The Computational Foundations of Collective Intelligence
This foundational work addresses the computational basis of collective intelligence, with implications for understanding group behavior in animals and potentially AI systems.
The paper tackles the problem of why collectives outperform individuals in problem-solving by proposing that greater computational resources enable collective intelligence, and demonstrates through case studies that collectives use qualitatively different strategies to solve problems more effectively.
Why do collectives outperform individuals when solving some problems? Fundamentally, collectives have greater computational resources with more sensory information, more memory, more processing capacity, and more ways to act. While greater resources present opportunities, there are also challenges in coordination and cooperation inherent in collectives with distributed, modular structures. Despite these challenges, we show how collective resource advantages lead directly to well-known forms of collective intelligence including the wisdom of the crowd, collective sensing, division of labour, and cultural learning. Our framework also generates testable predictions about collective capabilities in distributed reasoning and context-dependent behavioural switching. Through case studies of animal navigation and decision-making, we demonstrate how collectives leverage their computational resources to solve problems not only more effectively than individuals, but by using qualitatively different problem-solving strategies.