Signal Paper distinguishes two dynamics of systemic breakdown and recovery, calling for data-driven resilience models

Summary

A paper on resilience, defined as a system's capacity to withstand shocks and recover from them, distinguishes between two types of system dynamics: one in which phases of normalcy and phases of rapid breakdown followed by slow recovery can be separated, and another that applies to volatile organizations in which such phases are intertwined. It argues that breakdown is often self-inflicted, with situation awareness impaired by psychological mechanisms that produce incorrect expectations about societal dynamics, and that positive feedback can amplify the failure of a few elements into a broader failure cascade. The paper also notes that positive feedback can be harnessed to enable recovery rather than only driving collapse. In volatile systems, it argues resilience must be understood as an emergent property arising from the interaction of agents, which requires a data-driven approach informed by repositories, knowledge graphs, or artificial-intelligence tools to build agent-based models. The author concludes that maximizing performance frequently comes at the expense of resilience, and that second-order solutions aimed at transforming a system are more promising than attempts to reconstruct past conditions.