Position: Multi-Agent AI for Emergent Behavior Requires Mean-Field Learning

Saptarashmi Bandyopadhyay, Emile Timothy Anand, Arundhati Banerjee

Abstract:

As AI agents scale to large populations, traditional coordination mechanisms face intractable complexity.
In multi-agent settings, \emph{agentic emergence} is a term commonly used to characterize unpredictable collective behaviors at scales of multi-agent interactions, but is not well-defined.
\textbf{This position paper argues that grounding agentic emergence in Artificial Social Intelligence and Mean-Field Theory is necessary for transforming illusory heuristics to rigorous scaling laws}.
Current multi-agent interaction paradigms face exponential sample complexity barriers that fundamentally limit scalability due to combinatorial interaction spaces.
We argue that mean-field learning (MFL) is not merely an optimization technique but a principled and increasingly necessary requirement for reliable large-population orchestration.
We support this position by pointing to recent advances in sample complexity driven by game theory and MFL with example illustrations in agentic coding and robotics.
Finally, we offer a rationalized definition of emergence and outline a future research agenda where mean-field learning turns the abstract concept of emergence into a theoretically grounded reality for scaling populations of AI agents.