Emergent order in a continuous state adaptive network model of living systems

TL;DR

This paper investigates how spontaneous order emerges in biological systems through a continuous state adaptive network model, identifying conditions and phase transitions that lead to collective behavior.

Contribution

It introduces a novel continuous state adaptive network model to analyze emergent order and characterizes phase boundaries and transition types in biological systems.

Findings

Identified phase boundaries between swarming and disordered states

Characterized the nature of phase transitions in the model

Provided insights into conditions promoting collective order

Abstract

Order can spontaneously emerge from seemingly noisy interactions between biological agents, like a flock of birds changing their direction of flight in unison, without a leader or an external cue. We are interested in the generic conditions that lead to such emergent phenomena. To find these conditions, we use the framework of complex networks to characterize the state of agents and their mutual influence. We formulate a continuous state adaptive network model, from which we obtain the phase boundaries between swarming and disordered phases and characterize the order of the phase transition.