发表机构
Theoretical Physics of Living Matter, Institute for Advanced Simulation, Forschungszentrum Jülich; University of British Columbia（尤利希研究中心先进模拟研究所活体物质理论物理; 不列颠哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过数值模拟揭示认知鸟群中信息传播与集体逃逸的机制，发现欠阻尼状态下信息近乎线性传播，转向距离与响应强度、速度和阻尼的比值成比例，并阐明碎片化与凝聚力平衡及多捕食者场景下的群体反应特征。
AI 中文摘要
信息共享、威胁感知和集体规避凸显了群体智能的优势。在此，我们基于增强惯性自旋模型的数值模拟，并引入一个显式的捕食者，研究了认知智能体鸟群的行为。鸟群中面向捕食者一侧的每个智能体感知到威胁，通过局部相互作用触发集体转向。我们表明，增强惯性自旋模型在欠阻尼状态下能维持近乎线性的信息传播，即使存在现实的群体相互作用，如局部碰撞避免和群体凝聚力以及对齐。鸟群对静止捕食者的转向距离与比值 $\Delta_p/(v_0\sqrt{\eta})$ 成比例，确定了响应强度 $\Delta_p$、鸟群速度 $v_0$ 和信息传播阻尼 $\eta$ 之间的最优平衡。当鸟群的不同部分向不同方向转向时会出现碎片化，然而，快速的信息传播可以同步这些响应，并在被追捕时保持群体凝聚力。对于两个捕食者，稳定性分析和模拟的结合揭示了鸟群与单粒子反应之间的显著对比，散射角由鸟群几何形状、捕食者位置和探测前沿的异质响应共同决定。这些结果共同表明信息传播、鸟群几何形状和动力学如何共同塑造集体逃逸，为生物鸟群和机器人群体提供了普遍原则。
英文摘要
Information sharing, threat perception, and collective evasion highlight the advantages of swarm intelligence. Here, we study the behavior of flocks of cognitive agents, based on numerical simulations of the augmented inertial spin model, with an explicit predator. Each agent on the predator-facing side of the flock perceives the threat, triggering collective turns through local interactions. We show that the augmented inertial spin model sustains nearly linear information propagation in the under-damped regime, even with realistic flocking interactions such as local collision avoidance and swarm cohesion in addition to alignment. The turning distance of the flock to a stationary predator scales with the ratio $Δ_p/(v_0\sqrtη)$, identifying an optimal balance between response strength $Δ_p$, flock speed $v_0$, and damping $η$ of information propagation. Fragmentation arises when different parts of the flock turn in different directions, however, fast information propagation can synchronize these responses and maintain flock cohesion while under pursuit. For two predators, a combination of stability analysis and simulations reveals a stark contrast between flock and single-particle reactions, with scattering angle shaped by flock geometry, predator position, and the heterogeneous responses of the detecting front. Together, these results show how information propagation, flock geometry, and dynamics together shapes collective escape, providing general principles relevant to both biological flocks and robotic swarms.
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