提出多智能体博弈中的程序公平性，强调决策话语权平等。
Procedural Fairness in Multi-Agent Bandits
- 以策略中各智能体的代表权均等为公平标准
- 实验证明结果导向公平会牺牲话语权，而程序公平对结果影响小
- 适合关注决策公正性与治理机制的研究者
在多智能体多臂赌博机（MA-MAB）中，公平性常被简化为结果：最大化福利、减少不平等或平衡效用。然而心理学、经济学及罗尔斯理论表明，公平也关乎过程和谁有决策发言权。本文引入‘程序公平’概念，定义为各智能体在策略中具有相等的代表权，并基于代表而非效用构建核心稳定纳什福利目标。实证结果表明，仅优化结果的公平策略会牺牲平等话语权；而采用程序公平策略时，结果指标（如平等性和功利主义）的损失极小。进一步证明不同公平理念本质上存在根本冲突，凸显公平需明确规范选择。本文主张程序正当性应作为核心公平目标，并提供可操作的实现框架。
原文摘要 · Abstract (English)
In the context of multi-agent multi-armed bandits (MA-MAB), fairness is often reduced to outcomes: maximizing welfare, reducing inequality, or balancing utilities. However, evidence in psychology, economics, and Rawlsian theory suggests that fairness is also about process and who gets a say in the decisions being made. We introduce procedural fairness as equal voice and formalize it for MA-MABs as equal representation within a policy over arms, using a core-stable Nash welfare objective based on representation rather than utility. Empirical results confirm that fairness notions based on optimizing for outcomes sacrifice equal voice and representation, while the sacrifice in outcome-based objectives (like equality and utilitarianism) is minimal under procedurally fair policies. We further prove that different fairness notions prioritize fundamentally different and incompatible values, highlighting that fairness requires explicit normative choices. This paper argues that procedural legitimacy deserves greater focus as a fairness objective and provides a framework for putting procedural fairness into practice.
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