Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-06T09:19:27.961Z Has data issue: false hasContentIssue false

How Are the Sciences of Complex Systems Possible?

Published online by Cambridge University Press:  01 January 2022

Abstract

To understand the behavior of a complex system, one must understand the interactions among its parts. Doing so is difficult for nondecomposable systems, in which the interactions strongly influence the short term behavior of the parts. Science's principal tool for dealing with nondecomposable systems is a variety of probabilistic analysis that I call EPA. I show that EPA's power derives from an assumption that appears to be false of nondecomposable complex systems, in virtue of their very nondecomposability. Yet EPA is extremely successful. I aim to find an interpretation of EPA's assumption that is consistent with, indeed that explains, its success.

Type
Research Article
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.)

References

Auyang, Sunny Y. (1998), Foundations of Complex-System Theories in Economics, Evolutionary Biology, and Statistical Physics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Bechtel, William, and Richardson, Robert C. (1993), Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research. Princeton, NJ: Princeton University Press.Google Scholar
Garber, Elizabeth, Brush, Stephen G., and Everitt, C. W. F. (eds.) (1995), Maxwell on Heat and Statistical Mechanics. Bethlehem, PA: Lehigh University Press.Google Scholar
Giere, Ronald (1973), “Objective Single Case Probabilities and the Foundation of Statistics”, in Suppes, P., Henkin, L., Moisil, G. C., and Joja, A., (eds.), Logic, Methodology and Philosophy of Science IV: Proceedings of the Fourth International Congress for Logic, Methodology and Philosophy of Science, Bucharest, 1971. Amsterdam: North-Holland.Google Scholar
Hopf, Eberhard (1934), “On Causality, Statistics and Probability”, On Causality, Statistics and Probability 13:51102.Google Scholar
Loeb, Leonard B. (1934), The Kinetic Theory of Gases. 2nd ed. New York: McGraw Hill.Google Scholar
Ornstein, Donald S., and Weiss, Benjamin (1991), “Statistical Properties of Chaotic Systems”, Statistical Properties of Chaotic Systems 24:11116.Google Scholar
Poincaré, Henri ([1896] 1912), Calcul des Probabilités. 2nd ed. Paris: Gauthier-Villars. First edition 1896.Google Scholar
Reichenbach, Hans (1949), The Theory of Probability. Berkeley: University of California Press.Google Scholar
Roughgarden, Jonathan (1979), Theory of Population Genetics and Evolutionary Ecology. New York: Macmillan.Google Scholar
Salmon, Wesley (1984), Explanation and the Causal Structure of the World. Princeton, NJ: Princeton University Press.Google Scholar
Simon, Herbert A. ([1969] 1996), The Sciences of the Artificial. 3rd ed. Cambridge, MA: MIT Press. First edition 1969.Google Scholar
Sklar, Lawrence (1993), Physics and Chance. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Sober, Elliott (1984), The Nature of Selection. Cambridge, MA: MIT Press.Google Scholar
Strevens, Michael (2003), Bigger than Chaos: Understanding Complexity through Probability. Cambridge, MA: Harvard University Press.Google Scholar
Wimsatt, William C. (1986), “Forms of Aggregativity”, in Donagan, A., Perovich, A. N., and Wedin, M. V. (eds.), Human Nature and Natural Knowledge. Dordrecht: D. Reidel.Google Scholar