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Computer Simulation: The Cooperation between Experimenting and Modeling

Published online by Cambridge University Press:  01 January 2022

Abstract

The goal of the present article is to contribute to the epistemology and methodology of computer simulations. The central thesis is that the process of simulation modeling takes the form of an explorative cooperation between experimenting and modeling. This characteristic mode of modeling turns simulations into autonomous mediators in a specific way; namely, it makes it possible for the phenomena and the data to exert a direct influence on the model. The argumentation will be illustrated by a case study of the general circulation models of meteorology, the major simulation models in climate research.

Type
Research Article
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

I wish to thank Martin Carrier and Günter Küppers for fruitful discussions of former versions of the manuscript and the anonymous referees of Philosophy of Science for their useful suggestions.

References

Arakawa, Akio (1966), “Computational Design for Long-Term Numerical Integration of the Equations of Fluid Motion: Two-Dimensional Incompressible Flow. Part I,” Journal of Computational Physics 1:119143.CrossRefGoogle Scholar
Arakawa, Akio (2000), “A Personal Perspective on the Early Years of General Circulation Modeling at UCLA,” in Randall, David A. (ed.), General Circulation Model Development. San Diego: Academic Press, 166.Google Scholar
Dahan, Amy (2001), “History and Epistemology of Models: Meteorology as a Case-Study (1946–1963),” Archive for History of Exact Sciences 55:395422.Google Scholar
Dowling, Deborah (1999), “Experimenting on Theories,” Science in Context 12 (2): 261273..CrossRefGoogle Scholar
Fox Keller, Evelyn (2003), “Models, Simulation, and `Computer Experiments,’” in Radder, Hans (ed.), The Philosophy of Scientific Experimentation. Pittsburgh: University of Pittsburgh Press, 198215.CrossRefGoogle Scholar
Galison, Peter (1996), “Computer Simulations and the Trading Zone,” in Galison, Peter and Stump, David J. (eds.), The Disunity of Science: Boundaries, Contexts, and Power. Stanford, CA: Stanford University Press, 118157.Google Scholar
Gooding, David, Pinch, Trevor, and Schaffer, Simon, eds. (1989), The Uses of Experiment. Cambridge: Cambridge University Press.Google Scholar
Hartmann, Stephan (1996), “The World as a Process: Simulations in the Natural and Social Sciences,” in Hegselmann, Rainer (ed.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View. Dordrecht: Kluwer, 77100.CrossRefGoogle Scholar
Hon, Giora (1998), “Exploiting Errors,” Studies in the History and Philosophy of Science A 29 (3): 465481..Google Scholar
Hughes, R. I. G. (1999), “The Ising Model, Computer Simulation, and Universal Physics,” in Morgan, Mary and Morrison, Margaret (eds.), Models as Mediators: Perspectives on Natural and Social Science. Cambridge: Cambridge University Press, 97145.CrossRefGoogle Scholar
Humphreys, Paul (1991), “Computer Simulations,” in Fine, Arthur, Forbes, F., and Wessels, L. (eds.), PSA 1990: Proceedings of the Biennial Meeting of the Philosophy of Science Association, Vol. 2. East Lansing, MI: Philosophy of Science Association, 497506.Google Scholar
Humphreys, Paul (1994), “Numerical Experimentation,” in his Patrick Suppes: Scientific Philosopher. Vol. 2. Dordrecht: Kluwer, 103121.CrossRefGoogle Scholar
Humphreys, Paul (2004), Extending Ourselves: Computational Science, Empiricism, and Scientific Method. New York: Oxford University Press.CrossRefGoogle Scholar
Günter, Küppers, and Lenhard, Johannes (2005), “Computersimulationen: Modellierungen zweiter Ordnung,” Journal for General Philosophy of Science 36 (2): 305329..Google Scholar
Lewis, John M. (1998), “Clarifying the Dynamics of the General Circulation: Phillips’s 1956 Experiment,” Bulletin of the American Meteorological Society 79 (1): 3960..2.0.CO;2>CrossRefGoogle Scholar
Lorenz, Edward (1967), “The Nature of the Theory of the General Circulation of the Atmosphere,” Technical Paper 218. Geneva: World Meteorological Organization, 115161.Google Scholar
Metropolis, Nicolas, and Ulam, Stanislaw (1949), “The Monte Carlo Method,” Journal of the American Statistical Association 44:335341.CrossRefGoogle ScholarPubMed
