Resilience of coupled systems under deep uncertainty and dynamic complexity: An integrative literature review
Jannie Coenen, Vítor Vasconcelos, Heiman Wertheim, Marcel Olde Rikkert, Sophie Hadjisotiriou, Vittorio Nespeca, Tom Oreel, Rick Quax, Etiënne Rouwette, Vincent Marchau, Hubert Korzilius
- Year
- 2025
- Access
- Open access
Abstract
Resilience in coupled systems is increasingly critical in addressing global challenges such as climate change and pandemics. These systems show unpredictable behaviour due to dynamic complexity and deep uncertainty across spatiotemporal scales. Despite growing interest, few studies systematically integrate both concepts when assessing resilience. This paper conducts an integrative review of 102 English-language publications to identify gaps in current approaches. Findings reveal that most papers address lower levels of uncertainty and rarely consider dynamic complexity and deep uncertainty simultaneously, which limits the effectiveness of resilience strategies. To advance systems research, we propose a conceptual framework and practical tools to support researchers and decision-makers in evaluating and improving resilience. The paper also outlines future research directions for more robust, adaptive, and integrative resilience assessments.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992