Stability of Emergent Behaviors in Multi-Agent AI Planning Environments

Abstract

Mixed-motive multi-agent planning environments exhibit emergent behaviors that arise from interactions among autonomous agents balancing cooperative and competitive incentives. Stability in these systems depends not on fixed equilibrium solutions, but on how agents adapt to one another over time under varying resource conditions, communication patterns, and learning dynamics. This study investigates the factors that support or disrupt stable emergent behavior, emphasizing the role of synchronized policy adaptation, expressive state representation, and communication topology. Results show that coordinated behaviors can persist even without explicit negotiation when learning trajectories remain aligned and environmental variation occurs gradually. Conversely, abrupt adaptation shifts or fragmented information pathways destabilize cooperation, producing oscillatory or divergent agent strategies. The findings highlight that stability in multi-agent environments must be understood as a dynamic, interaction-driven property that depends on maintaining coherence across learning, representation, and communication layers.