer counterfactual (what-if) questions (how would a planet look like if life had no influence on it?). For these reasons the model has been considered not (...)
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  • Models as Relational Categories.Tommi Kokkonen - 2017 - Science & Education 26 (7-9):777-798.
    Model-based learning has an established position within science education. It has been found to enhance conceptual understanding and provide a way for engaging students in authentic scientific activity. Despite ample research, few studies have examined the cognitive processes regarding learning scientific concepts within MBL. On the other hand, recent research within cognitive science has examined the learning of so-called relational categories. Relational categories are categories whose membership is determined on the basis of the common relational structure. In this theoretical paper, (...)
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  • Imagination in Scientific Practice.Steven French - 2020 - European Journal for Philosophy of Science 10 (3):1-19.
    What is the role of the imagination in scientific practice? Here I focus on the nature and role of invitations to imagine in certain scientific texts as represented by the example of Einstein’s Special Relativity paper from 1905. Drawing on related discussions in aesthetics, I argue, on the one hand, that this role cannot be simply subsumed under ‘supposition’ but that, on the other, concerns about the impact of genre and symbolism can be dealt with, and hence present no obstacle (...)
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  • Don’T Blame the Idealizations.Nicholaos Jones - 2013 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 44 (1):85-100.
    Idealizing conditions are scapegoats for scientific hypotheses, too often blamed for falsehood better attributed to less obvious sources. But while the tendency to blame idealizations is common among both philosophers of science and scientists themselves, the blame is misplaced. Attention to the nature of idealizing conditions, the content of idealized hypotheses, and scientists’ attitudes toward those hypotheses shows that idealizing conditions are blameless when hypotheses misrepresent. These conditions help to determine the content of idealized hypotheses, and they do so in (...)
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  • The Nature of Model-World Comparisons.Fiora Salis - 2016 - The Monist 99 (3):243-259.
    Upholders of fictionalism about scientific models have not yet successfully explained how scientists can learn about the real world by making comparisons between models and the real phenomena they stand for. In this paper I develop an account of model-world comparisons in terms of what I take to be the best antirealist analyses of comparative claims that emerge from the current debate on fiction.
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  • From Models-as-Fictions to Models-as-Tools.Adrian Currie - 2017 - Ergo: An Open Access Journal of Philosophy 4.
    Many accounts of scientific modeling conceive of models as fictions: scientists interact with models in ways analogous to various aesthetic objects. Fictionalists follow most other accounts of modeling by taking them to be revelatory of the actual world in virtue of bearing some resemblance relation to a target system. While such fictionalist accounts capture crucial aspects of modelling practice, they are ill-suited to some design and engineering contexts. Here, models sometimes serve to underwrite design projects whereby real-world targets are constructed. (...)
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  • Reconstructing Probabilistic Realism: Re-enacting Syntactical Structures.Majid Davoody Beni - 2020 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 51 (2):293-313.
    Probabilistic realism and syntactical positivism were two among outdated theories that Feigl criticised on account of their semantical poverty. In this paper, I argue that a refined version of probabilistic realism, which relies on what Feigl specified as the pragmatic description of the symbolic behaviour of scientists’ estimations and foresight, is defendable. This version of statistical realism does not need to make the plausibility of realist thesis dependent on the conventional acceptance of a constructed semantic metalanguage. I shall rely on (...)
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  • Models as Make-Believe.Adam Toon - 2010 - In Roman Frigg & Matthew Hunter (eds.), Beyond Mimesis and Convention: Representation in Art and Science. Boston Studies in Philosophy of Science.
    In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I demonstrate (...)
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  • It’s Not a Game: Accurate Representation with Toy Models.James Nguyen - 2020 - British Journal for the Philosophy of Science 71 (3):1013-1041.
    Drawing on ‘interpretational’ accounts of scientific representation, I argue that the use of so-called ‘toy models’ provides no particular philosophical puzzle. More specifically; I argue that once one gives up the idea that models are accurate representations of their targets only if they are appropriately similar, then simple and highly idealized models can be accurate in the same way that more complex models can be. Their differences turn on trading precision for generality, but, if they are appropriately interpreted, toy models (...)
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  • Models and Representation.Roman Frigg & James Nguyen - 2017 - In Lorenzo Magnani & Tommaso Bertolotti (eds.), Springer Handbook of Model-Based Science. pp. 49-102.
    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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  • Deflationary Realism: Representation and Idealisation in Cognitive Science.Dimitri Coelho Mollo - forthcoming - Mind and Language.
    Debate on the nature of representation in cognitive systems tends to oscillate between robustly realist views and various anti-realist options. I defend an alternative view, deflationary realism, which sees cognitive representation as an offshoot of the extended application to cognitive systems of an explanatory model whose primary domain is public representation use. This extended application, justified by a common explanatory target, embodies idealisations, partial mismatches between model and reality. By seeing representation as part of an idealised model, deflationary realism avoids (...)
