Article
Version 1
Preserved in Portico This version is not peer-reviewed
Dynamic Complexity Measures: Definition and Calculation
Version 1
: Received: 11 January 2018 / Approved: 11 January 2018 / Online: 11 January 2018 (05:31:00 CET)
How to cite: Piqueira, J. R. C. Dynamic Complexity Measures: Definition and Calculation. Preprints 2018, 2018010099. https://doi.org/10.20944/preprints201801.0099.v1 Piqueira, J. R. C. Dynamic Complexity Measures: Definition and Calculation. Preprints 2018, 2018010099. https://doi.org/10.20944/preprints201801.0099.v1
Abstract
This work is a generalization of the Lopez-Ruiz, Mancini and Calbet (LMC); and Shiner, Davison and Landsberg (SDL) complexity measures, considering that the state of a system or process is represented by a dynamical variable during a certain time interval. As the two complexity measures are based on the calculation of informational entropy, an equivalent information source is defined and, as time passes, the individual information associated to the measured parameter is the seed to calculate instantaneous LMC and SDL measures. To show how the methodology works, an example with economic data is presented.
Keywords
complexity; disequilibrium; equilibrium; individual information; informational entropy
Subject
Physical Sciences, Thermodynamics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment