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Computer Simulations and Computational Models in Science

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Springer Handbook of Model-Based Science

Part of the book series: Springer Handbooks ((SHB))

Abstract

Computational science and computer simulations have significantly changed the face of science in recent times, even though attempts to extend our computational capacities are by no means new and computer simulations are more or less accepted across scientific fields as legitimate ways of reaching results (Sect. 34.1). Also, a great variety of computational models and computer simulations can be met across science, in terms of the types of computers, computations, computational models, or physical models involved and they can be used for various types of inquiries and in different scientific contexts (Sect. 34.2). For this reason, epistemological analyses of computer simulations are contextual for a great part. Still, computer simulations raise general questions regarding how their results are justified, how computational models are selected, which type of knowledge is thereby produced (Sect. 34.3), or how computational accounts of phenomena partly challenge traditional expectations regarding the explanation and understanding of natural systems (Sect. 34.4). Computer simulations also share various epistemological features with experiments and thought experiments; hence, the need for transversal analyses of these activities (Sect. 34.5). Finally, providing a satisfactory and fruitful definition of computer simulations turns out to be more difficult than expected, partly because this notion is at the crossroads of difficult questions like the nature of representation and computation or the success of scientific inquiries (Sect. 34.6). Overall, a pointed analysis of computer simulations in parallel requires developing insights about the evolving place of human capacities and humans within (computational) science (Sect. 34.7).

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Abbreviations

3-D:

three-dimensional

CA:

cellular automata

CERN:

European Organization for Nuclear Research

DDI:

denotation, demonstration, interpretation

DN:

deductive-nomological

ENIAC:

electronic numerical integrator and computer

fMRI:

functional magnetic resonance imaging

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Acknowledgements

I have tried to present a critical survey of the literature with the aim of clarifying discussions. I thank the editors for providing me the opportunity to write this article and for being so generous with space. I also thank T. Boyer-Kassem, J.M. Durán, E. Arnold, and specifically P. Humphreys for feedback or help concerning this review article. I am also grateful to A. Barberousse, J. P. Delahaye, J. Dubucs, R. El Skaf, R. Frigg, S. Hartmann, J. Jebeile, M. Morrison, M. Vorms, H. Zwirn for stimulating exchanges over the last couple of years about the issue of models and simulations and related questions. All remaining shortcomings are mine. Various valuable review articles, such as (J.M. Durán: A brief overview of the philosophical study of computer simulations, Am. Philos. Assoc. Newslett. Philos. Comput. 13(1), 38–46 (2013), W.S. Parker: Computer simulation, In: The Routledge Companion to Philosophy of Science, 2nd edn., ed. by S. Psillos, M. Curd (Routledge, London 2013)), have been recently written about the issue of computer simulations. (P. Humphreys: Computational science in Oxford bibliographies online, (2012) doi:10.1093/OBO/9780195396577-0100) presents and discusses important references and may be used as a short but insightful research guide.

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    Cyrille Imbert

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Imbert, C. (2017). Computer Simulations and Computational Models in Science. In: Magnani, L., Bertolotti, T. (eds) Springer Handbook of Model-Based Science. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-30526-4_34

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