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  • Representational Indispensability and Ontological Commitment.John Heron - 2020 - Thought: A Journal of Philosophy 9 (2):105-114.
    Thought: A Journal of Philosophy, EarlyView.
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  • Models, Sherlock Holmes and the Emperor Claudius.Adam Toon - manuscript
    Recently, a number of authors have suggested that we understand scientific models in the same way as fictional characters, like Sherlock Holmes. The biggest challenge for this approach concerns the ontology of fictional characters. I consider two responses to this challenge, given by Roman Frigg, Ronald Giere and Peter Godfrey-Smith, and argue that neither is successful. I then suggest an alternative approach. While parallels with fiction are useful, I argue that models of real systems are more aptly compared to works (...)
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  • Fiction, Creation and Fictionality : An Overview.Matthieu Fontaine & Shahid Rahman - 2010 - Methodos 10.
    La réflexion philosophique sur la non-existence est une thématique qui a été abordée au commencement même de la philosophie et qui suscite, depuis la publication en 1905 de « On Denoting » par Russell, les plus vifs débats en philosophie analytique. Cependant, le débat féroce sur la sémantique des noms propres et des descriptions définies qui surgirent suite à la publication du « On Referring » par Strawson en 1950 n’engagea pas d’étude systématique de la sémantique des fictions. En fait, (...)
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  • Scientific Models and Metalinguistic Negotiation.Mirco Sambrotta - 2019 - Theoria. An International Journal for Theory, History and Foundations of Science 34 (2):277.
    The aim of this paper is to explore the possibility that, at least, some metaphysical debates are ‘metalinguistic negotiations’. I will take the dispute between the dominant approaches of realism and the anti-realism ones about the ontological status of scientific models as a case-study. I will argue that such a debate may be better understood as a disagreement, at bottom normatively, motivated, insofar as a normative and non-factual question may be involved in it: how the relevant piece of language ought (...)
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  • How Fictional Worlds Are Created.Deena Skolnick Weisberg - 2016 - Philosophy Compass 11 (8):462-470.
    Both adults and children have the ability to not only think about reality but also use their imaginations and create fictional worlds. This article describes the process by which world creation happens, drawing from philosophical and psychological treatments of this issue. First, world creators recognize the need to create a fictional world, as when starting a pretend game or opening a novel. Then, creators merge some real-world knowledge with the premises of the fictional world to construct a fuller representation, though (...)
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  • Scientific Understanding and Felicitous Legitimate Falsehoods.Insa Lawler - forthcoming - Synthese:1-29.
    Science is replete with falsehoods that epistemically facilitate understanding by virtue of being the very falsehoods they are. In view of this puzzling fact, some have relaxed the truth requirement on understanding. I offer a factive view of understanding that fully accommodates the puzzling fact in four steps: (i) I argue that the question how these falsehoods are related to the phenomenon to be understood and the question how they figure into the content of understanding it are independent. (ii) I (...)
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  • Modelos, idealizaciones: una crítica del ficcionalismo.Alejandro Cassini - 2013 - Principia: An International Journal of Epistemology 17 (3):345.
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  • The Relation Between Scientific Models and Their Targets: Report on the “Representation in Science” Workshop.Aldo Filomeno - 2019 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 50 (2):307-310.
    Brief overview of the debates held in the workshop on scientific representation, in Prague, May 2018.
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  • Models Don’T Decompose That Way: A Holistic View of Idealized Models.Collin Rice - 2019 - British Journal for the Philosophy of Science 70 (1):179-208.
    Many accounts of scientific modelling assume that models can be decomposed into the contributions made by their accurate and inaccurate parts. These accounts then argue that the inaccurate parts of the model can be justified by distorting only what is irrelevant. In this paper, I argue that this decompositional strategy requires three assumptions that are not typically met by our best scientific models. In response, I propose an alternative view in which idealized models are characterized as holistically distorted representations that (...)
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  • Models and Fictions in Science.Peter Godfrey-Smith - 2009 - Philosophical Studies 143 (1):101 - 116.
    Non-actual model systems discussed in scientific theories are compared to fictions in literature. This comparison may help with the understanding of similarity relations between models and real-world target systems. The ontological problems surrounding fictions in science may be particularly difficult, however. A comparison is also made to ontological problems that arise in the philosophy of mathematics.
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  • The Content of Model-Based Information.Raphael van Riel - 2015 - Synthese 192 (12):3839-3858.
