Multi-Agent Empowerment and Emergence of Complex Behavior in Groups

Researchers Tristan Shah, Ilya Nemenman, Daniel Polani, and Stas Tiomkin have published a paper on arXiv (2604.21155) exploring how empowerment, an intrinsic motivation that measures an agent's ability to influence its future, can be extended to multi-agent systems. They formulated a principled extension of empowerment to multiple agents and demonstrated its efficient calculation. In two distinct environments—a pair of agents coupled by a tendon and a controllable Vicsek flock—this intrinsic motivation gave rise to characteristic modes of group organization, such as synchronized motion and flocking, without any explicit reward or engineering.

This work is significant because it shows that complex group behaviors can emerge from simple, local incentives like empowerment, rather than requiring top-down design or centralized control. The findings suggest that empowerment-based intrinsic motivations could be a scalable approach for coordinating swarms of robots, autonomous vehicles, or other multi-agent systems, enabling them to self-organize into efficient, adaptive groups. The paper is 11 pages long and was submitted to the Artificial Intelligence and Multiagent Systems categories on arXiv.