A mathematical framework for critical transitions: bifurcations, fast-slow systems and stochastic dynamics

TL;DR

This paper reviews mathematical theories for understanding critical transitions in dynamical systems, focusing on bifurcations, fast-slow dynamics, and stochastic effects, and discusses early-warning signs like variance and autocorrelation.

Contribution

It provides a comprehensive overview of how classical and modern mathematical theories can predict and analyze critical transitions in various complex systems.

Findings

Early-warning signs such as increasing variance can be analytically and numerically validated.

Fast-slow systems theory offers a natural framework for defining and studying critical transitions.

Stochastic effects near bifurcations can be analyzed using sample path techniques and PDEs.

Abstract

Bifurcations can cause dynamical systems with slowly varying parameters to transition to far-away attractors. The terms ``critical transition'' or ``tipping point'' have been used to describe this situation. Critical transitions have been observed in an astonishingly diverse set of applications from ecosystems and climate change to medicine and finance. The main goal of this paper is to give an overview which standard mathematical theories can be applied to critical transitions. We shall focus on early-warning signs that have been suggested to predict critical transitions and point out what mathematical theory can provide in this context. Starting from classical bifurcation theory and incorporating multiple time scale dynamics one can give a detailed analysis of local bifurcations that induce critical transitions. We suggest that the mathematical theory of fast-slow systems provides a natural definition of critical transitions. Since noise often plays a crucial role near critical transitions the next step is to consider stochastic fast-slow systems. The interplay between sample path techniques, partial differential equations and random dynamical systems is highlighted. Each viewpoint provides potential early-warning signs for critical transitions. Since increasing variance has been suggested as an early-warning sign we examine it in the context of normal forms analytically, numerically and geometrically; we also consider autocorrelation numerically. Hence we demonstrate the applicability of early-warning signs for generic models. We end with suggestions for future directions of the theory.