Source-linked AI summary
Large-scales patterns in a minimal cognitive flocking model: incidental leaders, nematic patterns, and aggregates
Lucas Barberis, Fernando Peruani
TL;DR
The paper asks whether flocking patterns can arise without velocity alignment or memory, using only instantaneous visual information. Simulations and nonlinear field equations show that a position-based model generates multiple large-scale patterns and differs fundamentally from velocity-alignment systems.
Problem
The paper investigates whether collective motion can emerge from instantaneous position-based visual information without memory or velocity-velocity alignment.
Method
The authors combine agent-based simulations with nonlinear field equations for active particles attracted to neighbors inside a vision cone.
Results
The model produces aggregates and milling-like patterns, locally polar worms led by incidental front particles, and macroscopic nematic bands with long-ranged nematic order.
Takeaways & Limitations
Position-based active models form a distinct class of collective-motion systems whose ordering is associated with density instabilities rather than spatially homogeneous order.
Takeaways & Limitations
The model assumes a limited vision cone and cognitive horizon, with the vision cone breaking action-reaction symmetry for β < π.
Abstract
from arXiv · showhide
We study a minimal cognitive flocking model, which assumes that the moving entities navigate using exclusively the available instantaneous visual information. The model consists of active particles, with no memory, that interact by a short-ranged, position-based, attractive force that acts inside a vision cone (VC) and lack velocity-velocity alignment. We show that this active system can exhibit -- due to the VC that breaks Newton's third law -- various complex, large-scale, self-organized patterns. Depending on parameter values, we observe the emergence of aggregates or milling-like patterns, the formation of moving -- locally polar -- files with particles at the front of these structures acting as effective leaders, and the self-organization of particles into macroscopic nematic structures leading to long-ranged nematic order. Combining simulations and non-linear field equations, we show that position-based active models, as the one analyzed here, represent a new class of active systems fundamentally different from other active systems, including velocity-alignment-based flocking systems. The reported results are of prime importance in the study, interpretation, and modeling of collective motion patterns in living and non-living active systems.