Collective cooperative intelligence

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.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2025-06-16
DOI
https://doi.org/10.1073/pnas.2319948121
Citations
8
Primary Topic
Evolutionary Game Theory and Cooperation
Type
article
Field-Weighted Citation Impact
33.61
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article

Collective cooperative intelligence

Tom Lenaerts, Edward Hughes, Udari Madhushani, Jessica C. Flack et al.
8 citations
Proceedings of the National Academy of Sciences
Evolutionary Game Theory and Cooperation
33.61
article

Collective cooperative intelligence

Tom Lenaerts, Edward Hughes, Udari Madhushani, Jessica C. Flack, Naomi Ehrich Leonard, Chaitanya S. Gokhale, Lewis Hammond, Wolfram Barfuß, Joel Z. Leibo, Christian Hilbe, Fernando Pereira dos Santos, Simon A. Levin, Janusz M Meylahn, Alex McAvoy
article en
8 citations

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.

Proceedings of the National Academy of SciencesVol. 122(25)
University of North Carolina at Chapel Hill (US), Google (United States) (US), Santa Fe Institute (US), Université Libre de Bruxelles (BE), Vrije Universiteit Brussel (BE), University of Bonn (DE), Princeton University (US), University of California System (US), University of Oxford (GB), Google DeepMind (United Kingdom) (GB), Google (United Kingdom) (GB), Max Planck Institute for Evolutionary Biology (DE), Laboratoire d'Informatique de Paris-Nord (FR), Département d'Informatique (FR), Toronto Metropolitan University (CA), University of Amsterdam (NL), University of Twente (NL), University of California, Berkeley (US), Stanford University (US)
Partnerships for the goals
Openalex Percentile: Top 0%
Evolutionary Game Theory and Cooperation
33.61
DOIOpen Access PDFOpenAlex
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