Preprint Article Version 1 This version is not peer-reviewed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 (doi: 10.20944/preprints201801.0099.v1). Piqueira, J.R.C. Dynamic Complexity Measures: Definition and Calculation. Preprints 2018, 2018010099 (doi: 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.

Subject Areas

complexity; disequilibrium; equilibrium; individual information; informational entropy

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