标题： 干预的悖论：自适应多角色协调网络中的弹性 显示英文标题

标题： The Paradox of Intervention: Resilience in Adaptive Multi-Role Coordination Networks

摘要： 复杂适应网络表现出显著的韧性，这是由结构（相互作用）和功能（状态）之间的动态相互作用驱动的。 尽管静态网络分析提供了有价值的见解，但理解在外部干预下结构和功能如何共同进化，对于解释系统级适应至关重要。 使用一个独特的秘密犯罪网络数据集，我们将实证观察与计算建模相结合，以测试各种干预措施对网络适应的影响。 我们的分析考察了具有专门角色的网络如何适应并形成涌现结构，以优化成本-收益权衡。 我们发现，涌现的稀疏连接网络表现出更大的韧性，揭示了一个安全与效率之间的权衡。 值得注意的是，干预可能会引发“犯罪隐蔽性增强”效应，即在网络可见性降低的情况下，犯罪活动反而增加。 虽然节点隔离会分裂网络，但它会加强剩余的活跃关系。 相比之下，关闭节点（类似于社会再融入）可能会无意中提高犯罪协调，增加活动或连通性。 失败的干预措施通常会导致功能暂时激增，然后恢复到基线水平。 令人惊讶的是，刺激连通性会破坏网络。 有效的干预需要精确校准节点角色、连接类型和外部条件。 这些发现挑战了在不同领域中的复杂适应系统中关于连通性和干预效果的传统假设。 显示英文摘要

摘要： Complex adaptive networks exhibit remarkable resilience, driven by the dynamic interplay of structure (interactions) and function (state). While static-network analyses offer valuable insights, understanding how structure and function co-evolve under external interventions is critical for explaining system-level adaptation. Using a unique dataset of clandestine criminal networks, we combine empirical observations with computational modeling to test the impact of various interventions on network adaptation. Our analysis examines how networks with specialized roles adapt and form emergent structures to optimize cost-benefit trade-offs. We find that emergent sparsely connected networks exhibit greater resilience, revealing a security-efficiency trade-off. Notably, interventions can trigger a "criminal opacity amplification" effect, where criminal activity increases despite reduced network visibility. While node isolation fragments networks, it strengthens remaining active ties. In contrast, deactivating nodes (analogous to social reintegration) can unintentionally boost criminal coordination, increasing activity or connectivity. Failed interventions often lead to temporary functional surges before reverting to baseline. Surprisingly, stimulating connectivity destabilizes networks. Effective interventions require precise calibration to node roles, connection types, and external conditions. These findings challenge conventional assumptions about connectivity and intervention efficacy in complex adaptive systems across diverse domains.