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  1. 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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  • The Scientific Image.William Demopoulos & Bas C. van Fraassen - 1982 - Philosophical Review 91 (4):603.
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  • Models, measurement and computer simulation: the changing face of experimentation.Margaret Morrison - 2009 - Philosophical Studies 143 (1):33-57.
    The paper presents an argument for treating certain types of computer simulation as having the same epistemic status as experimental measurement. While this may seem a rather counterintuitive view it becomes less so when one looks carefully at the role that models play in experimental activity, particularly measurement. I begin by discussing how models function as “measuring instruments” and go on to examine the ways in which simulation can be said to constitute an experimental activity. By focussing on the connections (...)
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  • Fictions in Science: Philosophical Essays on Modeling and Idealization.Mauricio Suárez (ed.) - 2008 - New York: Routledge.
    Science is popularly understood as being an ideal of impartial algorithmic objectivity that provides us with a realistic description of the world down to the last detail. The essays collected in this book—written by some of the leading experts in the field—challenge this popular image right at its heart, taking as their starting point that science trades not only in truth, but in fiction, too. With case studies that range from physics to economics and to biology, _Fictions in Science_ reveals (...)
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  • Extending Ourselves: Computational Science, Empiricism, and Scientific Method.Paul Humphreys - 2004 - New York, US: Oxford University Press.
    Computational methods such as computer simulations, Monte Carlo methods, and agent-based modeling have become the dominant techniques in many areas of science. Extending Ourselves contains the first systematic philosophical account of these new methods, and how they require a different approach to scientific method. Paul Humphreys draws a parallel between the ways in which such computational methods have enhanced our abilities to mathematically model the world, and the more familiar ways in which scientific instruments have expanded our access to the (...)
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  • Scientific fictions as rules of inference.Mauricio Suárez - 2008 - In Mauricio Suárez (ed.), Fictions in Science: Philosophical Essays on Modeling and Idealization. New York: Routledge. pp. 158--178.
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  • Computer Simulations in Science.Eric Winsberg - forthcoming - Stanford Encyclopedia of Philosophy.
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  • Abstraction and Idealization in the Formal Verification of Software Systems.Nicola Angius - 2013 - Minds and Machines 23 (2):211-226.
    Questions concerning the epistemological status of computer science are, in this paper, answered from the point of view of the formal verification framework. State space reduction techniques adopted to simplify computational models in model checking are analysed in terms of Aristotelian abstractions and Galilean idealizations characterizing the inquiry of empirical systems. Methodological considerations drawn here are employed to argue in favour of the scientific understanding of computer science as a discipline. Specifically, reduced models gained by Dataion are acknowledged as Aristotelian (...)
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  • Three Kinds of Idealization.Michael Weisberg - 2007 - Journal of Philosophy 104 (12):639-659.
    Philosophers of science increasingly recognize the importance of idealization: the intentional introduction of distortion into scientific theories. Yet this recognition has not yielded consensus about the nature of idealization. e literature of the past thirty years contains disparate characterizations and justifications, but little evidence of convergence towards a common position.
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  • Scientific Theories of Computational Systems in Model Checking.Nicola Angius & Guglielmo Tamburrini - 2011 - Minds and Machines 21 (2):323-336.
    Model checking, a prominent formal method used to predict and explain the behaviour of software and hardware systems, is examined on the basis of reflective work in the philosophy of science concerning the ontology of scientific theories and model-based reasoning. The empirical theories of computational systems that model checking techniques enable one to build are identified, in the light of the semantic conception of scientific theories, with families of models that are interconnected by simulation relations. And the mappings between these (...)
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  • Reasoning about knowledge.Ronald Fagin, Joseph Y. Halpern, Yoram Moses & Moshe Vardi - 2003 - Cambridge: MIT Press.
    Reasoning About Knowledge is the first book to provide a general discussion of approaches to reasoning about knowledge and its applications to distributed ...
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  • (6 other versions)The Structure of Scientific Revolutions.Thomas S. Kuhn - 1962 - Chicago, IL: University of Chicago Press. Edited by Ian Hacking.
