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.
67fdc9adc47ae882c7a17c8a
Xinnong Zhang
claimed_verified
2025-04-15T07:54:18.076Z
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xnzhang
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Lishi0905
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Jiayu Lin
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Xinyi Mou
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Shiyue Yang
67fdc9adc47ae882c7a17c8e
Xiawei Liu
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Libo Sun
admin_assigned
2025-04-15T08:26:53.104Z
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Libo Sun
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libo-ca
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Hanjia Lyu
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Yihang Yang
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Weihong Qi
67fdc9adc47ae882c7a17c93
Yue Chen
67fdc9adc47ae882c7a17c94
Guanying Li
67fdc9adc47ae882c7a17c95
Ling Yan
67fdc9adc47ae882c7a17c96
Yao Hu
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Siming Chen
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2025-04-15T08:28:27.202Z
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Siming
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SimingChen
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Yu Wang
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Jingxuan Huang
67fdc9adc47ae882c7a17c9a
Jiebo Luo
67fdc9adc47ae882c7a17c9b
Shiping Tang
admin_assigned
2025-04-15T08:28:00.350Z
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Simon Tang
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tangshiping
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Libo Wu
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Baohua Zhou
admin_assigned
2025-04-15T08:27:40.001Z
67d066e70fdab2f434aa1488
/avatars/d17838856185856306ec5d4a121314f1.svg
Baohua Zhou
user
milesz7777
67fdc9adc47ae882c7a17c9e
Zhongyu Wei
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.
SocioVerse, an LLM-agent-driven world model, effectively simulates social behaviors and dynamics across various domains with standardized procedures and minimal manual adjustments.
large language models LLM-agent-driven world model alignment components population dynamics diversity credibility representativeness