AI 中文总结
研究恢复力，区分两种系统动态，指出崩溃常自我造成且正反馈有正负作用。认为易变系统中恢复力是主体交互的涌现属性，需数据驱动方法，模型显示恢复力是稳健性与适应性的妥协，二阶解决方案更有前景。
AI 中文摘要
恢复力指系统承受冲击并从中恢复的能力。我们区分了两种不同类型的动态。第一种是正常阶段与快速崩溃随后缓慢恢复阶段可分离；第二种适用于这些阶段相互交织的易变组织。崩溃常是自我造成的，心理机制会削弱态势感知，通过正反馈少数元素的失败会放大成失败级联。但正反馈也可用于促进恢复。在易变系统中，恢复力是主体交互产生的涌现属性，需要数据驱动方法为基于主体的模型提供信息。此类模型表明恢复力是稳健性和适应性之间的妥协。最大化性能常以恢复力为代价，旨在转变系统的二阶解决方案比重建过去条件更有前景。
英文摘要
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.
