A Mathematical Framework for Resilience: Dynamics, Uncertainties, Strategies and Recovery Regimes
Michel De Lara (CERMICS)

TL;DR
This paper introduces a formal mathematical framework for resilience using control theory under uncertainty, defining resilience as the ability to steer systems back to normal within acceptable regimes despite perturbations.
Contribution
It develops a novel control-theoretic approach to quantify resilience, extending existing definitions by incorporating recovery regimes and risk measures under uncertainty.
Findings
Resilience can be characterized as a controllability problem for stochastic systems.
Recovery regimes can be delineated using risk measures, linking resilience to quantitative indicators.
The framework applies to both discrete and continuous dynamical systems under uncertainty.
Abstract
Resilience is a rehashed concept in natural hazard management - resilience of cities to earthquakes, to floods, to fire, etc. In a word, a system is said to be resilient if there exists a strategy that can drive the system state back to "normal" after any perturbation. What formal flesh can we put on such a malleable notion? We propose to frame the concept of resilience in the mathematical garbs of control theory under uncertainty. Our setting covers dynamical systems both in discrete or continuous time, deterministic or subject to uncertainties. We will say that a system state is resilient if there exists an adaptive strategy such that the generated state and control paths, contingent on uncertainties, lay within an acceptable domain of random processes, called recovery regimes. We point out how such recovery regimes can be delineated thanks to so called risk measures, making the…
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Taxonomy
TopicsComplex Systems and Decision Making
