6.人工智能已经在社会模拟中使用，用来预测用户行为：SocioVerse：基于LLM智能体与千万级真实用户池的社会模拟世界模型

SocioVerse: https://github.com/FudanDISC/SocioVerse

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摘要

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.

社会模拟正通过虚拟个体与环境间的交互行为建模，革新传统社会科学研究。随着大语言模型（LLMs）的进步，该方法在捕捉个体差异和预测群体行为方面展现出巨大潜力。然而现有方法在环境、目标用户、交互机制和行为模式的对齐上面临挑战。为此，我们提出SocioVerse——一个由LLM智能体驱动的社会模拟世界模型。该框架包含四大对齐模块和千万级真实用户池，并通过政治、新闻和经济三大领域的实验验证其有效性。结果表明，SocioVerse能通过标准化流程和最小人工干预，在保证多样性、可信度和代表性的同时，精准反映大规模人口动态。

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