Morgan, Mary (2003), “Experiments without Material Intervention: Model Experiments, Virtual Experiments, and Virtually Experiments,” in Radder, Hans (ed.), The Philosophy of Scientific Experimentation. Pittsburgh: University of Pittsburgh Press, 216235.CrossRefGoogle Scholar
Morrison, Margaret (1999), “Models as Autonomous Agents,” in Morgan, Mary and Morrison, Margaret (eds.), Models as Mediators: Perspectives on Natural and Social Science. Cambridge: Cambridge University Press, 3865.CrossRefGoogle Scholar
Neelamkavil, Francis (1987), Computer Simulation and Modelling. New York: Wiley.Google Scholar
Norton, Stephen D., and Suppe, Frederick (2001), “Why Atmospheric Modeling Is Good Science,” in Miller, Clark A. and Edwards, Paul N. (eds.), Changing the Atmosphere. Cambridge, MA: MIT Press, 67105.Google Scholar
Oreskes, Naomi, Shrader-Frechette, Kristin, and Belitz, Kenneth (1994), “Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences,” Science 263:641646.CrossRefGoogle ScholarPubMed
Pfeffer, Richard L., ed. (1960), Dynamics of Climate: The Proceedings of a Conference on the Application of Numerical Integration Techniques to the Problem of the General Circulation Held October 26–28, 1955. Oxford: Pergamon.Google Scholar
Phillips, Norman (1956), “The General Circulation of the Atmosphere: A Numerical Experiment,” Quarterly Journal of the Royal Meteorological Society 82 (352): 123164..CrossRefGoogle Scholar
Phillips, Norman (1959), “An Example of Non-linear Computational Instability,” in Bolin, Bert (ed.), The Atmosphere and the Sea in Motion. New York: Rockefeller Institute.Google Scholar
Phillips, Norman (2000), “Foreword,” in Randall, David A., General Circulation Model Development. San Diego: Academic Press, xxviixxix.Google Scholar
Radder, Hans, ed. (2003), The Philosophy of Scientific Experimentation. Pittsburgh: University of Pittsburgh Press.CrossRefGoogle Scholar
Redhead, Michael (1987), “Models in Physics,” British Journal for the Philosophy of Science 31:145163.CrossRefGoogle Scholar
Rohrlich, Fritz (1991), “Computer Simulation in the Physical Sciences,” in Fine, Arthur, Forbes, F., and Wessels, L. (eds.), PSA 1990: Proceedings of the Biennial Meeting of the Philosophy of Science Association, Vol. 2. East Lansing, MI: Philosophy of Science Association, 507518.Google Scholar
Schweber, Sylvan, and Waechter, Matthias (2000), “Complex Systems, Modelling and Simulation,” Studies in the History and Philosophy of Modern Physics 31 (4): 583609..CrossRefGoogle Scholar
Steinle, Friedrich (2002), “Discovering? Justifying? Experiments in History and Philosophy of Science,” in Steinle, Friedrich and Schickore, Jutta (eds.), Revisiting Discovery and Justification. Preprint 211. Berlin: Max-Planck-Institut für Wissenschaftsgeschichte, 175184.Google Scholar
Stöckler, Manfred (2000), “On Modeling and Simulations as Instruments for the Study of Complex Systems,” in Carrier, Martin, Massey, Gerald J., and Ruetsche, Laura (eds.), Science at Century’s End. Pittsburgh: University of Pittsburgh Press, 355373.CrossRefGoogle Scholar
Ulam, Stanislaw (1952), “Random Processes and Transformations,” in Proceedings of the International Congress of Mathematicians 1950. Providence, RI: American Mathematical Society.Google Scholar
Ulam, Stanislaw, Bednarek, A. R., and Ulam, Francoise (1990), Analogies between Analogies: The Mathematical Reports of S. M. Ulam and His Los Alamos Collaborators. Berkeley: University of California Press.CrossRefGoogle Scholar
Von Neumann, John, and Richtmyer, Robert D. (1947), “Statistical Methods in Neutron Diffusion,” in Ulam, Stanislaw, Bednarek, A. R., and Ulam, Francoise, Analogies between Analogies: The Mathematical Reports of S. M. Ulam and His Los Alamos Collaborators. Berkeley: University of California Press.Google Scholar
Weart, Spencer (2003), Arakawa’s Computation Device. American Institute of Physics, http://www.aip.org/history/climate/arakawa.htm.Google Scholar
Wiin-Nielsen, Aksel (1991), “The Birth of Numerical Weather Prediction,” Tellus 43:3652.CrossRefGoogle Scholar
Winsberg, Eric (1999), “Sanctioning Models: The Epistemology of Simulation,” Science in Context 12 (2): 275292..CrossRefGoogle Scholar
Winsberg, Eric (2003), “Simulated Experiments: Methodology for a Virtual World,” Philosophy of Science 70:105125.CrossRefGoogle Scholar