Emergent Behavior Stability 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.