Adaptive-network models of swarm dynamics

TL;DR

This paper introduces an adaptive-network model for swarm dynamics that captures key behaviors like phase transitions and intermittency, providing insights into collective motion with minimal spatial assumptions.

Contribution

It presents a low-dimensional analytical model that reproduces experimental swarm behaviors, challenging the importance of spatial geometry in collective motion.

Findings

Model reproduces symmetry breaking and phase transitions.

Captures noise- and density-driven order-disorder transitions.

Suggests spatial geometry may be less critical for collective behavior.

Abstract

We propose a simple adaptive-network model describing recent swarming experiments. Exploiting an analogy with human decision making, we capture the dynamics of the model by a low-dimensional system of equations permitting analytical investigation. We find that the model reproduces several characteristic features of swarms, including spontaneous symmetry breaking, noise- and density-driven order-disorder transitions that can be of first or second order, and intermittency. Reproducing these experimental observations using a non-spatial model suggests that spatial geometry may have a lesser impact on collective motion than previously thought.