Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-10T10:24:01.812Z Has data issue: false hasContentIssue false
  • English
  • Français

Making Models Count

Published online by Cambridge University Press:  01 January 2022

Anna Alexandrova
Get access Rights & Permissions [Opens in a new window]

Abstract

What sort of claims do scientific models make and how do these claims then underwrite empirical successes such as explanations and reliable policy interventions? In this paper I propose answers to these questions for the class of models used throughout the social and biological sciences, namely idealized deductive ones with a causal interpretation. I argue that the two main existing accounts misrepresent how these models are actually used, and propose a new account.

Type
Research Article
Information
Philosophy of Science , Volume 75 , Issue 3 , July 2008 , pp. 383 - 404
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 owe many thanks to Nancy Cartwright, Francesco Guala, Robert Northcott, Jay Odenbaugh, Julian Reiss, Till Gruene-Yanoff, Harold Kincaid, Don Ross, and many others including my teachers and friends at UC San Diego and audiences at the Philosophy Department colloquia at New York University, University of Missouri Columbia, Washington University in St Louis, Center for Philosophy of Science at Pittsburgh, London School of Economics, the 2007 meeting of the American Philosophical Association Pacific Division, and the 2007 meeting of the British Society for Philosophy of Science.

References

Alexandrova, Anna, and Northcott, Robert (forthcoming), “Progress in Economics: Lessons from the Spectrum Auctions”, in Ross, Don and Kincaid, Harold (eds.), Oxford Handbook of Philosophy of Economics. Oxford: Oxford University Press.Google Scholar
Cartwright, Nancy (1989), Nature's Capacities and Their Measurement. Oxford: Oxford University Press.Google Scholar
Cartwright, Nancy (1995), “Reply to Eels, Humphreys, and Morrison”, Reply to Eels, Humphreys, and Morrison 55:177–187.Google Scholar
Cartwright, Nancy (1999a), “Capacities”, in Davis, John, Hands, Wade, and Mäki, Uskali (eds.), The Handbook of Economic Methodology. Northampton: Elgar, 45–48.Google Scholar
Cartwright, Nancy (1999b), The Dappled World. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Cartwright, Nancy (1999c), “Vanity of Rigour in Economics”, in Discussion Paper Series, Centre for the Philosophy of Natural and Social Science, London School of Economics, 1–11.Google Scholar
Cartwright, Nancy ([2006] 2007), “Why Economic Models Are a Bad Basis for Induction to the Real World”, presentation at the Philosophy of Science Association 2006 meeting in Vancouver. Reprinted in Nancy Carthwright, Hunting Causes and Using Them. Cambridge: Cambridge University Press, 217–235.Google Scholar
Crampton, Peter (1998), “The Efficiency of the FCC Spectrum Auctions”, The Efficiency of the FCC Spectrum Auctions 41:727–736.Google Scholar
Giere, Ronald (1988), Explaining Science. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Guala, Francesco (2005), Methodology of Experimental Economics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hausman, Daniel M. (1992), The Inexact and Separate Science of Economics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hausman, Daniel M. (1994), “Paul Samuelson as Dr. Frankenstein: When Idealizations Escape and Run Amuck”, in Hamminga, Bert and Marchi, Neil de (eds.), Idealization in Economics. Poznan Studies in the Philosophy of the Sciences and the Humanities. Amsterdam: Rodopi, 229–243.Google Scholar
Humphreys, Paul (1995), “Abstract and Concrete”, Abstract and Concrete 55:157–161.Google Scholar
Kagel, John, and Levin, Daniel (1986), “The Winner's Curse Phenomenon and Public Information in Common Value Auctions”, The Winner's Curse Phenomenon and Public Information in Common Value Auctions 76:894–920.Google Scholar
Klemperer, Paul (1999), “Auction Theory: A Guide to the Literature”, Auction Theory: A Guide to the Literature 13:227–286.Google Scholar
Klemperer, Paul (2002a), “How (Not) to Run Auctions: The European 3G Telecom Auctions”, How (Not) to Run Auctions: The European 3G Telecom Auctions 46:829–845.Google Scholar
Klemperer, Paul (2002b), “What Really Matters in Auction Design”, What Really Matters in Auction Design 16:169–189.Google Scholar
Maki, Uskali (1992), “On the Method of Idealization in Economics”, On the Method of Idealization in Economics 26:319–354.Google Scholar
McMillan, John (1994), “Selling Spectrum Rights”, Selling Spectrum Rights 8:145–162.Google Scholar
McMillan, John, Rothschild, Michael, and Wilson, Robert (1997), “Introduction”, Introduction 6:425–430.Google Scholar
McMullin, Ernan (1985), “Galilean Idealization”, Galilean Idealization 16:247–273.Google Scholar
Milgrom, Paul (2004), Putting Auction Theory to Work. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Morgan, Mary S. (2002), “Model Experiments and Models in Experiments”, in Magnani, Lorenzo and Nersessian, Nancy J. (eds.), Model-Based Reasoning: Science, Technology, Values. New York: Kluwer Academic, 41–58.CrossRefGoogle Scholar
Morgan, Mary S., and Morrison, Margaret (1999), Models as Mediators. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Osborne, Martin (2003), An Introduction to Game Theory. New York: Oxford University Press.Google Scholar
Plott, Charles (1997), “Laboratory Experimental Testbeds: Application to the PCS Auction”, Laboratory Experimental Testbeds: Application to the PCS Auction 6:605–638.Google Scholar
van Fraassen, Bas (1980), The Scientific Image. Oxford: Oxford University Press.CrossRefGoogle Scholar
Weisberg, Michael (2005), “Robustness Analysis”, Robustness Analysis 73:730–742.Google Scholar