    The paper offers an account of the structure of information provided by models that relevantly deviate from reality. It is argued that accounts of scientific modeling according to which a model’s epistemic and pragmatic relevance stems from the alleged fact that models give access to possibilities fail. First, it seems that there are models that do not give access to possibilities, for what they describe is impossible. Secondly, it appears that having access to a possibility is epistemically and pragmatically idle. (...)
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  • Fictionalism.Fiora Salis - 2015 - Online Companion to Problems in Analytic Philosophy.
    In this entry I will offer a survey of the contemporary debate on fic- tionalism, which is a distinctive anti-realist view about certain regions of discourse that are valued for their usefulness rather than their truth.
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  • 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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  • Is Captain Kirk a Natural Blonde? Do X-Ray Crystallographers Dream of Electron Clouds? Comparing Model-Based Inferences in Science with Fiction.Ann-Sophie Barwich - 2018 - In Otávio Bueno, George Darby, Steven French & Dean Rickles (eds.), Thinking About Science, Reflecting on Art: Bringing Aesthetics and Philosophy of Science Together. London, UK:
    Scientific models share one central characteristic with fiction: their relation to the physical world is ambiguous. It is often unclear whether an element in a model represents something in the world or presents an artifact of model building. Fiction, too, can resemble our world to varying degrees. However, we assign a different epistemic function to scientific representations. As artifacts of human activity, how are scientific representations allowing us to make inferences about real phenomena? In reply to this concern, philosophers of (...)
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  • Models, Fictions, and Realism: Two Packages.Arnon Levy - 2012 - Philosophy of Science 79 (5):738-748.
    Some philosophers of science – the present author included – appeal to fiction as an interpretation of the practice of modeling. This raises the specter of an incompatibility with realism, since fiction-making is essentially non-truth-regulated. I argue that the prima facie conflict can be resolved in two ways, each involving a distinct notion of fiction and a corresponding formulation of realism. The main goal of the paper is to describe these two packages. Toward the end I comment on how to (...)
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  • Philosophical Issues Related to Risks and Values.Renato Rodrigues Kinouchi - 2018 - Filosofia Unisinos 19 (3).
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  • Psa 2018.Philsci-Archive -Preprint Volume- - unknown
    These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018.
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  • Make-Believe and Model-Based Representation in Science: The Epistemology of Frigg’s and Toon’s Fictionalist Views of Modeling.Poznic Michael - 2016 - Teorema: International Journal of Philosophy 35 (3):201-218.
    Roman Frigg and Adam Toon, both, defend a fictionalist view of scientific modeling. One fundamental thesis of their view is that scientists are participating in games of make-believe when they study models in order to learn about the models themselves and about target systems represented by the models. In this paper, the epistemology of these two fictionalist views is critically discussed. I will argue that both views can give an explanation of how scientists learn about models they are studying. However, (...)
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  • Users, Structures, and Representation.M. Frisch - 2015 - British Journal for the Philosophy of Science 66 (2):285-306.
    This article defends a pragmatic and structuralist account of scientific representation of the kind recently proposed by Bas van Fraassen against criticisms of both the structuralist and the pragmatist plank of the account. I argue that the account appears to have the unacceptable consequence that the domain of a theory is restricted to phenomena for which we actually have constructed a model—a worry arising from the account’s pragmatism, which is exacerbated by its structuralism. Yet, the account has the resources, at (...)
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  • Fiction and Scientific Representation.Roman Frigg - 2010 - In .
    Understanding scientific modelling can be divided into two sub-projects: analysing what model-systems are, and understanding how they are used to represent something beyond themselves. The first is a prerequisite for the second: we can only start analysing how representation works once we understand the intrinsic character of the vehicle that does the representing. Coming to terms with this issue is the project of the first half of this chapter. My central contention is that models are akin to places and characters (...)
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  • Branching in the Landscape of Possibilities.Thomas Müller - 2012 - Synthese 188 (1):41-65.
    The metaphor of a branching tree of future possibilities has a number of important philosophical and logical uses. In this paper we trace this metaphor through some of its uses and argue that the metaphor works the same way in physics as in philosophy. We then give an overview of formal systems for branching possibilities, viz., branching time and (briefly) branching space-times. In a next step we describe a number of different notions of possibility, thereby sketching a landscape of possibilities. (...)
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  • Explaining Simulated Phenomena. A Defense of the Epistemic Power of Computer Simulations.Juan M. Durán - 2013 - Dissertation, University of Stuttgart
  • Simplified Models: A Different Perspective on Models as Mediators.C. D. McCoy & Michela Massimi - 2018 - European Journal for Philosophy of Science 8 (1):99-123.
    We introduce a novel point of view on the “models as mediators” framework in order to emphasize certain important epistemological questions about models in science which have so far been little investigated. To illustrate how this perspective can help answer these kinds of questions, we explore the use of simplified models in high energy physics research beyond the Standard Model. We show in detail how the construction of simplified models is grounded in the need to mitigate pressing epistemic problems concerning (...)
