Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-26T16:14:44.985Z Has data issue: false hasContentIssue false
  • English
  • Français

Validating the Universe in a Box

Published online by Cambridge University Press:  01 January 2022

Chris Smeenk  and
Sarah C. Gallagher
Get access Rights & Permissions [Opens in a new window]

Abstract

Computer simulations of the formation and evolution of large-scale structure in the universe are integral to the enterprise of modern cosmology. Establishing the reliability of these simulations has been extremely challenging, primarily because of epistemic opacity. In this setting, robustness analysis defined by requiring converging outputs from a diverse ensemble of simulations is insufficient to determine simulation validity. We propose an alternative path of structured code validation that applies eliminative reasoning to isolate and reduce possible sources of error, a potential path that is already being explored by some cosmologists.

Type
Physical Sciences
Information
Philosophy of Science , Volume 87 , Issue 5: PSA 2018 - Proceedings of the 2018 biennial meeting of the Philosophy of Science Association. Part II Symposia Papers , December 2020 , pp. 1221 - 1233
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

†

We have benefited from questions and comments at PSA 2018, extended discussions with our cosymposiasts (in particular, Marie Gueguen), and constructive comments from two referees. This article was made possible in part through the support of grant 61048 from the John Templeton Foundation and the Natural Science and Engineering Research Council of Canada. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation.

References

Amendola, L., et al. 2018. “Cosmology and Fundamental Physics with the Euclid Satellite.” Living Reviews in Relativity 21:2.CrossRefGoogle ScholarPubMed
Bullock, J. S., and Boylan-Kolchin, M.. 2017. “Small-Scale Challenges to the ΛCDM Paradigm.” Annual Reviews of Astronomy and Astrophysics 55:343–87.CrossRefGoogle Scholar
Franklin, A. 1989. The Neglect of Experiment. Cambridge: Cambridge University Press.Google Scholar
Frisch, M. 2015. “Predictivism and Old Evidence: A Critical Look at Climate Model Tuning.” European Journal for the Philosophy of Science 5:171–90.CrossRefGoogle Scholar
Gueguen, M. 2020. “On the Robustness of Cosmological Simulations.” Philosophy of Science, in this issue.CrossRefGoogle Scholar
Guo, Q., et al. 2011. “From Dwarf Spheroidals to cD Galaxies: Simulating the Galaxy Population in a ΛCDM Cosmology.” Monthly Notices of the Royal Astronomical Society 413:101–31.CrossRefGoogle Scholar
Humphreys, P. 2009. “The Philosophical Novelty of Computer Simulation Methods.” Synthese 169 (3): 615–26.CrossRefGoogle Scholar
Lenhard, J. 2019. Calculated Surprises: A Philosophy of Computer Simulation. Oxford: Oxford University Press.CrossRefGoogle Scholar
Lenhard, J., and Winsberg, E.. 2010. “Holism, Entrenchment, and the Future of Climate Model Pluralism.” Studies in History and Philosophy of Science B 41 (3): 253–62.Google Scholar
Massimi, M. 2018. “Three Problems about Multi-Scale Modelling in Cosmology.” Studies in History and Philosophy of Science B 64:26–38.CrossRefGoogle Scholar
Parker, W. S. 2008. “Franklin, Holmes, and the Epistemology of Computer Simulation.” International Studies in the Philosophy of Science 22 (2): 165–83.CrossRefGoogle Scholar
Parker, W. S.. 2011. “When Climate Models Agree: The Significance of Robust Model Predictions.” Philosophy of Science 78 (4): 579–600.CrossRefGoogle Scholar
Ruphy, S. 2011. “Limits to Modeling: Balancing Ambition and Outcome in Astrophysics and Cosmology.” Simulation and Gaming 42 (2): 177–94.CrossRefGoogle Scholar
Schupbach, J. N. 2016. “Robustness Analysis as Explanatory Reasoning.” British Journal for the Philosophy of Science 69 (1): 275–300.CrossRefGoogle Scholar
Simon, J. D., and Geha, M.. 2007. “The Kinematics of the Ultra-Faint Milky Way Satellites: Solving the Missing Satellite Problem.” Astrophysical Journal 670:313–31.CrossRefGoogle Scholar
Springel, V., et al. 2005. “Simulations of the Formation, Evolution and Clustering of Galaxies and Quasars.” Nature 435:629–36.CrossRefGoogle ScholarPubMed
van den Bosch, F. C., and Ogiya, G.. 2018. “Dark Matter Substructure in Numerical Simulations: A Tale of Discreteness Noise, Runaway Instabilities, and Artificial Disruption.” Monthly Notices of the Royal Astronomical Society 475:4066–87.CrossRefGoogle Scholar
Weisberg, M. 2006. “Robustness Analysis.” Philosophy of Science 73 (5): 730–42.CrossRefGoogle Scholar
Winsberg, E. 2010. Science in the Age of Computer Simulation. Chicago: Cambridge University Press.CrossRefGoogle Scholar