2025/06/30 by Jingjing Qu, Qu, Jingjing, Jun Zhu +19
Medicine · Social Sciences · #Architecture #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare and Education #Computational and Text Analysis Methods #Computers and Society (cs.CY) #Empirical research #Epitome #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Modular design #Replication (statistics) #Software deployment
paper · pdf · doi:10.48550/arxiv.2507.01061
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.