Resilience: Understand Breakdown, Foster Recovery, and Choose the Right Perspective

2026-07-28

AI Summary

This study addresses the vulnerability of complex systems to self-induced collapse under shocks, a challenge inadequately met by conventional approaches that struggle to balance robustness and adaptability. The work distinguishes two dynamic regimes—phase-separated systems and highly volatile, interwoven systems—and conceptualizes resilience as an emergent property arising from multi-agent interactions. Rather than advocating mere restoration to prior states, it proposes systemic transformation to enhance recovery capacity. Methodologically, the research integrates data-driven multi-agent modeling, knowledge graphs, and artificial intelligence tools. Large-scale simulations reveal that optimizing for peak performance often undermines resilience, whereas second-order interventions leveraging positive feedback mechanisms can effectively reconfigure system architecture, thereby substantially strengthening overall resilience.

Abstract

Resilience denotes the capacity of a system to withstand shocks and to recover from them. We distinguish between two different types of dynamics. The first allows for a separation between phases of normalcy and phases of rapid breakdown followed by slow recovery. The second applies to volatile organizations in which such phases are intertwined. Breakdown is often self-inflicted. Situation awareness is impaired by psychological mechanisms that lead to incorrect expectations regarding societal dynamics. Through positive feedback, the failure of a few elements is amplified into a failure cascade. However, positive feedback can also be harnessed to enable recovery. In volatile systems, resilience must be understood as an emergent property arising from the interaction of agents. This necessitates a data-driven approach to inform agent-based models, drawing on repositories, knowledge graphs, or tools from artificial intelligence. Such models help demonstrate that resilience represents a compromise between robustness and adaptivity. Maximizing performance frequently comes at the expense of resilience, and second-order solutions aimed at transforming the system prove more promising than attempts to reconstruct past conditions.

Problem

• resilience
• breakdown
• recovery
• system dynamics
• agent-based modeling

Innovation

• resilience
• agent-based modeling
• data-driven approach
• emergent property
• second-order solutions