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Computational Models

Published online by Cambridge University Press:  01 January 2022

Paul Humphreys
Paul Humphreys*
Affiliation:
University of Virginia
*
Send requests for reprints to the author, Corcoran Department of Philosophy, 512 Cabell Hall, University of Virginia, Charlottesville, VA 22904; [email protected].
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Abstract

A different way of thinking about how the sciences are organized is suggested by the use of cross-disciplinary computational methods as the organizing unit of science, here called computational templates. The structure of computational models is articulated using the concepts of construction assumptions and correction sets. The existence of these features indicates that certain conventionalist views are incorrect, in particular it suggests that computational models come with an interpretation that cannot be removed as well as a prior justification. A form of selective realism is described which denies that one can simply read the ontological commitments from the theory itself.

Type
Research Article
Information
Philosophy of Science , Volume 69 , Issue S3 , September 2002 , pp. S1 - S11
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

‡

My thinking about modelling has been influenced by the work of a number of people, especially Bill Wimsatt, David Freedman, and Stephan Hartmann, to all of whom thanks are due for many illuminating conversations. Support from the National Science Foundation is gratefully acknowledged. A more detailed account of the ideas in this paper can be found in Humphreys (2003].

References

Epstein, J. (1997), Nonlinear Dynamics, Mathematical Biology, and Social Science. Santa Fe Institute Studies in the Science of Complexity, Lecture Notes, Vol. 4. Reading, Mass.: Addison-Wesley.Google Scholar
Feller, W. (1968), An Introduction to Probability Theory and Its Applications, Vol. 1. New York: John Wiley and Sons.Google Scholar
Hammersley, J. M., and Handscomb, D.C. (1964), Monte Carlo Methods. London: Methuen.CrossRefGoogle Scholar
Humphreys, P. (1991), ‘Computer Simulations’, in A. Fine, M. Forbes, and L. Wessels (eds), PSA 1990, Vol. 2. East Lansing: Philosophy of Science Association, 497–506.Google Scholar
Humphreys, P. (1994), “Numerical Experimentation”. in P. Humphreys (ed), Patrick Suppes: Scientific Philosopher, Vol. 2. Dordrecht: Kluwer Academic Publishers, 103–121.CrossRefGoogle Scholar
Humphreys, P. (1995), “Computational Science and Scientific Method”, Computational Science and Scientific Method 5, 499–512.Google Scholar
Humphreys, P. (2003), Extending Ourselves: Computational Science, Empiricism, and Scientific Method. New York: Oxford University Press.Google Scholar
Karplus, W. (1959), Analog Simulation: Solution of Field Problems. New York: McGraw-Hill.Google Scholar
Morse, P., and Feshbach, H. (1953), Methods of Theoretical Physics, Part 1. New York: McGraw-Hill.Google Scholar
Quine, W. V. O. (1961), “Logic and the Reification of Universals”, in Quine, W. V. O., From a Logical Point of View, 2nd Revised Edition. Cambridge, Mass.: Harvard University Press, 102–129.Google Scholar
Suppes, P. ([1970] 2002), Set-Theoretical Structures in Science. Mimeo manuscript, Institute for Mathematical Studies in the Social Sciences, Stanford University. Reprinted with additions as Set–Theoretical Structures in Science: Problems of Representation and Invariance CSLI Lecture Notes 130. Chicago: University of Chicago Press.Google Scholar
van Fraassen, B. (1972), “A Formal Approach to the Philosophy of Science”, in Colodny, R. (ed), Paradigms and Paradoxes. Pittsburgh: University of Pittsburgh Press, 303–366.Google Scholar