Milling and meandering: Flocking dynamics of stochastically interacting agents with a field of view

TL;DR

This paper presents a stochastic agent-based model for flocking behavior driven by velocity alignment within a field of view, revealing how view constraints influence emergent collective patterns without attraction forces.

Contribution

It introduces a novel stochastic flocking model emphasizing the role of field of view in pattern formation, with analysis tools for cluster dynamics.

Findings

Field of view critically affects emergent flocking patterns.

Patterns exhibit long-term spatial cohesion despite stochastic interactions.

Cluster analysis reveals diverse dynamical behaviors depending on view parameters.

Abstract

We introduce a stochastic agent-based model for the flocking dynamics of self-propelled particles that exhibit velocity-alignment interactions with neighbours within their field of view. The stochasticity in the dynamics of the model arises purely from the uncertainties at the level of interactions. Despite the absence of attractive forces, this model gives rise to a wide array of emergent patterns that exhibit long-time spatial cohesion. In order to gain further insights into the dynamical nature of the resulting patterns, we investigate the system behaviour using an algorithm that identifies spatially distinct clusters of the flock and computes their corresponding angular momenta. Our results suggest that the choice of field of view is crucial in determining the resulting emergent dynamics of stochastically interacting particles.