探索基于生成式智能体模型的因果机制

中文摘要 AI 辅助

本文探讨了将生成式智能体模型应用于经典ABM（基于智能体的建模）应用场景：测试个体层面的行为规则如何产生集体现象。我们提出了RePair方法，该方法校准模拟世界，将候选机制操作化为自然语言规则，通过匹配干预估计其效应，并检查行为轨迹。我们通过在四个基于既有社会科学模型和实证研究的模拟世界中测试该方法来评估其性能。结果表明：（1）自然语言规则能产生可测量的集体效应；（2）随着配置的累积，规则比较可能趋于收敛；（3）行为轨迹将集体效应与智能体的行为和交互联系起来，帮助研究者评估所提出的因果过程。综合这些发现，展示了使用生成式智能体模型探索因果机制的可行性，并为生成可靠、可解释的解释提供了实践指导。

英文摘要

In this paper, we explore using generative agent-based models for a classical ABM application: testing how individual-level behavioral rules produce collective phenomena. We introduce RePair, a method that calibrates simulation worlds, operationalizes candidate mechanisms as natural-language rules, estimates their effects through matched interventions, and examines behavioral traces. We assess the method by testing it in four simulation worlds grounded in established social-science models and empirical studies. Our results reveal that (1) natural-language rules can produce measurable collective effects; (2) rule comparisons can converge as configurations accumulate; and (3) behavioral traces connect collective effects to agents' actions and interactions, helping researchers evaluate the proposed causal process. Together, these findings show the feasibility of using generative agent-based models to explore causal mechanisms and provide practical guidance for producing reliable, interpretable explanations.

发表机构

• University of California, San Diego（加州大学圣地亚哥分校）
• University of British Columbia（不列颠哥伦比亚大学）

机构由 AI 辅助整理，请以论文原文为准。