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The guiding principle in collective motion

The guiding principle in collective motion is the emergence of coordinated behavior or patterns from the interactions of individual entities within a group. The collective motion refers to the collective behavior exhibited by abundance of entities, such as the flocking of birds, schools of fishes, or herds of animals, where individuals move together in a coordinated manner. One of the key principles underlying collective motion is known as "self-organization." Self-organization mainly refers to the spontaneous emergence of order and organization in a system without external control or coordination. In the context of collective motion, self-organization occurs when the interactions among individuals give rise to collective behavior patterns, such as flocking or schooling (see the Video).

Prof. Huepe's recent Nature Physics article “Sheep lead the way” is in the “News & Views” section.

Physicists are intrigued by collective animal motion because it offers new statistical and dynamical systems beyond traditional physical forces. The collective behavior of animal groups, such as sheep and bird flocks, can increase group fitness through information pooling. The Vicsek model, which describes bird flocks, was a starting point for understanding collective motion. However, it assumes identical behavior among individuals and has limitations in connecting with other physical systems. Alternative models have been explored by considering different paradigms, and experimental evidence that shows many animal groups lack alignment forces. The recent study by Gómez-Nava and colleagues (Gómez-Nava et al., Nature Physics 18, pages 1494–1501 (2022)) focused on sheep flocks and discovered simple interaction rules that lead to intermittent collective motion events, summarized in 'New & Views' section of Nature Physics titled "Sheep lead the way" by Cristián Huepe. These events involve grazing and moving in a line formation, with hierarchical leadership from random individuals. The study emphasizes the importance of exploring diverse models beyond alignment-based approaches better to understand collective behavior in animal and robotic swarms.