LLM多智能体系统中的互动公平：一个评估框架
Interactional Fairness in LLM Multi-Agent Systems: An Evaluation Framework
AI总结：
提出面向LLM多智能体系统的互动公平评估框架，结合组织公正量表与关键事件技术，通过资源协商模拟揭示语气、理由及情境对公平感知与决策的影响。
摘要：
随着大型语言模型（LLM）越来越多地用于多智能体系统，公平性问题不应只局限于资源分配和程序设计，还应涵盖智能体之间沟通方式的公平性。借鉴组织心理学，我们提出了一个用于评估基于LLM的多智能体系统（LLM-MAS）中互动公平的新颖框架，该框架包含人际公平（IF）和信息公平（InfF）。我们将互动公平的理论基础扩展到非感知智能体，把公平重新界定为一种可由社会解释的信号，而非主观体验。随后，我们改编了组织公正研究中的成熟工具，包括Colquitt组织公正量表和关键事件技术，以将公平作为智能体交互的行为属性进行测量。我们通过一项资源协商任务的受控模拟初步研究验证了该框架。研究系统操纵语气、解释质量、结果不平等以及任务框架（协作与竞争），以评估IF如何影响智能体行为。结果表明，即使客观结果保持不变，语气和理由质量也会显著影响接受决策。此外，IF与InfF的影响会随情境而变化。本工作为LLM-MAS中的公平审计和规范敏感对齐奠定了基础。
英文摘要：
As large language models (LLMs) are increasingly used in multi-agent systems, questions of fairness should extend beyond resource distribution and procedural design to include the fairness of how agents communicate. Drawing from organizational psychology, we introduce a novel framework for evaluating Interactional fairness encompassing Interpersonal fairness (IF) and Informational fairness (InfF) in LLM-based multi-agent systems (LLM-MAS). We extend the theoretical grounding of Interactional Fairness to non-sentient agents, reframing fairness as a socially interpretable signal rather than a subjective experience. We then adapt established tools from organizational justice research, including Colquitt's Organizational Justice Scale and the Critical Incident Technique, to measure fairness as a behavioral property of agent interaction. We validate our framework through a pilot study using controlled simulations of a resource negotiation task. We systematically manipulate tone, explanation quality, outcome inequality, and task framing (collaborative vs. competitive) to assess how IF influences agent behavior. Results show that tone and justification quality significantly affect acceptance decisions even when objective outcomes are held constant. In addition, the influence of IF vs. InfF varies with context. This work lays the foundation for fairness auditing and norm-sensitive alignment in LLM-MAS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
