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Epitome: Pioneering an Experimental Platform for AI-Social Science Integration

arXiv 2507.01061 ↗First posted 30 Jun 2025cs.CY cs.AI cs.HCcited by 0

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Abstract (arXiv)

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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References · 34

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  1. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 157
  2. 2022Language Models as Agent Modelscited by 132
  3. 2024Emergent social conventions and collective bias in LLM populationscited by 123
  4. 2025LLM Social Simulations Are a Promising Research Methodcited by 99
  5. 2023Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysiscited by 92
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  22. 2025Emergent Abilities of Large Language Models under Continued Pretraining for Language Adaptationcited by 5

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