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  • 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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  • How Models Represent.James Nguyen - 2016 - Dissertation,
    Scientific models are important, if not the sole, units of science. This thesis addresses the following question: in virtue of what do scientific models represent their target systems? In Part i I motivate the question, and lay out some important desiderata that any successful answer must meet. This provides a novel conceptual framework in which to think about the question of scientific representation. I then argue against Callender and Cohen’s attempt to diffuse the question. In Part ii I investigate the (...)
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  • Manipulative Imagination: How to Move Things Around in Mathematics.Valeria Giardino - 2018 - Theoria : An International Journal for Theory, History and Fundations of Science 33 (2):345-360.
    In the first part of the paper, previous work about embodied mathematics and the practice of topology will be presented. According to the proposed view, in order to become experts, topologists have to learn how to use manipulative imagination: representations are cognitive tools whose functioning depends from pre-existing cognitive abilities and from specific training. In the second part of the paper, the notion of imagination as “make-believe” is discussed to give an account of cognitive tools in mathematics as props; to (...)
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  • Experimental Modeling in Biology: In Vivo Representation and Stand-Ins As Modeling Strategies.Marcel Weber - 2014 - Philosophy of Science 81 (5):756-769.
    Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in for a physically (...)
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  • Scientific Models and Representation.Gabriele Contessa - 2011 - In Steven French & Juha Saatsi (eds.), The Continuum Companion to the Philosophy of Science. Continuum Press. pp. 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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  • El Papel Del Pensamiento Poblacional En la Teoría de la Doble Herencia.Lorenzo Baravalle - 2018 - Scientiae Studia 15 (2):283.
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  • Learning From Minimal Economic Models.Till Grüne-Yanoff - 2009 - Erkenntnis 70 (1):81-99.
    It is argued that one can learn from minimal economic models. Minimal models are models that are not similar to the real world, do not resemble some of its features, and do not adhere to accepted regularities. One learns from a model if constructing and analysing the model affects one’s confidence in hypotheses about the world. Economic models, I argue, are often assessed for their credibility. If a model is judged credible, it is considered to be a relevant possibility. Considering (...)
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  • Um Quadro Dialógico Para Ficções Como Objetos Hipotéticos.Shahid Rahman & Juan Redmond - 2015 - Filosofia Unisinos 16 (1).
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  • Semblance or Similarity? Reflections on Simulation and Similarity: Michael Weisberg: Simulation and Similarity: Using Models to Understand the World. Oxford University Press, 2013. 224pp. ISBN 9780199933662, $65.00.Jay Odenbaugh - 2015 - Biology and Philosophy 30 (2):277-291.
    In this essay, I critically evaluate components of Michael Weisberg’s approach to models and modeling in his book Simulation and Similarity. First, I criticize his account of the ontology of models and mathematics. Second, I respond to his objections to fictionalism regarding models arguing that they fail. Third, I sketch a deflationary approach to models that retains many elements of his account but avoids the inflationary commitments.
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  • Playing with Molecules.Adam Toon - 2011 - Studies in History and Philosophy of Science Part A 42 (4):580-589.
    Recent philosophy of science has seen a number of attempts to understand scientific models by looking to theories of fiction. In previous work, I have offered an account of models that draws on Kendall Walton’s ‘make-believe’ theory of art. According to this account, models function as ‘props’ in games of make-believe, like children’s dolls or toy trucks. In this paper, I assess the make-believe view through an empirical study of molecular models. I suggest that the view gains support when we (...)
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  • Scientific Phenomena and Patterns in Data.Pascal Ströing - 2018 - Dissertation, LMU München
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  • Infinity and the Foundations of Linguistics.Ryan Nefdt - 2019 - Synthese 196 (5):1671-1711.
    The concept of linguistic infinity has had a central role to play in foundational debates within theoretical linguistics since its more formal inception in the mid-twentieth century. The conceptualist tradition, marshalled in by Chomsky and others, holds that infinity is a core explanandum and a link to the formal sciences. Realism/Platonism takes this further to argue that linguistics is in fact a formal science with an abstract ontology. In this paper, I argue that a central misconstrual of formal apparatus of (...)
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  • Incompatible Models in Chemistry: The Case of Electronegativity.Hernán Accorinti - 2019 - Foundations of Chemistry 21 (1):71-81.
    During the second half of the nineteenth century, electronegativity has been one of the most relevant chemical concepts to explain the relationships between chemical substances and their possible reactions. Specifically, EN is a property of the substances that allows them to attract external electrons in bonding situations. The problem arises because EN cannot be measured directly. Indeed, the only way to measure it is through different properties that do can be directly measured, for instance enthalpy, ionization energies or electron affinities. (...)
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