2025/04/10 by Giraldo-Barreto, Julian, Holubec, Viktor · 2 citations
#Adaptation and Self-Organizing Systems (nlin.AO) #FOS: Physical sciences #Soft Condensed Matter (cond-mat.soft)
paper · doi:10.48550/arxiv.2504.07778
Understanding collective self-organization in active matter, such as bird flocks and fish schools, remains a grand challenge in physics. Interactions that induce alignment are essential for flocking; however, alignment alone is generally insufficient to maintain group cohesion in the presence of noise, leading traditional models to introduce artificial boundaries or explicit attractive forces. Here, we propose a model that achieves cohesive flocking through purely alignment-based interactions by introducing predictive alignment, in which agents reorient to maximize alignment with the prevailing orientations of their anticipated future neighbors. Implemented in a discrete-time Vicsek-type framework, this approach delivers robust, noise-resistant cohesion without additional parameters. In the stable regime, flock size scales linearly with interaction radius, remaining nearly immune to noise or propulsion speed, and the group coherently follows a leader under noise. These findings reveal how predictive strategies enhance self-organization, paving the way for a new class of active matter models blending physics and cognitive-like dynamics.