Market-Aware Multi-Agent Systems: Preventing Emergent Collusive Behavior in Reinforcement-Learning Trading Agents

Abstract

As reinforcement learning (RL) continues to advance multi-agent systems for algorithmic trading, a pressing concern has emerged: the tendency of autonomous agents to develop tacit collusive behaviors that undermine market competitiveness. This paper addresses the challenge of emergent collusion in market-facing multi-agent reinforcement learning (MARL) environments by proposing a novel market-aware framework. Drawing on organizational paradigms in agent coordination, game-theoretic principles under uncertainty, and recent findings on algorithmic collusion, we develop a MARL architecture that incorporates macroeconomic signals—such as price elasticity, demand shifts, and aggregate agent behavior—into each agent’s policy optimization. Empirical simulations in synthetic financial markets demonstrate that standard MARL agents often converge toward supra-competitive pricing equilibria, echoing findings from recent studies on self-reinforcing AI collusion. By contrast, our market-aware agents adaptively adjust to dynamic environments and maintain competitive pricing even in the absence of explicit regulation. We further discuss the role of platform-level incentives and shared market feedback as regulatory substitutes, offering architectural and governance guidelines for mitigating systemic risks posed by advanced AI in digital economies. This work contributes to both AI safety and computational economics by bridging algorithmic design and policy-aware learning mechanisms to ensure fair, efficient, and collusion-resistant trading systems.