首次解释多智能体系统中突发极端事件的成因与驱动者。
Interpreting Emergent Extreme Events in Multi-Agent Systems
- 用谢林值量化每个动作对极端事件的影响。
- 揭示事件起源时间、主导智能体及关键行为模式。
- 适用于金融、经济等复杂系统风险分析。
大型语言模型驱动的多智能体系统已成为模拟复杂人类行为系统的强大工具。这些系统中的交互常引发难以解释的极端事件，其根源被涌现性所掩盖。理解这些事件对系统安全至关重要。本文提出首个解释多智能体系统中涌现极端事件的框架，旨在回答三个核心问题：事件何时开始？由谁推动？哪些行为促成？具体而言，我们采用谢林值（Shapley value）对各智能体在不同时间步采取的动作进行精准归因，即为每个动作分配一个影响得分。随后，沿时间、智能体和行为维度聚合归因得分，以量化各维度的风险贡献。最后，基于这些贡献分数设计一系列指标，刻画极端事件的特征。在经济、金融和社会等多种多智能体场景下的实验表明，该框架有效，并提供了关于极端现象涌现机制的普遍洞见。
原文摘要 · Abstract (English)
Large language model-powered multi-agent systems have emerged as powerful tools for simulating complex human-like systems. The interactions within these systems often lead to extreme events whose origins remain obscured by the black box of emergence. Interpreting these events is critical for system safety. This paper proposes the first framework for explaining emergent extreme events in multi-agent systems, aiming to answer three fundamental questions: When does the event originate? Who drives it? And what behaviors contribute to it? Specifically, we adapt the Shapley value to faithfully attribute the occurrence of extreme events to each action taken by agents at different time steps, i.e., assigning an attribution score to the action to measure its influence on the event. We then aggregate the attribution scores along the dimensions of time, agent, and behavior to quantify the risk contribution of each dimension. Finally, we design a set of metrics based on these contribution scores to characterize the features of extreme events. Experiments across diverse multi-agent system scenarios (economic, financial, and social) demonstrate the effectiveness of our framework and provide general insights into the emergence of extreme phenomena.
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