Chaotic Itinerancy in Collective Behaviour Emerging from Active Inference: A Multi-Agent Model of Trust and...

Chaotic itinerancy-irregular switching among metastable collective states-provides a dynamical substrate for flexible social coordination, yet its mechanistic origin in multi-agent systems remains unclear. We present a multi-agent Active Inference model in which chaotic itinerancy emerges from Expected Free Energy minimisation without outcome-level social priors. Agents select actions to minimise Expected Free Energy while updating preferences through a precision-gated learning mechanism modulated by interpersonal trust. Hill-function nonlinearity in state transitions creates bistable "affordance landscapes" that gate behavioural mode switching. Simulations with small number of agents on an Erdős-Rényi trust network reveal spontaneous alternation among multiple metastable behavioural clusters, heavy-tailed dwell-time distributions, and sign-changing finite-time Lyapunov exponents-three hallmarks of chaotic itinerancy. Crucially, replacing Hill-function dynamics with linear transitions reduces the chaotic-itinerancy detection rate from 80% to 20%, demonstrating that nonlinear affordance structure is necessary for generating metastable switching. We further show that agents with simplified internal models of the world sustain richer itinerant dynamics as a group than "perfect-foresight" agents, suggesting that bounded rationality may be functionally advantageous for maintaining behavioural flexibility. These results establish active inference as a principled framework for modelling chaotic itinerancy in social systems and offer a computational account of trust-mediated collective transitions observed in theatre workshops and group dynamics.