This paper introduces CMASE, a framework for Computational Multi-Agent Society Experiments that integrates generative agent-based modeling with virtual ethnographic methods to support researcher embedding, interactive participation, and mechanism-oriented intervention in virtual social environments. By transforming the simulation into a simulated ethnographic field, CMASE shifts the researcher from an external operator to an embedded participant. Specifically, the framework is designed to achieve three core capabilities: (1) enabling real-time human-computer interaction that allows researchers to dynamically embed themselves into the system to characterize complex social intervention processes; (2) reconstructing the generative logic of social phenomena by combining the rigor of computational experiments with the interpretative depth of traditional ethnography; and (3) providing a predictive foundation with causal explanatory power to make forward-looking judgments without sacrificing empirical accuracy. Experimental results show that CMASE can not only simulate complex phenomena, but also generate behavior trajectories consistent with both statistical patterns and mechanistic explanations. These findings demonstrate CMASE's methodological value for intervention modeling, highlighting its potential to advance interdisciplinary integration in the social sciences. The official code is available at: https://github.com/armihia/CMASE .

翻译：本文提出CMASE（计算多智能体社会实验框架），该框架将基于生成智能体的建模与虚拟民族志方法相结合，以支持研究者在虚拟社会环境中进行嵌入、交互式参与及面向机制的干预。通过将仿真转化为模拟的民族志场域，CMASE使研究者从外部操作者转变为嵌入式参与者。具体而言，该框架旨在实现三项核心能力：（1）实现实时人机交互，允许研究者动态嵌入系统以刻画复杂的社会干预过程；（2）通过结合计算实验的严谨性与传统民族志的解释深度，重构社会现象的生成逻辑；（3）提供具有因果解释力的预测基础，在不牺牲经验准确性的前提下做出前瞻性判断。实验结果表明，CMASE不仅能模拟复杂现象，还能生成既符合统计模式又符合机制解释的行为轨迹。这些发现证明了CMASE在干预建模方面的方法论价值，凸显了其在推动社会科学跨学科融合方面的潜力。官方代码发布于：https://github.com/armihia/CMASE。