跟随者-领导者系统中不同程度的领导力对集体导航的影响
The impact of different degrees of leadership on collective navigation in follower-leader systems
AI总结：
本研究扩展非局部双曲模型，探讨跟随者-领导者系统中冷漠型、观察型和可说服型三种领导力对集体导航的影响，发现最优领导策略取决于个体间的对齐程度。
摘要：
在动物和细胞群体中，领导者的存在通常是集体迁移过程成功的基础，我们将这种成功表征为群体保持朝着目标持续移动的内聚构型。我们扩展了最近针对跟随者-领导者系统的非局部双曲模型，以研究不同程度的领导力。具体而言，我们考虑三个层次的领导力：冷漠型领导者，他们不会根据跟随者改变自己的运动；观察型领导者，他们试图与跟随者保持连接，但不允许跟随者影响其期望的对齐；可说服型领导者，他们将到达某个目标的尝试与所有邻居（包括跟随者和领导者）的对齐相结合。我们结合分析和数值模拟，研究在何种条件下每种程度的领导力能够实现向目的地的成功集体移动。我们发现，冷漠型领导者的策略只能在短时间内实现内聚且目标导向的迁移。相反，观察型和可说服型领导者提供了稳健的引导，这表明最优的领导者行为取决于迁移个体之间的连接：如果对齐程度较低，跟随者对领导者产生更大影响有利于成功引导；否则，这可能是有害的，并可能产生各种不成功的群集动态。
英文摘要：
In both animal and cell populations, the presence of leaders often underlies the success of collective migration processes, which we characterise by a group maintaining a cohesive configuration that consistently moves toward a target. We extend a recent non-local hyperbolic model for follower-leader systems to investigate different degrees of leadership. Specifically, we consider three levels of leadership: indifferent leaders, who do not alter their movement according to followers; observant leaders, who attempt to remain connected with the followers, but do not allow followers to affect their desired alignment; and persuadable leaders, who integrate their attempt to reach some target with the alignment of all neighbours, both followers and leaders. A combination of analysis and numerical simulations is used to investigate under which conditions each degree of leadership allows successful collective movement to a destination. We find that the indifferent leaders' strategy can result in a cohesive and target-directed migration only for short times. Observant and persuadable leaders instead provide robust guidance, showing that the optimal leader behavior depends on the connection between the migrating individuals: if alignment is low, greater follower influence on leaders is beneficial for successful guidance; otherwise, it can be detrimental and may generate various unsuccessful swarming dynamics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