    Thomas S. Kuhn's classic book is now available with a new index.
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  • The World as a Process: Simulations in the Natural and Social Sciences.Stephan Hartmann - 1996 - In Rainer Hegselmann et al (ed.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View.
    Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...)
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  • Models and representation.Richard Hughes - 1997 - Philosophy of Science 64 (4):336.
    A general account of modeling in physics is proposed. Modeling is shown to involve three components: denotation, demonstration, and interpretation. Elements of the physical world are denoted by elements of the model; the model possesses an internal dynamic that allows us to demonstrate theoretical conclusions; these in turn need to be interpreted if we are to make predictions. The DDI account can be readily extended in ways that correspond to different aspects of scientific practice.
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  • (1 other version)Theory and reality: an introduction to the philosophy of science.Peter Godfrey-Smith - 2003 - Chicago: University of Chicago Press.
    How does science work? Does it tell us what the world is "really" like? What makes it different from other ways of understanding the universe? In Theory and Reality , Peter Godfrey-Smith addresses these questions by taking the reader on a grand tour of one hundred years of debate about science. The result is a completely accessible introduction to the main themes of the philosophy of science. Intended for undergraduates and general readers with no prior background in philosophy, Theory and (...)
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  • Software Intensive Science.John Symons & Jack Horner - 2014 - Philosophy and Technology 27 (3):461-477.
    This paper argues that the difference between contemporary software intensive scientific practice and more traditional non-software intensive varieties results from the characteristically high conditionality of software. We explain why the path complexity of programs with high conditionality imposes limits on standard error correction techniques and why this matters. While it is possible, in general, to characterize the error distribution in inquiry that does not involve high conditionality, we cannot characterize the error distribution in inquiry that depends on software. Software intensive (...)
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  • Model-Based Reasoning in Scientific Discovery.L. Magnani, Nancy Nersessian & Paul Thagard (eds.) - 1999 - Kluwer/Plenum.
    The book Model-Based Reasoning in Scientific Discovery, aims to explain how specific modeling practices employed by scientists are productive methods of ...
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  • Model-based and manipulative abduction in science.Lorenzo Magnani - 2004 - Foundations of Science 9 (3):219-247.
    What I call theoretical abduction (sentential and model-based)certainly illustrates much of what is important in abductive reasoning, especially the objective of selecting and creating a set of hypotheses that are able to dispense good (preferred) explanations of data, but fails to account for many cases of explanation occurring in science or in everyday reasoning when the exploitation of the environment is crucial. The concept of manipulative abduction is devoted to capture the role of action in many interesting situations: action provides (...)
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  • Does matter really matter? Computer simulations, experiments, and materiality.Wendy S. Parker - 2009 - Synthese 169 (3):483-496.
    A number of recent discussions comparing computer simulation and traditional experimentation have focused on the significance of “materiality.” I challenge several claims emerging from this work and suggest that computer simulation studies are material experiments in a straightforward sense. After discussing some of the implications of this material status for the epistemology of computer simulation, I consider the extent to which materiality (in a particular sense) is important when it comes to making justified inferences about target systems on the basis (...)
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  • Mental models in cognitive science.P. N. Johnson-Laird - 1980 - Cognitive Science 4 (1):71-115.
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  • The philosophy of simulation: hot new issues or same old stew?Roman Frigg & Julian Reiss - 2008 - Synthese 169 (3):593-613.
    Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science , but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of (...)
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  • Holism, entrenchment, and the future of climate model pluralism.Johannes Lenhard & Eric Winsberg - 2010 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 41 (3):253-262.
    In this paper, we explore the extent to which issues of simulation model validation take on novel characteristics when the models in question become particularly complex. Our central claim is that complex simulation models in general, and global models of climate in particular, face a form of confirmation holism. This holism, moreover, makes analytic understanding of complex models of climate either extremely difficult or even impossible. We argue that this supports a position we call convergence skepticism: the belief that the (...)
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  • Theory and Reality. An Introduction to the Philosophy of Science.Peter Godfrey-Smith - 2005 - Tijdschrift Voor Filosofie 67 (2):393-394.
