Publication type: Scientific Report
Authors: Rui A. P. Perdigão, Carlos A. L. Pires, Julia Hall
Date: 2018, January 2nd
Title: Nonlinear Dynamics and Predictability of Non-Ergodic Synergistic Emergence in Complex Coevolutionary Systems
Indexed: Yes (Crossref)
Cite as: Perdigão R.A.P., Pires C.A.L. & Hall J. (2018): Nonlinear Dynamics and Predictability of Non-Ergodic Synergistic Emergence in Complex Coevolutionary Systems. https://doi.org/10.46337/180102.
Methodological Keywords: Information Physics, Information Theory, Complex Systems, Dynamical Systems, Mathematical Physics, Non-Ergodic, Chaos, Entropy, Emergence, Synergy, Coevolution, Causation.
Applied Keywords: Big Data Analytics, Artificial Intelligence, Machine Learning, Earth System Dynamics, Hydrologic Dynamics, Hydrologic Analytics, Geomorphology, Ecosystem Dynamics, Socio-Environmental Dynamics.
A novel mathematical physics theory is hereby introduced for the nonlinear diagnostic and prognostic evaluation of complex coevolutionary systems and their predictability. Special emphasis is given to unveiling general principles underlying elusive structurally transient dynamics, non-recurring critical transitions and extreme events. With broad applications in mind, we position ourselves in a generic non-ergodic and non- periodic coevolutionary setting, wherein the traditional assumptions taken in the study of stochastic-dynamical systems do not necessarily hold. The theory enables the dynamic emergence of new processes to be predicted before incorporating any a priori knowledge about their existence, paving the way for the prognosis of unprecedented dynamics through synergistic innovations. This is particularly relevant when predicting the multiscale emergence of critical non-periodic phenomena such as high-profile hydro-meteorological extremes.
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