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  1. Computer Simulations in Science and Engineering. Concept, Practices, Perspectives.Juan Manuel Durán - 2018 - Springer.
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  • Three Myths of Computer Science.James H. Moor - 1978 - British Journal for the Philosophy of Science 29 (3):213-222.
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  • Three Paradigms of Computer Science.Amnon H. Eden - 2007 - Minds and Machines 17 (2):135-167.
    We examine the philosophical disputes among computer scientists concerning methodological, ontological, and epistemological questions: Is computer science a branch of mathematics, an engineering discipline, or a natural science? Should knowledge about the behaviour of programs proceed deductively or empirically? Are computer programs on a par with mathematical objects, with mere data, or with mental processes? We conclude that distinct positions taken in regard to these questions emanate from distinct sets of received beliefs or paradigms within the discipline: – The rationalist (...)
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  • What an Algorithm Is.Robin Hill - 2016 - Philosophy and Technology 29 (1):35-59.
    The algorithm, a building block of computer science, is defined from an intuitive and pragmatic point of view, through a methodological lens of philosophy rather than that of formal computation. The treatment extracts properties of abstraction, control, structure, finiteness, effective mechanism, and imperativity, and intentional aspects of goal and preconditions. The focus on the algorithm as a robust conceptual object obviates issues of correctness and minimality. Neither the articulation of an algorithm nor the dynamic process constitute the algorithm itself. Analysis (...)
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  • The Limits of Correctness.Brian Cantwell Smith - 1985 - Acm Sigcas Computers and Society 14 (1):18-26.
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  • Programming Languages as Technical Artifacts.Raymond Turner - 2014 - Philosophy and Technology 27 (3):377-397.
    Taken at face value, a programming language is defined by a formal grammar. But, clearly, there is more to it. By themselves, the naked strings of the language do not determine when a program is correct relative to some specification. For this, the constructs of the language must be given some semantic content. Moreover, to be employed to generate physical computations, a programming language must have a physical implementation. How are we to conceptualize this complex package? Ontologically, what kind of (...)
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  • Epistemic Opacity, Confirmation Holism and Technical Debt: Computer Simulation in the Light of Empirical Software Engineering.Julian Newman - 2016 - In History and Philosophy of Computing (IFIP AICT 487). Cham, Switzerland: Springer. pp. 256-272.
    Epistemic opacity vis a vis human agents has been presented as an essential, ineliminable characteristic of computer simulation models resulting from the characteristics of the human cognitive agent. This paper argues, on the contrary, that such epistemic opacity as does occur in computer simulations is not a consequence of human limitations but of a failure on the part of model developers to adopt good software engineering practice for managing human error and ensuring the software artefact is maintainable. One consequence of (...)
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  • Varying the Explanatory Span: Scientific Explanation for Computer Simulations.Juan Manuel Durán - 2017 - International Studies in the Philosophy of Science 31 (1):27-45.
    This article aims to develop a new account of scientific explanation for computer simulations. To this end, two questions are answered: what is the explanatory relation for computer simulations? And what kind of epistemic gain should be expected? For several reasons tailored to the benefits and needs of computer simulations, these questions are better answered within the unificationist model of scientific explanation. Unlike previous efforts in the literature, I submit that the explanatory relation is between the simulation model and the (...)
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  • When Are Two Algorithms the Same?Andreas Blass, Nachum Dershowitz & Yuri Gurevich - 2009 - Bulletin of Symbolic Logic 15 (2):145-168.
    People usually regard algorithms as more abstract than the programs that implement them. The natural way to formalize this idea is that algorithms are equivalence classes of programs with respect to a suitable equivalence relation. We argue that no such equivalence relation exists.
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  • Implementation is Semantic Interpretation.Willam J. Rapaport - 1999 - The Monist 82 (1):109-130.
    What is the computational notion of “implementation”? It is not individuation, instantiation, reduction, or supervenience. It is, I suggest, semantic interpretation.
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  • Extending Ourselves: Computational Science, Empiricism, and Scientific Method.Paul Humphreys - 2004 - Oxford University Press.
    Computational methods have become the dominant technique in many areas of science. This book contains the first systematic philosophical account of these new methods and their consequences for scientific method. This book will be of interest to philosophers of science and to anyone interested in the role played by computers in modern science.
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  • Grounds for Trust: Essential Epistemic Opacity and Computational Reliabilism.Juan M. Durán & Nico Formanek - 2018 - Minds and Machines 28 (4):645-666.
    Several philosophical issues in connection with computer simulations rely on the assumption that results of simulations are trustworthy. Examples of these include the debate on the experimental role of computer simulations :483–496, 2009; Morrison in Philos Stud 143:33–57, 2009), the nature of computer data Computer simulations and the changing face of scientific experimentation, Cambridge Scholars Publishing, Barcelona, 2013; Humphreys, in: Durán, Arnold Computer simulations and the changing face of scientific experimentation, Cambridge Scholars Publishing, Barcelona, 2013), and the explanatory power of (...)
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  • Reconstructing Reality: Models, Mathematics, and Simulations.Margaret Morrison - 2015 - Oup Usa.
    The book examines issues related to the way modeling and simulation enable us to reconstruct aspects of the world we are investigating. It also investigates the processes by which we extract concrete knowledge from those reconstructions and how that knowledge is legitimated.
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  • Program Verification: The Very Idea.James H. Fetzer - 1988 - Communications of the Acm 31 (9):1048--1063.
    The notion of program verification appears to trade upon an equivocation. Algorithms, as logical structures, are appropriate subjects for deductive verification. Programs, as causal models of those structures, are not. The success of program verification as a generally applicable and completely reliable method for guaranteeing program performance is not even a theoretical possibility.
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