Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-09T14:24:50.142Z Has data issue: false hasContentIssue false

Holism, or the Erosion of Modularity: A Methodological Challenge for Validation

Published online by Cambridge University Press:  01 January 2022

Abstract

Modularity is a key concept in building and evaluating complex simulation models. My main claim is that in simulation modeling modularity tends to degenerate for reasons inherent to simulation methodology. The argument will proceed by analyzing the techniques of parameterization, tuning, and kludging. They are—to a certain extent—inevitable when building complex simulation models but erode modularity. As a result, the common account of validating simulations faces a major problem, namely, a problem of holism. In the conclusion, I will ask to what extent holism sets limits to validation.

Type
Modeling
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

I would like to thank Nic Fillion, Rob Moir, and the reviewers of this journal who helped to improve this article. The work was funded by German Research Foundation (DFG) priority program (SPP) 1689.

References

Agre, Philip E. 2003. “Hierarchy and History in Simon’s ‘Architecture of Complexity.’Journal of the Learning Sciences 3:413–26.Google Scholar
Brooks, Frederick P. 2010. The Design of Design. Reading, MA: Addison-Wesley.Google Scholar
Clark, Andy. 1987. “The Kludge in the Machine.” Mind and Language 2 (4): 277300..10.1111/j.1468-0017.1987.tb00123.xCrossRefGoogle Scholar
Fillion, Nicolas. 2017. “The Vindication of Computer Simulations.” In Mathematics as a Tool, ed. Lenhard, Johannes and Carrier, Martin, 137–56. Boston Studies in History and Philosophy of Science 327. Cham: Springer.Google Scholar
Foote, Brian, and Yoder, Joseph. 2000. “Big Ball of Mud.” In Pattern Languages of Program Design 4, ed. Harrison, Neil, Foote, Brian, and Rohnert, Hans. Boston: Addison-Wesley. http://laputan.org/pub/foote/mud.pdf.Google Scholar
Frigg, Roman, and Reiss, Julian. 2009. “The Philosophy of Simulation: Hot New Issues or Same Old Stew?Synthese 169 (3): 593613..10.1007/s11229-008-9438-zCrossRefGoogle Scholar
Gabriel, Richard P. 1996. Patterns of Software: Tales from the Software Community. New York: Oxford University Press.Google Scholar
Gramelsberger, Gabriele, and Feichter, Johann, eds. 2011. Climate Change and Policy: The Calculability of Climate Change and the Challenge of Uncertainty. Heidelberg: Springer.10.1007/978-3-642-17700-2CrossRefGoogle Scholar
Hasse, Hans, and Lenhard, Johannes. 2017. “On the Role of Adjustable Parameters.” In Mathematics as a Tool, ed. Lenhard, Johannes and Carrier, Martin, 93116. Boston Studies in History and Philosophy of Science 327. Cham: Springer.10.1007/978-3-319-54469-4_6CrossRefGoogle Scholar
Hourdin, Frédéric, et al. 2013. “LMDZ5B: The Atmospheric Component of The IPSL Climate Model with Revisited Parameterizations for Clouds and Convection.” Climate Dynamics 40:2193–222.CrossRefGoogle Scholar
Humphreys, Paul. 2009. “The Philosophical Novelty of Computer Simulation Methods.” Synthese 169 (3): 615–26..CrossRefGoogle Scholar
Lenhard, Johannes, and Winsberg, Eric. 2010. “Holism, Entrenchment, and the Future of Climate Model Pluralism.” Studies in History and Philosophy of Modern Physics 41:253–62.CrossRefGoogle Scholar
Mauritsen, Thorsten, et al. 2012. “Tuning the Climate of a Global Model.” Journal of Advances in Modeling Earth Systems 4 (3): M00A01. doi:10.1029/2012MS000154.CrossRefGoogle Scholar
Morrison, Margaret. 2015. Reconstructing Reality: Models, Mathematics, and Simulations. New York: Oxford University Press.10.1093/acprof:oso/9780199380275.001.0001CrossRefGoogle Scholar
Oberkampf, William L., and Roy, Christopher J.. 2010. Verification and Validation in Scientific Computing. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Oberkampf, William L., and Trucano, Timothy G.. 2000. Validation Methodology in Computational Fluid Dynamics. Washington, DC: US Department of Energy.10.2514/6.2000-2549CrossRefGoogle Scholar
Pahl, Gerhard, and Beitz, Wolfgang. 1984. Engineering Design: A Systematic Approach. Berlin: Springer.Google Scholar
Parker, Wendy. 2014. “Values and Uncertainties in Climate Prediction, Revisited.” Studies in History and Philosophy of Science 46:2430.CrossRefGoogle ScholarPubMed
Simon, Herbert A. 1969. The Sciences of the Artificial. Cambridge, MA: MIT Press.Google Scholar
Smith, Leonard A. 2002. “What Might We Learn from Climate Forecasts?Proceedings of the National Academy of Sciences of the USA 4 (99): 2487–92..Google Scholar
Solomon, Susan, Qin, Dahe, Manning, Martin, Chen, Z., Marquis, Melinda, Averyt, Kristen B., Tignor, Melinda M. B., and Miller, Henry L., eds. 2007. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.Google Scholar
Stainforth, David A., Downing, Thomas E., Washington, Richard, Lopez, Anna, and New, Mark. 2007. “Issues in the Interpretation of Climate Model Ensembles to Inform Decisions.” Philosophical Transactions of the Royal Society A 365 (1857): 2145–61..Google ScholarPubMed
Stevens, Björn, and Bony, Sandrine. 2013. “What Are Climate Models Missing?Science 340:1053–54.CrossRefGoogle ScholarPubMed
Wimsatt, William C. 2007. Re-engineering Philosophy for Limited Beings: Piecewise Approximations to Reality. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Winograd, Terry, and Flores, Fernando. 1991. Understanding Computers and Cognition. Reading, MA: Addison-Wesley.Google Scholar
Winsberg, Eric. 2010. Science in the Age of Computer Simulation. Chicago: University of Chicago Press.10.7208/chicago/9780226902050.001.0001CrossRefGoogle Scholar