用大模型搭建可控人机互动实验平台，研究人类在AI社会中的行为。
Epitome: Pioneering an Experimental Platform for AI-Social Science Integration
- 通过大模型代理构建类矩阵虚拟世界，实现对社交动态的精准操控。
- 复现三项经典实验，验证平台能降低复杂度并生成可靠结果。
- 适合研究人机交互、社会心理与未来人机共存的学者与政策制定者。
大型语言模型（LLMs）通过构建受控的人机混合环境，为社会科学实验带来前所未有的可能性。我们推出Epitome（www.epitome-ai.com），一个开源实验平台，其核心是模拟矩阵式社会世界，使研究人员能在保持生态有效性的前提下，观察孤立的人类个体与群体与LLM代理之间的互动。该平台突破三大前沿：（1）采用LLM合作者方法，在降低复杂性的同时扩展交互规模；（2）实证研究人类在高密度AI环境中的行为模式；（3）探索人机混合集体中涌现的特性。平台基于管理学、传播学、社会学、心理学与伦理学的跨学科基础，采用模块化架构，覆盖基础模型部署至数据采集全过程。通过复现三项经典实验，验证了平台生成稳健发现的能力，并显著降低实验复杂度。该工具为理解人类如何适应人机共融的社会现实提供了关键洞见，对政策制定、教育设计与以人为本的AI发展具有重要意义。
原文摘要 · Abstract (English)
Large Language Models (LLMs) enable unprecedented social science experimentation by creating controlled hybrid human-AI environments. We introduce Epitome (www.epitome-ai.com), an open experimental platform that operationalizes this paradigm through Matrix-like social worlds where researchers can study isolated human subjects and groups interacting with LLM agents. This maintains ecological validity while enabling precise manipulation of social dynamics. Epitome approaches three frontiers: (1) methodological innovation using LLM confederates to reduce complexity while scaling interactions; (2) empirical investigation of human behavior in AI-saturated environments; and (3) exploration of emergent properties in hybrid collectives. Drawing on interdisciplinary foundations from management, communication, sociology, psychology, and ethics, the platform's modular architecture spans foundation model deployment through data collection. We validate Epitome through replication of three seminal experiments, demonstrating capacity to generate robust findings while reducing experimental complexity. This tool provides crucial insights for understanding how humans navigate AI-mediated social realities, knowledge essential for policy, education, and human-centered AI design.
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