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[2601.20538] Interpreting Emergent Extreme Events in Multi-Agent Systems
Summary
This paper presents a framework for interpreting emergent extreme events in multi-agent systems, focusing on the origins and drivers of these events using the Shapley value for attribution.
Why It Matters
Understanding emergent extreme events in multi-agent systems is crucial for ensuring system safety and reliability. This research provides a structured approach to identify and quantify the contributions of individual agents to such events, which can enhance decision-making and risk management in complex systems.
Key Takeaways
- Introduces a framework for explaining emergent extreme events in multi-agent systems.
- Utilizes the Shapley value to attribute event origins to specific agent actions.
- Quantifies risk contributions across dimensions of time, agent, and behavior.
- Demonstrates effectiveness through experiments in economic, financial, and social scenarios.
- Provides insights into the dynamics of extreme phenomena in complex systems.
Computer Science > Multiagent Systems arXiv:2601.20538 (cs) [Submitted on 28 Jan 2026 (v1), last revised 15 Feb 2026 (this version, v2)] Title:Interpreting Emergent Extreme Events in Multi-Agent Systems Authors:Ling Tang, Jilin Mei, Dongrui Liu, Chen Qian, Dawei Cheng, Jing Shao, Xia Hu View a PDF of the paper titled Interpreting Emergent Extreme Events in Multi-Agent Systems, by Ling Tang and 6 other authors View PDF HTML (experimental) Abstract: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....
