用大模型生成虚拟人群，发现心理模拟有偏差但整体可行。
Effectiveness of Large Language Models in Simulating Regional Psychological Structures: An Empirical Examination of Personality and Subjective Well-being
- 基于人口数据生成2943名虚拟参与者，模拟七地区心理特征。
- 虚拟人群在外向、开放性上偏低，宜人性和神经质偏高，幸福感更低。
- 模型预测幸福的机制与真实数据不同，提示需更好情感建模。
本研究检验大语言模型是否能基于人口信息模拟具有文化根基的心理模式。使用DeepSeek生成2943名与CFPS2018人口分布匹配的虚拟参与者，对比其在大五人格特质（15项中文量表）和主观幸福感（单题项评分）上与真实人群在七个中国地区的差异。结果表明，真实与模拟数据在区域心理趋势上总体相似，但存在系统性偏差：虚拟参与者外向性和开放性得分较低，宜人性和神经质得分较高，且幸福感持续偏低。预测结构也不同：真实数据中尽责性、外向性和开放性均正向预测幸福感，而AI数据中仅开放性和宜人性显著，外向性甚至呈负向预测。这表明尽管大模型可近似群体心理分布，但对文化特定及情感维度仍存在低估。研究揭示了大模型虚拟参与者在大规模心理研究中的潜力与局限，强调需引入更丰富的文化语料与情感建模能力。
原文摘要 · Abstract (English)
This study examines whether LLMs can simulate culturally grounded psychological patterns based on demographic information. Using DeepSeek, we generated 2943 virtual participants matched to demographic distributions from the CFPS2018 and compared them with human responses on the Big Five personality traits and subjective well-being across seven Chinese regions.Personality was measured using a 15-item Chinese Big Five inventory, and happiness with a single-item rating. Results revealed broad similarity between real and simulated datasets, particularly in regional variation trends. However, systematic differences emerged:simulated participants scored lower in extraversion and openness, higher in agreeableness and neuroticism, and consistently reported lower happiness. Predictive structures also diverged: while human data identified conscientiousness, extraversion and openness as positive predictors of happiness, the AI emphasized openness and agreeableness, with extraversion predicting negatively. These discrepancies suggest that while LLMs can approximate population-level psychological distributions, they underrepresent culturally specific and affective dimensions. The findings highlight both the potential and limitations of LLM-based virtual participants for large-scale psychological research and underscore the need for culturally enriched training data and improved affective modeling.
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