Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-23T00:25:59.305Z Has data issue: false hasContentIssue false
  • English
  • Français

Effective Complexity as a Measure of Information Content

Published online by Cambridge University Press:  01 January 2022

James W. McAllister
Get access Rights & Permissions [Opens in a new window]

Abstract

Murray Gell-Mann has proposed the concept of effective complexity as a measure of information content. The effective complexity of a string of digits is defined as the algorithmic complexity of the regular component of the string. This paper argues that the effective complexity of a given string is not uniquely determined. The effective complexity of a string admitting a physical interpretation, such as an empirical data set, depends on the cognitive and practical interests of investigators. The effective complexity of a string as a purely formal construct, lacking a physical interpretation, is either close to zero, or equal to the string's algorithmic complexity, or arbitrary, depending on the auxiliary criterion chosen to pick out the regular component of the string. Because of this flaw, the concept of effective complexity is unsuitable as a measure of information content.

Type
Research Article
Information
Philosophy of Science , Volume 70 , Issue 2 , April 2003 , pp. 302 - 307
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 thank an anonymous referee of this journal for constructive comments on a previous draft.

References

Bryant, Edward (1997), Climate Process and Change. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Gell-Mann, Murray (1994), The Quark and the Jaguar: Adventures in the Simple and the Complex. New York: W.H. Freeman.Google Scholar
Gell-Mann, Murray (1995), “What Is Complexity?”, What Is Complexity? 1:16–19.Google Scholar
Gell-Mann, Murray, and Hartle, James B. (1997), “Strong Decoherence”, in Feng, Da Hsuan and Hu, Bei Lok (eds.), Quantum Classical Correspondence: The 4th Drexel Symposium on Quantum Nonintegrability. Cambridge, MA: International Press, 3–35.Google Scholar
Gell-Mann, Murray, and Lloyd, Seth (1996), “Information Measures, Effective Complexity, and Total Information”, Information Measures, Effective Complexity, and Total Information 2:44–52.Google Scholar
Li, Ming, and Vitányi, Paul M.B. (1997), An Introduction to Kolmogorov Complexity and Its Applications, 2nd ed. Berlin: Springer.CrossRefGoogle Scholar
Lloyd, Seth, and Slotine, Jean-Jacques E. (1996), “Information Theoretic Tools for Stable Adaptation and Learning”, Information Theoretic Tools for Stable Adaptation and Learning 10:499–530.Google Scholar
McAllister, James W. (1997), “Phenomena and Patterns in Data Sets”, Phenomena and Patterns in Data Sets 47:217–228.Google Scholar
Partridge, R. Bruce (1995), 3 K: The Cosmic Microwave Background Radiation. Cambridge: Cambridge University Press.CrossRefGoogle Scholar