发表机构
Universitat Politècnica de Catalunya; Universitat de Barcelona（加泰罗尼亚理工大学; 巴塞罗那大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述了统计物理学框架下鱼群集体运动的研究进展，揭示异质非互惠相互作用驱动转向级联与类临界波动共存，为活体集体信息处理提供模型。
AI 中文摘要
我们回顾了近期将鱼群行为纳入统计物理学框架的理论与实证进展，重点强调异质性和时间依赖性相互作用如何主导集体运动。基于Vicsek型模型向复杂加权社交网络的扩展，我们表明拓扑结构和连接强度会定性改变集群稳定性和临界阈值，而经验权重通常降低全局对齐。高分辨率轨迹推断揭示了选择性、非互惠的响应：个体优先关注速度更快的邻居，产生瞬时的、由速度诱导的领导现象，而非固定层级。在介观尺度上，鱼群表现出雪崩式的转向级联，具有无标度的规模与持续时间统计、余震聚集以及短记忆的Omori型时间衰减。综合来看，这些发现支持一个统一图景，即集体有序与类临界波动共存：对齐提供稳定性，而近临界变异性保持响应性。这一综合凸显了鱼群作为研究活体集体如何跨尺度组织、传递和处理信息的易处理实验模型。
英文摘要
We review recent theoretical and empirical advances that recast fish schooling within a statistical-physics framework, emphasizing how heterogeneous and time-dependent interactions govern collective motion. Building on extensions of Vicsek-type models to complex and weighted social networks, we show that topology and link strength qualitatively alter flocking stability and critical thresholds, with empirical weights typically reducing global alignment. High-resolution trajectory inference reveals selective, nonreciprocal responses: individuals preferentially attend to faster neighbors, producing transient, speed-induced leadership rather than fixed hierarchies. At the mesoscopic scale, schools exhibit avalanche-like turning cascades with scale-free size and duration statistics, aftershock clustering, and an Omori-type temporal decay with a short memory. Together, these findings support a unified picture in which collective order and critical-like fluctuations coexist: alignment provides stability while near-critical variability preserves responsiveness. This synthesis highlights fish schools as a tractable experimental model for how living collectives organize, transmit and process information across scales.
CommentsTo appear in "Order, Disorder and Criticality. Advanced Problems of Phase Transitions and Complex Systems". Yu. Holovatch (editor). vol. 9 World Scientific, Singapore, 2026
