Abstract
Cooperation at scale is critical for achieving a sustainable future for humanity. However, achieving
collective, cooperative behavior—in which intelligent actors in complex environments jointly improve
their well-being—remains poorly understood. Complex systems science (CSS) provides a rich
understanding of collective phenomena, the evolution of cooperation, and the institutions that can
sustain both. Yet, much of the theory in this area fails to fully consider individual-level complexity
and environmental context—largely for the sake of tractability and because it has not been clear how
to do so rigorously. These elements are well captured in multiagent reinforcement learning (MARL),
which has recently put focus on cooperative (artificial) intelligence. However, typical MARL
simulations can be computationally expensive and challenging to interpret. In this perspective, we
propose that bridging CSS and MARL affords new directions forward. Both fields can complement
each other in their goals, methods, and scope. MARL offers CSS concrete ways to formalize cognitive
processes in dynamic environments. CSS offers MARL improved qualitative insight into emergent
collective phenomena. We see this approach as providing the necessary foundations for a proper
science of collective, cooperative intelligence. We highlight work that is already heading in this
direction and discuss concrete steps for future research.