Friedkin and Johnsen Unveil the Dynamics of Multi-Agent Debate

As LLM-based multi-agent debate drives advances in agentic AI, a central question arises: How does collective intelligence emerge from interactions among agents? We introduce a framework to study the underlying dynamics through the Friedkin–Johnsen (FJ) model of opinion formation. Across diverse empirical settings, the FJ model accurately reconstructs the evolution of agents’ beliefs while providing interpretable measures of stubbornness and persuasiveness that characterize how agents engage in consensus formation. We systematically compare FJ with alternatives, including DeGroot dynamics and a statistical-physics-inspired model, and find advantages in trajectory reconstruction, consistency with interventions, and interpretability of communication patterns. Analyzing different communication topologies and group sizes, we find that stubborn agents become less prevalent in larger groups, while persuasive agents become more prominent. Our results establish a simple, interpretable framework for understanding how individual interactions shape LLM multi-agent collaboration.