Hostname: page-component-745bb68f8f-f46jp Total loading time: 0 Render date: 2025-01-27T23:29:59.591Z Has data issue: false hasContentIssue false
  • English
  • Français

Robustness Analysis

Published online by Cambridge University Press:  01 January 2022

Michael Weisberg
Get access Rights & Permissions [Opens in a new window]

Abstract

Modelers often rely on robustness analysis, the search for predictions common to several independent models. Robustness analysis has been characterized and championed by Richard Levins and William Wimsatt, who see it as central to modern theoretical practice. The practice has also been severely criticized by Steven Orzack and Elliott Sober, who claim that it is a nonempirical form of confirmation, effective only under unusual circumstances. This paper addresses Orzack and Sober's criticisms by giving a new account of robustness analysis and showing how the practice can identify robust theorems. Once the structure of robust theorems is clearly articulated, it can be shown that such theorems have a degree of confirmation, despite the lack of direct empirical evidence for their truth.

Type
Strategies of Modeling in Biology and Chemistry
Information
Philosophy of Science , Volume 73 , Issue 5: Proceedings of the 2004 Biennial Meeting of The Philosophy of Science Association. Part II: Symposia Papers. Edited by Miriam Solomon , December 2006 , pp. 730 - 742
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 am grateful for the extremely helpful comments of Patrick Forber, Peter Godfrey-Smith, Richard Levins, Ryan Muldoon, Deena Skolnick Weisberg, Brian Skyrms, Elliott Sober, C. Kenneth Waters, as well as audiences in the Biology Department at Penn, the Philosophy of Science Association, and the International Society for History, Philosophy, and Social Studies of Biology.

References

Hofbauer, J., and Sigmund, K. (1998), Evolutionary Games and Population Dynamics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Levins, R. (1966), “The Strategy of Model Building in Population Biology,” in Sober, E. (ed.), Conceptual Issues in Evolutionary Biology. 1st ed. Cambridge, MA: MIT Press, 18–27.Google Scholar
Levins, R. (1993), “A Response to Orzack and Sober: Formal Analysis and the Fluidity of Science,” Quarterly Review of Biology 68(4): 547–555.CrossRefGoogle Scholar
Lotka, A. J. (1956), Elements of Mathematical Biology. New York: Dover.Google Scholar
May, R. M. (2001), Stability and Complexity in Model Ecosystems. Landmarks in Biology ed. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Orzack, S. H., and Sober, E. (1993), “A Critical Assessment of Levins’s `The Strategy of Model Building in Population Biology' (1966),” Quarterly Review of Biology 68(4): 533–546.CrossRefGoogle Scholar
Roughgarden, J. (1979), Theory of Population Genetics and Evolutionary Ecology: An Introduction. New York: Macmillan.Google Scholar
Skyrms, B. (2000), “Stability and Explanatory Significance of Some Evolutionary Models,” Philosophy of Science 64:94–113.CrossRefGoogle Scholar
Volterra, V. (1926a), “Fluctuations in the Abundance of a Species Considered Mathematically,” Nature 118:558–560.CrossRefGoogle Scholar
Volterra, V. (1926b). “Variazioni e fluttuazioni del numero d’individui in specie animali conviventi,” Memorie Della R. Accademia Nazionale Dei Lincei 2:5–112.Google Scholar
Wimsatt, W. C. (1981), “Robustness, Reliability, and Overdetermination,” in Brewer, M. and Collins, B. (eds.), Scientific Inquiry and the Social Sciences. San Francisco: Jossey-Bass, 124–163.Google Scholar