Adaptive network models of collective decision making in swarming systems

TL;DR

This paper introduces adaptive network models for swarming systems, demonstrating how link dynamics influence collective decision-making and phase transitions in agent-based models.

Contribution

It provides an analytical framework linking adaptive network properties to phase transitions in collective motion, highlighting the role of discrete agent states.

Findings

Adaptive network models capture phase transitions in swarming systems.

Number of agent states influences transition properties.

Analytical results match observed collective behavior.

Abstract

We consider a class of adaptive network models where links can only be created or deleted between nodes in different states. These models provide an approximate description of a set of systems where nodes represent agents moving in physical or abstract space, the state of each node represents the agent's heading direction, and links indicate mutual awareness. We show analytically that the adaptive network description captures the phase transition to collective motion in swarming systems and that the properties of this transition are determined by the number of states (discrete heading directions) that can be accessed by each agent.