Intelligence of agents produces a structural phase transition in collective behaviour

TL;DR

This paper demonstrates that collective behavior in information-processing organisms arises from a universal nonequilibrium phase transition, linking cognitive processes, pattern formation, and information transfer in biological and artificial systems.

Contribution

It reveals a universal physical mechanism underlying collective organization and phase transitions in cognitive agents, bridging biology, cognition, and artificial intelligence.

Findings

Identification of a continuous order-disorder transition in collective behavior

Emergence of a Goldstone mode indicating collective information transfer

Universal applicability to biological and artificial swarm systems

Abstract

Living organisms process information to interact and adapt to their changing environment with the goal of finding food, mates or averting hazards. The structure of their niche has profound repercussions by both selecting their internal architecture and also inducing adaptive responses to environmental cues and stimuli. Adaptive, collective behaviour underpinned by specialized optimization strategies is ubiquitously found in the natural world. This exceptional success originates from the processes of fitness and selection. Here we prove that a universal physical mechanism of a nonequilibrium transition underlies the collective organization of information-processing organisms. As cognitive agents build and update an internal, cognitive representation of the causal structure of their environment, complex patterns emerge in the system, where the onset of pattern formation relates to the spatial overlap of cognitive maps. Studying the exchange of information among the agents reveals a continuous, order-disorder transition. As a result of the spontaneous breaking of translational symmetry, a Goldstone mode emerges, which points at a collective mechanism of information transfer among cognitive organisms. Taken together, the characteristics of this phase transition consolidate different results in cognitive and biological sciences in a universal manner. These finding are generally applicable to the design of artificial intelligent swarm systems that do not rely on centralized control schemes.