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How Does Holism Challenge the Validation of Computer Simulation?

  • Johannes LenhardEmail author
Chapter
Part of the Simulation Foundations, Methods and Applications book series (SFMA)

Abstract

Designing and building complex artifacts like simulation models often rely on the strategy of modularity. My main claim is that the validation of simulation models faces a challenge of holism because modularity tends to erode over the process of building a simulation model. Two different reasons that fuel the tendency to erosion are analyzed. Both are based on the methodology of simulation, but on different levels. The first has to do with the way parameter adjustment works in simulation; the second comes from how different groups of software programmers work together. The chapter will conclude by drawing lessons about how holism challenges the validation of simulation and by discussing a corollary to conformational holism: the boundary between validation and verification tends to become blurred, thus undermining a strategy that insists on keeping them separate.

Keywords

Kluge Modularity Parameterization Tuning Verification and validation 

Notes

Acknowledgements

This chapter looks back on a history of improvements. I would like to thank the reviewers of this volume for their helpful suggestions and the active audiences at the “How to Build Trust…” conference, Hanover 2015, and the PSA 2016 (whose proceedings include a shorter version of this paper). I also thank the DFG for funding under SPP 1689.

References

  1. Agre, P. E. (2003). Hierarchy and history in Simon’s “Architecture of complexity“, Journal of the Learning Sciences, 3, 413–426.CrossRefGoogle Scholar
  2. Brooks, F. P. (2010). The design of design. Boston: Addison-Wesley.Google Scholar
  3. Clark, Andy. (1987). The kludge in the machine. Mind and Language, 2(4), 277–300.MathSciNetCrossRefGoogle Scholar
  4. Fillion, N. (2017). The vindication of computer simulations. In J. Lenhard, & M. Carrier (Eds.), Mathematics as a tool, 137–56. Boston studies in history and philosophy of science 327. Cham: Springer.Google Scholar
  5. Foote, B., & Joseph, Y. (2000). Big ball of mud. In H. Neil, F. Brian & R. Hans (Eds.), Pattern Languages of Program Design 4 (= Software Patterns. 4). Addison Wesley, 2000. Retrieved July 25, 2018, from http://laputan.org/pub/foote/mud.pdf.
  6. Frigg, R., & Reiss, J. (2009). The philosophy of simulation. Hot new issues or same old stew? Synthese, 169(3), 593–613.Google Scholar
  7. Gabriel, R. P. (1996). Patterns of software. Tales from the software community. New York and Oxford: Oxford University Press.Google Scholar
  8. Gramelsberger, G., & Johann F. (eds.). (2011). Climate change and policy. The calculability of climate change and the challenge of uncertainty. Heidelberg: Springer.Google Scholar
  9. Hasse, H., & Lenhard, J. (2017). On the role of adjustable parameters. In J. Lenhard, & M. Carrier (Eds.), Mathematics as a tool, Boston Studies in History and Philosophy of Science, forthcoming.Google Scholar
  10. Humphreys, Paul. (2009). The philosophical novelty of computer simulation methods. Synthese, 169(3), 615–626.MathSciNetCrossRefGoogle Scholar
  11. Lenhard, J. (2016). Computer simulation. In P. Humphreys (Ed.), Oxford handbook in the philosophy of science (pp. 717–737). New York: Oxford University Press.Google Scholar
  12. Lenhard, Johannes. (2014). Disciplines, models, and computers: The path to computational quantum chemistry. Studies in History and Philosophy of Science Part A, 48, 89–96.CrossRefGoogle Scholar
  13. Lenhard, Johannes, & Winsberg, Eric. (2010). Holism, entrenchment, and the future of climate model pluralism. Studies in History and Philosophy of Modern Physics, 41, 253–262.CrossRefGoogle Scholar
  14. Mauritsen, T. et al. (2012). Tuning the climate of a global model. Journal of Advances in Modeling Earth Systems, 4.CrossRefGoogle Scholar
  15. Morrison, Margaret, & Reality, Reconstructing. (2015). Models, mathematics, and simulations. New York: Oxford University Press.Google Scholar
  16. Oberkampf, William L., & Roy, C. J. (2010). Verification and validation in scientific computing. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  17. Oberkampf, W. L., & Trucano, T. G. (2000). Validation methodology in computational fluid dynamics. In American Institute for Aeronautics and Astronautics (2000–2549).Google Scholar
  18. Pahl, G., & Beitz, W. (1984). Engineering design: A systematic approach. Revised editions in 1996, 2007. Heidelberg: Springer.Google Scholar
  19. Parker, W. (2013). Values and uncertainties in climate prediction, revisited. Studies in History and Philosophy of Science.Google Scholar
  20. Perdew, J. P., Ruzsinsky, A., Tao, J., Staroverov, V., Scuseria, G., & Csonka, G. (2005). Prescription for the design and selection of density functional approximations: More constraint satisfaction with fewer fits. The Journal of Chemical Physics, 123.Google Scholar
  21. Simon, Herbert A. (1969). The Sciences of the Artificial. Cambridge: The MIT Press.Google Scholar
  22. Smith, Leonard A. (2002). What might we learn from climate forecasts? Proceeedings of the National Academy of Sciences USA, 4(99), 2487–2492.CrossRefGoogle Scholar
  23. Solomon, S., et al. (eds.). (2007). Contribution of working group i to the fourth assessment report of the intergovernmental panel on climate change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.Google Scholar
  24. Stainforth, D. A., Downing, T. E., Washington, R., & New, M. (2007). Issues in the interpretation of climate model ensembles to inform decisions. Philosophical Transactions of the Royal Society, 365(1857), 2145–2161.CrossRefGoogle Scholar
  25. Wimsatt, William C. (2007). Re-engineering philosophy for limited beings: Piecewise approximations to reality. Cambridge, MA and London: Harvard University Press.Google Scholar
  26. Winograd, T., & Flores, F. (1991) Understanding computers and cognition. Reading, MA: Addison-Wesley.Google Scholar
  27. Winsberg, Eric. (2010). Science in the age of computer simulation. Chicago: University of Chicago Press.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.HLRS, University of StuttgartStuttgartGermany

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