SocioVerse: A World Model for Social Simulation Powered by LLM Agents
  and A Pool of 10 Million Real-World Users
Authors: name: Xinnong Zhang, name: Jiayu Lin, name: Xinyi Mou, name: Shiyue Yang, name: Xiawei Liu, name: Libo Sun, name: Hanjia Lyu, name: Yihang Yang, name: Weihong Qi, name: Yue Chen, name: Guanying Li, name: Ling Yan, name: Yao Hu, name: Siming Chen, name: Yu Wang, name: Jingxuan Huang, name: Jiebo Luo, name: Shiping Tang, name: Libo Wu, name: Baohua Zhou, name: Zhongyu Wei

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.