SKM 2023 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 13: Data Analytics of Complex Dynamical Systems (joint session DY/SOE)
SOE 13.5: Vortrag
Donnerstag, 30. März 2023, 10:30–10:45, MOL 213
Bayesian approach to anticipate critical transitions in complex systems — •Martin Heßler1,2 and Oliver Kamps2 — 1Westfälische Wilhelms-Universität Münster, 48149 Münster — 2Center for Nonlinear Science, Westfälische Wilhelms-Universität Münster, 48149 Münster
Complex systems in nature, technology and society can undergo sudden transitions between system states with very different behaviour. In order to avoid undesired consequences of these tipping events, statistical measures have been proposed as leading indicators. They can give a hint of an ongoing bifurcation-induced (B-tipping) destabilization process. However, we present an alternative approach that is open-source available and more robust under numerous aspects. It assumes the dynamical system to be described by a Langevin equation. Starting from this stochastic description, we combine MCMC sampling, rolling window methods and Bayesian reasoning to derive the drift slope as an alternative early warning sign including credibility bands which make it easier to distinguish significant leading indicator trends prior to B-tipping. Furthermore, our approach provides information about an increasing noise level in a multi-stable system. This is an important information related to the Kramers escape rate of a noise-induced tipping (N-tipping) event. We show some results and discuss the method's potential to be applied in N-tipping scenarios and under more complex conditions like correlated non-Markovian or multiplicative noise. Finally, possible limitations and tasks of future research are mentioned.