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Models: The Blueprints for Laws

Published online by Cambridge University Press:  01 April 2022

Nancy Cartwright
Nancy Cartwright*
Affiliation:
London School of Economics and Political Science
*
†Department of Philosophy, Logic and Scientific Method, London School of Economics and Political Science, Houghton Street, London WC2 2AE, UK.
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Abstract

In this paper the claim that laws of nature are to be understood as claims about what necessarily or reliably happens is disputed. Laws can characterize what happens in a reliable way, but they do not do this easily. We do not have laws for everything occurring in the world, but only for those situations where what happens in nature is represented by a model: models are blueprints for nomological machines, which in turn give rise to laws. An example from economics shows, in particular, how we use—and how we need to use—models to get probabilistic laws.

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Footnotes

‡

Research for this paper was supported by the project “Modelling in Physics and Economics” at the LSE. Towfa Shomar was a great aid in both the research and production of the paper.

References

Cartwright, N. (1983), How the Laws of Physics Lie. Oxford: Oxford University Press.10.1093/0198247044.001.0001CrossRefGoogle Scholar
Cartwright, N. (1994), “Fundamentalism versus the Patchwork of Laws”, Proceedings of the Aristotelian Society 94: 279–292.CrossRefGoogle Scholar
Cartwright, N. (1995), “Ceteris Paribus Laws and Socio-Economic Machines”, The Monist 78: 276–294.10.5840/monist19957831CrossRefGoogle Scholar
Cartwright, N. (1996), ‘What is a Causal Structure?‘, in Vaughn R. McKim and Stephen P. Turner (eds.), Causality in Crisis? Statistical Methods and the Search for Causal Knowledge in the Social Sciences. Notre Dame: University of Notre Dame Press, pp. 341–355.Google Scholar
Cartwright, N. (1997), “Where Do Laws of Nature Come From?”, Dialectica 1 (forthcoming).Google Scholar
Giere, R. (1988), Explaining Science: A Cognitive Approach. Chicago: Chicago University Press.CrossRefGoogle Scholar
Hacking, I. (1965), Logic of Statistical Inference. Cambridge: Cambridge University Press.10.1017/CBO9781316534960CrossRefGoogle Scholar
Kyburg, H. (1969), Probability Theory. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
McAllister, H. E. (1975), Elements of Business and Economic Statistics. New York: Wiley.Google Scholar
Mill, J. S. (1843; 1856), The System of Logic, 2 vols. London: John W. Parker and Son.Google Scholar
Mulholland, H. and Jones, C.R. (1968), Fundamentals of Statistics. London: Butterworth.10.1007/978-1-4899-6507-3CrossRefGoogle Scholar
Pissarides, C. (1992), “Loss of Skill During Unemployment and the Persistence of Unemployment Shocks”, Quarterly Journal of Economics 107: 1371–1391.10.2307/2118392CrossRefGoogle Scholar
Robinson, E. A. (1985), Probability Theory and Applications. Boston: International Human Resources Development Corporation.CrossRefGoogle Scholar
Salmon, W.C. (1971), with contributions by Richard C. Jeffrey and James G. Greene, Statistical Explanation and Statistical Relevance. Pittsburgh: University of Pittsburgh Press.10.2307/j.ctt6wrd9pCrossRefGoogle Scholar