Measures of Statistical Complexity: Why?

David P. Feldman and James P. Crutchfield

Working Papers from Santa Fe Institute

Abstract: We review several statistical complexity measures proposed over the last decade and a half as general indicators of structure or correlation. Recently, L\`opez-Ruiz, Mancini, and Calbet [Phys. Lett. A 209 (1995) 321] introduced another measure of statistical complexity $C_{\rm LMC}$ that, like others, satisfies the ``boundary conditions'' of vanishing in the extreme ordered and disordered limits. We examine some properties of $C_{\rm LMC}$ and find that it is neither an intensive nor an extensive thermodynamic variable. It depends nonlinearly on system size and vanishes exponentially in the thermodynamic limit for all one-dimensional finite-range spin systems. We propose a simple alteration of $C_{\rm LMC}$ that renders it extensive. however, this remedy results in a quantity that is a trivial function of the entropy density and hence of no use as a measure of structure or memory. We conclude by suggesting that a useful ``statistical complexity'' must not only obey the ordered-random bounary conditions of vanishing, it must also be defined in a setting that gives a clear interpretation to what structures are quantified.

Keywords: statistical complexity; excess entropy; mutual information; Shannon entropy; Kolmogorov complexity (search for similar items in EconPapers)
Date: 1997-07
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