涌现行为对通信延迟具有鲁棒性，但代价是系统演化更缓慢
Emergent Behavior Is Robust to Communication Delays at the Cost of Slower System Evolution
浏览论文内容
中文总结 AI 辅助
该研究针对异质智能体集体行为受通信延迟影响的问题，通过减缓智能体动力学保证涌现行为的鲁棒性，明确延迟对向量场的缩放作用，发现延迟诱导的减速或为扩散耦合的普遍特征。
中文摘要 AI 辅助
已有研究表明，异质智能体间的强耦合可实现实用同步，形成由涌现动力学支配的集体行为。然而，由于高增益方法通常对延迟敏感，存在通信延迟时涌现行为是否仍能保持仍是未解决的问题。为解决该问题，我们转而减缓智能体动力学，在慢时间尺度上复现强耦合的同步机制，从而保证存在任意恒定通信延迟时集体行为仍能涌现。但这会产生代价：延迟会诱导类导数项，进而缩放涌现动力学的向量场，缩放程度可明确表征为耦合权重与延迟时长的函数。我们进一步指出，这种延迟诱导的减速具有独立研究价值，在合适假设下可能是扩散耦合的普遍特征。
英文摘要
Previous works have shown that strong coupling among heterogeneous agents enforces practical synchronization, leading to collective behavior governed by emergent dynamics. However, it remains an open question whether emergent behavior persists in the presence of communication delays, as high-gain methods are typically sensitive to delays. To address this problem, we instead slow down the agent dynamics, reproducing the synchronization mechanism of strong coupling on a slow time scale. As a result, the emergence of collective behavior is guaranteed despite arbitrary constant communication delays. This, however, comes at a cost: delays induce a derivative-like term, thereby scaling down the vector field of the emergent dynamics. The amount of the scaling is explicitly characterized as a function of the coupling weights and the delay lengths. We further suggest that this delay-induced slowdown, which is of independent interest, may be a general feature of diffusive coupling under suitable assumptions.
