设计自适应契约让不同能力的智能体公平分配收益
Fair Contracts in Principal-Agent Games with Heterogeneous Types
- 用可学习的线性契约动态调整对不同智能体的激励
- 在序列社会困境中实现各智能体结果均等化
- 兼顾公平与系统效率，适合多智能体协作场景
公平在多智能体系统中虽理想却难实现，尤其当智能体具有影响能力的隐含差异时。这种隐藏异质性常导致即使规则相同，财富分配仍不均。受现实案例启发，我们提出基于重复主-代理博弈的框架，其中主方作为博弈参与者，学习向代理提供自适应契约。通过一种简单而强大的契约结构，我们证明公平意识的主方可学习同质线性契约，在序列社会困境中实现跨代理的结果均等。重要的是，这种公平性不牺牲效率：我们的结果表明，在保持整体性能的同时，可实现系统内的公平与稳定。
原文摘要 · Abstract (English)
Fairness is desirable yet challenging to achieve within multi-agent systems, especially when agents differ in latent traits that affect their abilities. This hidden heterogeneity often leads to unequal distributions of wealth, even when agents operate under the same rules. Motivated by real-world examples, we propose a framework based on repeated principal-agent games, where a principal, who also can be seen as a player of the game, learns to offer adaptive contracts to agents. By leveraging a simple yet powerful contract structure, we show that a fairness-aware principal can learn homogeneous linear contracts that equalize outcomes across agents in a sequential social dilemma. Importantly, this fairness does not come at the cost of efficiency: our results demonstrate that it is possible to promote equity and stability in the system while preserving overall performance.
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