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Simplicity and Simplification in Astrophysical Modeling

Published online by Cambridge University Press:  01 January 2022

Abstract

With the ever-growing quality of observational data in astronomy, the complexity of astrophysical models has been increasing in turn. This trend raises the question: Are there still reasons to prefer simpler models if the final goal is an actual model-target comparison? I argue for two aspects in which astrophysical research may favor models having reduced complexity: first, to address the problem of determining the values of adjustable parameters and, second, to pave the way for a validation of the model based on the modeler’s understanding of the scope of the model and the critical processes on the target’s side.

Type
Data
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

I would like to thank Melissa Jacquart, who organized the symposium Models and Computer Simulations in Astrophysics. Thanks also to her and to Barry Madore for stimulating discussions and encouraging feedback on earlier drafts of the article. I am particularly grateful for the thoughtful and positive suggestions made by the Proceedings volume editor Wendy Parker and the anonymous reviewers.

References

Anderl, Sibylle. 2016. “Astronomy and Astrophysics.” In The Oxford Handbook of Philosophy of Science, ed. Humphreys, Paul, 652–70. Oxford: Oxford University Press.Google Scholar
Anderl, Sibylle, Gusdorf, Antoine, and Güsten, Rolf. 2014. “APEX Observations of Supernova Remnants.” Pt. 1, “Non-stationary Magnetohydrodynamic Shocks in W44.” Astronomy and Astrophysics 569:81100.CrossRefGoogle Scholar
Anderl, Sibylle, et al. 2016. “Probing the CO and Methanol Snow Lines in Young Protostars: Results from the CALYPSO IRAM-PdBI Survey.” Astronomy and Astrophysics 591:A3.CrossRefGoogle Scholar
Bower, Richard G., Vernon, Ian R., Goldstein, Michael, Benson, A. J., Lacey, C. G., Baugh, Carlton M., Cole, Shaun, and Frenk, C. S.. 2010. “The Parameter Space of Galaxy Formation.” Monthly Notices of the Royal Astronomical Society 407:2017–45.CrossRefGoogle Scholar
Cleland, Carol E. 2002. “Methodological and Epistemic Differences between Historical Science and Experimental Science.” Philosophy of Science 69:474–96.CrossRefGoogle Scholar
Draine, Bruce T., and McKee, Christopher F.. 1993. “Theory of Interstellar Shocks.” Annual Review of Astronomy and Astrophysics 31:373432.CrossRefGoogle Scholar
Flower, David R., and des Forêts, Guillaume Pineau. 2003. “The Influence of Grains on the Propagation and Structure of C-Type Shock Waves in Interstellar Molecular Clouds.” Monthly Notice of the Royal Astronomical Society 343:390400.CrossRefGoogle Scholar
Franklin, Allan. 1986. The Neglect of Experiment. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Goldsmith, Paul. 2015. “Commemorating Charles H. Townes (1915–2015): Remarkable Scientist and Inspiring Teacher.” Presented at the 6th Zermatt ISM Symposium.Google Scholar
Gustafsson, Magnus, Ravkilde, T., Kristensen, L. E., Cabrit, S., Field, D., and Pineau Des Forêts, G.. 2010. “3D Model of Bow Shocks.” Astronomy and Astrophysics 513:122.CrossRefGoogle Scholar
Hartmann, Stephan. 1998. “Idealization in Quantum Field Theory.” In Idealization in Contemporary Physics, ed. Shanks, N., 99122. Amsterdam: Rodopi.Google Scholar
Humphreys, Paul. 2004. Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford: Oxford University Press.CrossRefGoogle Scholar
Landau, Lew D., and Lifshitz, Evgeny. 1959. Fluid Mechanics. Oxford: Pergamon.Google Scholar
Bourlot, Le, Jaques, Guillaume Pineau des Forets, Roueff, Edith, and Schilke, Peter. 1993. “Bistability in Dark Cloud Chemistry.” Astrophysical Journal Letters 416:L87L90.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 (3): 253–62..CrossRefGoogle Scholar
Orzack, Steven H., and Sober, Elliott. 1993. “A Critical Assessment of Levins’s The Strategy of Model Building in Population Biology (1966).” Quarterly Review of Biology 68:533–46.CrossRefGoogle Scholar
Parker, Wendy. 2011. “When Climate Models Agree: The Significance of Robust Model Predictions.” Philosophy of Science 78:579600.CrossRefGoogle Scholar
Parker, Wendy 2014. “Simulation and Understanding in the Study of Weather and Climate.” Perspectives on Science 22 (3): 336–56..CrossRefGoogle Scholar
Polanyi, Michael. 1958. Personal Knowledge: Towards a Post-critical Philosophy. Chicago: University of Chicago Press.Google Scholar
Reutlinger, Alexander, Hangleiter, Dominik, and Hartmann, Stephan. 2017. “Understanding (with) Toy Models.” British Journal for the Philosophy of Science. doi:10.1093/bjps/axx005.CrossRefGoogle Scholar
Strogatz, Steven H. 1994. Nonlinear Dynamics and Chaos. Boulder, CO: Perseus.Google Scholar
Sundberg, Mikaela. 2010. “Cultures of Simulations vs. Cultures of Calculations? The Development of Simulation Practices in Meteorology and Astrophysics.” Studies in History and Philosophy of Modern Physics 41:273–81.CrossRefGoogle Scholar
Tielens, Alexander G. G. M. 2005. The Physics and Chemistry of the Interstellar Medium. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Weisberg, Michael. 2013. Simulation and Similarity: Using Models to Understand the World. Oxford: Oxford University Press.CrossRefGoogle Scholar
Winsberg, Eric. 2010. Science in the Age of Computer Simulation. Chicago: University of Chicago Press.CrossRefGoogle Scholar