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  • (1 other version)The Neglect of Experiment.Allan Franklin - 1989 - British Journal for the Philosophy of Science 40 (2):185-190.
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  • Zenon Pylyshyn, "Computation and Cognition: Toward a Foundation for Cognitive Science" and Alvin I. Goldman, "Epistemology and Cognition". [REVIEW]Andy Clark - 1988 - Philosophical Quarterly 38 (153):526-532.
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  • (1 other version)The Neglect of Experiment.Allan Franklin - 1988 - Philosophy of Science 55 (2):306-308.
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  • Science in the age of computer simulation.Eric Winsberg - 2010 - Chicago: University of Chicago Press.
    Introduction -- Sanctioning models : theories and their scope -- Methodology for a virtual world -- A tale of two methods -- When theories shake hands -- Models of climate : values and uncertainties -- Reliability without truth -- Conclusion.
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  • The philosophy of simulation: hot new issues or same old stew?Roman Frigg & Julian Reiss - 2011 - Synthese 180 (1):77-77.
    Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science, but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of models (...)
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  • Models and stories in Hadron physics.Mary S. Morgan & Margaret Morrison - 1999 - In Mary S. Morgan & Margaret Morrison (eds.), Models as Mediators: Perspectives on Natural and Social Science. Cambridge University Press. pp. 326-346.
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  • The Neglect of Experiment.Allan Franklin - 1986 - Cambridge University Press.
    What role have experiments played, and should they play, in physics? How does one come to believe rationally in experimental results? The Neglect of Experiment attempts to provide answers to both of these questions. Professor Franklin's approach combines the detailed study of four episodes in the history of twentieth century physics with an examination of some of the philosophical issues involved. The episodes are the discovery of parity nonconservation in the 1950s; the nondiscovery of parity nonconservation in the 1930s, when (...)
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  • Sanctioning Models: The Epistemology of Simulation.Eric Winsberg - 1999 - Science in Context 12 (2):275-292.
    The ArgumentIn its reconstruction of scientific practice, philosophy of science has traditionally placed scientific theories in a central role, and has reduced the problem of mediating between theories and the world to formal considerations. Many applications of scientific theories, however, involve complex mathematical models whose constitutive equations are analytically unsolvable. The study of these applications often consists in developing representations of the underlying physics on a computer, and using the techniques of computer simulation in order to learn about the behavior (...)
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  • Model-based creative abduction.L. Magnani - 1999 - In L. Magnani, Nancy Nersessian & Paul Thagard (eds.), Model-Based Reasoning in Scientific Discovery. Kluwer/Plenum. pp. 219--238.
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  • Structural representation and surrogative reasoning.Chris Swoyer - 1991 - Synthese 87 (3):449 - 508.
    It is argued that a number of important, and seemingly disparate, types of representation are species of a single relation, here called structural representation, that can be described in detail and studied in a way that is of considerable philosophical interest. A structural representation depends on the existence of a common structure between a representation and that which it represents, and it is important because it allows us to reason directly about the representation in order to draw conclusions about the (...)
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  • On computation and cognition: Toward a foundation of cognitive science.Zenon Pylyshyn - 1989 - Artificial Intelligence 38 (2):248-251.
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  • Experiment, Right or Wrong.Allan Franklin - 1990 - New York: Cambridge University Press.
    In Experiment, Right or Wrong, Allan Franklin continues his investigation of the history and philosophy of experiment presented in his previous book, The Neglect of Experiment. Using a combination of case studies and philosophical readings of those studies, Franklin again addresses two important questions: (1) What role does and should experiment play in the choice between competing theories and in the confirmation or refutation of theories and hypotheses? (2) How do we come to believe reasonably in experimental results? Experiment, Right (...)
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  • Experiment Right or Wrong.Allan Franklin & David Gooding - 1994 - British Journal for the Philosophy of Science 45 (1):341-352.
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  • A brief overview of the philosophical study of computer simulations.Juan M. Durán - 2013 - American Philosophical Association Newsletter on Philosophy and Computers 13 (1):38-46.
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