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Universality and Modeling Limiting Behaviors

Published online by Cambridge University Press:  01 January 2022

Collin Rice
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Abstract

Most attempts to justify the use of idealized models to explain appeal to the accuracy of the model with respect to difference-making causes. In this article, I argue for an alternative way to justify using idealized models to explain that appeals to universality classes. In support of this view, I show that scientific modelers seeking to explain stable limiting behaviors often explicitly appeal to universality classes in order to justify their use of idealized models to explain.

Type
Models and Modeling
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. 829 - 840
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

†

Thanks to Julia Bursten, Robert Batterman, Chris Pincock, Jenn Jhun, and the audience of our symposium at PSA 2018 for helpful discussions and feedback. I am also grateful to two anonymous reviewers whose comments helped improve the final version.

References

Alexandrowicz, Z. 1980. “Critically Branched Chains and Percolation Clusters.” Physics Letters A 80:284–86.Google Scholar
Ariew, A., Rice, C., and Rohwer, Y.. 2015. “Autonomous Statistical Explanations and Natural Selection.” British Journal for the Philosophy of Science 66 (3): 635–58.CrossRefGoogle Scholar
Batterman, R. W. 2002. The Devil in the Details: Asymptotic Reasoning in Explanation, Reduction, and Emergence. Oxford: Oxford University Press.Google Scholar
Batterman, R. W., and Rice, C.. 2014. “Minimal Model Explanations.” Philosophy of Science 81 (3): 349–76.CrossRefGoogle Scholar
Benguigui, L. 1995. “A New Aggregation Model: Application to Town Growth.” Physica A 219:13–26.CrossRefGoogle Scholar
Bokulich, A. 2011. “How Scientific Models Can Explain.” Synthese 180:33–45.CrossRefGoogle Scholar
Bokulich, A.. 2012. “Distinguishing Explanatory from Nonexplanatory Fictions.” Philosophy of Science 79:725–37.CrossRefGoogle Scholar
Bonachela, J. A., Nadell, C. D., Xavier, J. B., and Levin, S. A.. 2011. “Universality in Bacterial Colonies.” Journal of Statistical Physics 144 (2): 303–15.CrossRefGoogle Scholar
Cartwright, N. 1983. How the Laws of Physics Lie. Oxford: Oxford University Press.CrossRefGoogle Scholar
Corwin, I. 2016. “Kardar-Parisis-Zhang Universality.” Notices of the American Mathematical Society 63 (3): 230–39.CrossRefGoogle Scholar
Craver, C. 2006. “When Mechanistic Models Explain.” Synthese 153:355–76.CrossRefGoogle Scholar
Eden, M. 1961. “A Two-Dimensional Growth Process.” In 4th Berkeley Symposium on Mathematical Statistics and Probability, 223–39. Berkeley: University of California Press.Google Scholar
Elgin, M., and Sober, E.. 2002. “Cartwright on Explanation and Idealization.” Erkenntnis 57:441–50.CrossRefGoogle Scholar
Frigg, R. 2010. “Models and Fiction.” Synthese 172:251–68.CrossRefGoogle Scholar
Gisiger, T. 2001. “Scale Invariance in Biology: Coincidence or Evidence of a Universal Mechanism?” Biological Review 76:161–209.CrossRefGoogle ScholarPubMed
Glennan, S. 2017. The New Mechanical Philosophy. Oxford: Oxford University Press.CrossRefGoogle Scholar
Goldenfeld, N., and Kadanoff, L. P.. 1999. “Simple Lessons from Complexity.” Science 284:87–89.CrossRefGoogle ScholarPubMed
Hermann, H. J. 1986. “Geometrical Cluster Growth Models and Kinetic Gelation.” Physics Reports 136 (3): 153–227.CrossRefGoogle Scholar
Kadanoff, L. P. 2013. “Theories of Matter: Infinities and Renormalization.” In The Oxford Handbook of Philosophy of Physics, ed. Batterman, Robert, 141–88. Oxford: Oxford University Press.Google Scholar
Kaplan, D. M., and Craver, C. F.. 2011. “The Explanatory Force of Dynamical and Mathematical Models in Neuroscience: A Mechanistic Perspective.” Philosophy of Science 78:601–27.CrossRefGoogle Scholar
Kardar, M., Parisi, G., and Zhang, Y. C.. 1986. “Dynamic Scaling of Growth Interfaces.” Physics Review Letters 56 (9): 889–92.CrossRefGoogle Scholar
Longino, H. 2013. Studying Human Behavior: How Scientists Investigate Aggression and Sexuality. Chicago: Cambridge University Press.CrossRefGoogle Scholar
Morrison, M. 2015. Reconstruction Reality: Models, Mathematics, and Simulations. Oxford: Oxford University Press.CrossRefGoogle Scholar
Parunak, H. V. D., Brueckner, S., and Savit, R.. 2004. “Universality in Multi-Agent Systems.” In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 930–37. New York: Association for Computing Machinery.Google Scholar
Potochnik, A. 2017. Idealization and the Aims of Science. Chicago: Cambridge University Press.CrossRefGoogle Scholar
Rice, C. 2015. “Moving beyond Causes: Optimality Models and Scientific Explanation.” Noûs 49 (3): 589–615.CrossRefGoogle Scholar
Rice, C.. 2018. “Idealized Models, Holistic Distortions and Universality.” Synthese 195 (6): 2795–819.CrossRefGoogle Scholar
Strevens, M. 2008. Depth: An Account of Scientific Explanation. Cambridge, MA: Harvard University Press.Google Scholar
Weisberg, M. 2013. Simulation and Similarity. New York: Oxford University Press.CrossRefGoogle Scholar
Woodward, J. 2003. Making Things Happen: A Theory of Causal Explanation. Oxford: Oxford University Press.Google Scholar
Zhang, J., Zhang, Y. C., Alst⊘m, P., and Levinsen, M. T.. 1992. “Modeling Forest Fire by a Paper-Burning Experiment, a Realization of the Interface Growth Mechanism.” Physica A 189:383–89.Google Scholar