Conor Heins, Beren Millidge, Lancelot Da Costa,  Richard P. Mann,  Karl J. Friston, Iain D. Couzin

PNAS

We introduce a model of collective behavior, proposing that individual members within a group, such as a school of fish or a flock of birds, act to minimize surprise. This active inference approach naturally generates well-known collective phenomena such as cohesion and directed movement without explicit behavioral rules. Our model reveals intricate relationships between individual beliefs and group properties, demonstrating that beliefs about uncertainty can shape collective decision-making accuracy. As agents update their generative model in real time, groups become more sensitive to external perturbations and more robust in encoding information. Our work provides fresh insights into understanding collective dynamics and could inspire strategies in the study of animal behavior, swarm robotics, and distributed systems.

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