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.

翻译：基于大语言模型的多智能体系统已成为模拟复杂类人系统的强大工具。这些系统内部的交互常会引发起源被涌现黑箱所遮蔽的极端事件。解释此类事件对系统安全至关重要。本文提出了首个用于解释多智能体系统中涌现极端事件的框架，旨在回答三个基本问题：事件何时起源？由谁驱动？何种行为促成其发生？具体而言，我们改进夏普利值方法，将极端事件的发生忠实归因于智能体在不同时间步采取的每个行动，即为行动分配归因分数以衡量其对事件的影响程度。随后沿时间、智能体与行为三个维度聚合归因分数，从而量化各维度的风险贡献。最后，基于这些贡献分数设计了一套度量指标来刻画极端事件的特征。在多样化多智能体系统场景（经济、金融与社会领域）中的实验验证了本框架的有效性，并为极端现象的涌现机制提供了普适性见解。