SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913941914910720 |
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| author | Zhang, Xinnong Lin, Jiayu Mou, Xinyi Yang, Shiyue Liu, Xiawei Sun, Libo Lyu, Hanjia Yang, Yihang Qi, Weihong Chen, Yue Li, Guanying Yan, Ling Hu, Yao Chen, Siming Wang, Yu Huang, Xuanjing Luo, Jiebo Tang, Shiping Wu, Libo Zhou, Baohua Wei, Zhongyu |
| author_facet | Zhang, Xinnong Lin, Jiayu Mou, Xinyi Yang, Shiyue Liu, Xiawei Sun, Libo Lyu, Hanjia Yang, Yihang Qi, Weihong Chen, Yue Li, Guanying Yan, Ling Hu, Yao Chen, Siming Wang, Yu Huang, Xuanjing Luo, Jiebo Tang, Shiping Wu, Libo Zhou, Baohua Wei, Zhongyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10157 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users Zhang, Xinnong Lin, Jiayu Mou, Xinyi Yang, Shiyue Liu, Xiawei Sun, Libo Lyu, Hanjia Yang, Yihang Qi, Weihong Chen, Yue Li, Guanying Yan, Ling Hu, Yao Chen, Siming Wang, Yu Huang, Xuanjing Luo, Jiebo Tang, Shiping Wu, Libo Zhou, Baohua Wei, Zhongyu Computation and Language Computers and Society 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. |
| title | SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users |
| topic | Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2504.10157 |