A Framework for Analyzing Abnormal Emergence in Service Ecosystems Through LLM-based Agent Intention Mining

TL;DR

This paper presents EAMI, a novel framework leveraging large language models and intention mining to analyze abnormal emergence in complex service ecosystems dynamically and interpretably.

Contribution

It introduces a new dynamic, interpretable emergence analysis framework using LLMs and intention mining, addressing limitations of static microscopic approaches.

Findings

Validated in online-to-offline service systems

Effective in identifying phase transition points

Demonstrates generalizability and efficiency

Abstract

With the rise of service computing, cloud computing, and IoT, service ecosystems are becoming increasingly complex. The intricate interactions among intelligent agents make abnormal emergence analysis challenging, as traditional causal methods focus on individual trajectories. Large language models offer new possibilities for Agent-Based Modeling (ABM) through Chain-of-Thought (CoT) reasoning to reveal agent intentions. However, existing approaches remain limited to microscopic and static analysis. This paper introduces a framework: Emergence Analysis based on Multi-Agent Intention (EAMI), which enables dynamic and interpretable emergence analysis. EAMI first employs a dual-perspective thought track mechanism, where an Inspector Agent and an Analysis Agent extract agent intentions under bounded and perfect rationality. Then, k-means clustering identifies phase transition points in group intentions, followed by a Intention Temporal Emergence diagram for dynamic analysis. The experiments validate EAMI in complex online-to-offline (O2O) service system and the Stanford AI Town experiment, with ablation studies confirming its effectiveness, generalizability, and efficiency. This framework provides a novel paradigm for abnormal emergence and causal analysis in service ecosystems. The code is available at https://anonymous.4open.science/r/EAMI-B085.