SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users

Social simulation is transforming traditional social science research by modeling human behavior through interactions between virtual individuals and their environments. With recent advances in large language models (LLMs), this approach has shown growing potential in capturing individual differences and predicting group behaviors. However, existing methods face alignment challenges related to the environment, target users, interaction mechanisms, and behavioral patterns. To this end, we introduce SocioVerse, an LLM-agent-driven world model for social simulation. Our framework features four powerful alignment components and a user pool of 10 million real individuals. To validate its effectiveness, we conducted large-scale simulation experiments across three distinct domains: politics, news, and economics. Results demonstrate that SocioVerse can reflect large-scale population dynamics while ensuring diversity, credibility, and representativeness through standardized procedures and minimal manual adjustments.

@article{zhang2025_2504.10157,
  title={ SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users },
  author={ Xinnong Zhang and Jiayu Lin and Xinyi Mou and Shiyue Yang and Xiawei Liu and Libo Sun and Hanjia Lyu and Yihang Yang and Weihong Qi and Yue Chen and Guanying Li and Ling Yan and Yao Hu and Siming Chen and Yu Wang and Xuanjing Huang and Jiebo Luo and Shiping Tang and Libo Wu and Baohua Zhou and Zhongyu Wei },
  journal={arXiv preprint arXiv:2504.10157},
  year={ 2025 }
}