Synthese

, Volume 152, Issue 1, pp 1–19 | Cite as

Models of Success Versus the Success of Models: Reliability without Truth

Abstract

In computer simulations of physical systems, the construction of models is guided, but not determined, by theory. At the same time simulations models are often constructed precisely because data are sparse. They are meant to replace experiments and observations as sources of data about the world; hence they cannot be evaluated simply by being compared to the world. So what can be the source of credibility for simulation models? I argue that the credibility of a simulation model comes not only from the credentials supplied to it by the governing theory, but also from the antecedently established credentials of the model building techniques employed by the simulationists. In other words, there are certain sorts of model building techniques which are taken, in and of themselves, to be reliable. Some of these model building techniques, moreover, incorporate what are sometimes called “falsifications.” These are contrary-to-fact principles that are included in a simulation model and whose inclusion is taken to increase the reliability of the results. The example of a falsification that I consider, called artificial viscosity, is in widespread use in computational fluid dynamics. Artificial viscosity, I argue, is a principle that is successfully and reliably used across a wide domain of fluid dynamical applications, but it does not offer even an approximately “realistic” or true account of fluids. Artificial viscosity, therefore, is a counter-example to the principle that success implies truth – a principle at the foundation of scientific realism. It is an example of reliability without truth.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Campbell, J.: 2000 ‘Artificial Viscosity for Multi-dimensional Hydrodynamics Codes’, http://cnls.lanl.gov/Highlights/2000-09/article.htm
  2. Caramana, E.J., Shashkov, M.J., Whalen, P.P. 1988‘Formulations of Artificial Viscosity for Multi-Dimensional Shock Wave Computations’Journal of Computational Physics1447097CrossRefGoogle Scholar
  3. Fine, A. 1991‘Piecemeal Realism’Philosophical Studies617996CrossRefGoogle Scholar
  4. Fine, A. 1996The Shaky GameUniversity of Chicago PressChicagoGoogle Scholar
  5. Fine, A. 2001‘The Scientific Image’ Twenty Years Later’Philosophical Studies106107122CrossRefGoogle Scholar
  6. Galison, P. 1997Image and Logic: A Material Culture of MicrophysicsUniversity of Chicago PressChicagoGoogle Scholar
  7. Hacking, I. 1988‘On the Stability of the Laboratory Sciences’The Journal of Philosophy85507515Google Scholar
  8. Horwich, P. 1999TruthOxford University PressOxfordGoogle Scholar
  9. Kitcher, P. 2002‘On the Explanatory Role of Correspondence Truth’Philosophy and Phenomenological Research64346364Google Scholar
  10. Laudan, L. 1981‘A Confutation of Convergent Realism’Philosophy of Science48218249CrossRefGoogle Scholar
  11. Morgan, M.Morrison, M. eds. 1999Models as MediatorsCambridge University PressCambridgeGoogle Scholar
  12. Steinhoff, J., Underhill, D. 1994‘Modification of the Euler Equations for ‘Vorticity Confinement’: Application to the Computation of Interacting Vortex Rings’Physics of Fluids627382744CrossRefGoogle Scholar
  13. Neumann, J, R. D., Richtmyer 1950‘A Method for the Numerical Calculation of Hydrodynamical Shocks’Journal of Applied Physics21232247CrossRefGoogle Scholar
  14. Winsberg, E. 1999‘Sanctioning Models: The Epistemology of Simulation’Science in Context12275292CrossRefGoogle Scholar
  15. Winsberg, E. 2003‘Simulated Experiments: Methodology for a Virtual World’Philosophy of Science70105125CrossRefGoogle Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of South FloridaTampaU.S.A

Personalised recommendations