Dynamic Incentivized Cooperation under Changing Rewards

2026-01-10

arXiv.org

AI Summary

This work addresses the challenge of sustaining cooperation in multi-agent systems when environmental rewards change dynamically—a limitation of existing peer incentive methods that rely on fixed incentive values. To overcome this, the authors propose DRIVE, the first fully decentralized dynamic incentive mechanism. In DRIVE, each agent adaptively generates incentive signals by exchanging local reward differences with peers, enabling robust cooperation without requiring re-tuning of parameters under shifting reward conditions. Built within a multi-agent reinforcement learning framework, the method integrates reward difference exchange with adaptive incentive policies. Experiments demonstrate that DRIVE significantly outperforms current approaches in both the general Prisoner’s Dilemma and complex sequential social dilemmas, effectively achieving and stably maintaining cooperative behavior.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Peer incentivization (PI) is a popular multi-agent reinforcement learning approach where all agents can reward or penalize each other to achieve cooperation in social dilemmas. Despite their potential for scalable cooperation, current PI methods heavily depend on fixed incentive values that need to be appropriately chosen with respect to the environmental rewards and thus are highly sensitive to their changes. Therefore, they fail to maintain cooperation under changing rewards in the environment, e.g., caused by modified specifications, varying supply and demand, or sensory flaws - even when the conditions for mutual cooperation remain the same. In this paper, we propose Dynamic Reward Incentives for Variable Exchange (DRIVE), an adaptive PI approach to cooperation in social dilemmas with changing rewards. DRIVE agents reciprocally exchange reward differences to incentivize mutual cooperation in a completely decentralized way. We show how DRIVE achieves mutual cooperation in the general Prisoner's Dilemma and empirically evaluate DRIVE in more complex sequential social dilemmas with changing rewards, demonstrating its ability to achieve and maintain cooperation, in contrast to current state-of-the-art PI methods.

Problem

• peer incentivization
• changing rewards
• multi-agent reinforcement learning
• social dilemmas
• cooperation

Innovation

• Dynamic Incentivization
• Peer Incentivization
• Changing Rewards
• Decentralized Cooperation
• Multi-Agent Reinforcement Learning