• Corpus ID: 3021835

Statistical measures of complexity for strongly interacting systems

@article{Sol1999StatisticalMO,
  title={Statistical measures of complexity for strongly interacting systems},
  author={Ricard V. Sol{\'e} and Bartolo Luque},
  journal={Research Papers in Economics},
  year={1999}
}
  • R. Solé, B. Luque
  • Published 1 August 1999
  • Mathematics
  • Research Papers in Economics
In recent studies, new measures of complexity for nonlinear systems have been proposed based on probabilistic grounds, as the LMC measure (Phys. Lett. A 209 (1995) 321). All these measures share an intuitive consideration: complexity seems to emerge in nature close to instability points, as for example the phase transition points characteristic of critical phenomena. Here we discuss these measures and their reliability for detecting complexity close to critical points in complex systems… 
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References

SHOWING 1-10 OF 39 REFERENCES
Chaos
or "What a lie", or "How could I have said so"? or demanded at once that she should be sent for. Yet, he never on one single occasion, before others, spoke to Mrs. Cox on the subject. The only…
Complexity Metaphors, Models, and Reality
* Fundamental Concepts * Examples of Complex Adaptive Systems * Nonadaptive Systems, Scaling, Self-Similarity, and Measures of Complexity * General Discussion * Afterwords
Phys
  • Let. A 223 (1996) 348; D. P. Feldman and J. P. Crutchfield, Phys. Lett. A, 238
  • 1998
Matt Davison and P
  • T. Landsberg Phys. Rev E 59(2)
  • 1999
Thermodynamics of chaotic systems
ORIGINS OF ORDER
Int. J. Theor. Phys
  • Int. J. Theor. Phys
  • 1986
Phys. Lett. A
  • Phys. Lett. A
  • 1995
Phys. Lett. A
  • Phys. Lett. A
  • 1995
J. Phys. A: Math. Gen
  • J. Phys. A: Math. Gen
  • 1988
...
...