Can nudges boost cooperation in multi-agent games?
LEARNING NUDGES FOR CONDITIONAL COOPERATION: A MULTI-AGENT REINFORCEMENT LEARNING MODEL
September 17, 2024
https://arxiv.org/pdf/2409.09509-
The paper examines how an AI "social planner" can use deep reinforcement learning (DRL) to encourage cooperation in a simulated public goods game with human-like, conditionally cooperative agents (CC).
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Two DRL agents with different reward functions successfully nudged CC agents to contribute more to the common good. This demonstrates the potential of DRL for shaping positive social norms in multi-agent systems and has implications for LLM-based systems where guiding agent behavior towards desirable outcomes is crucial. Notably, early intervention by the DRL agents proved crucial, highlighting the importance of initial conditions and early interactions in multi-agent systems.