大语言模型在模拟区域心理结构方面的有效性：关于人格与主观幸福感的实证检验
Effectiveness of Large Language Models in Simulating Regional Psychological Structures: An Empirical Examination of Personality and Subjective Well-being
AI总结：
本研究基于中国七省数据实证检验大语言模型模拟区域心理结构的效果，发现其能近似群体心理分布，但存在人格与幸福感预测的系统性偏差，凸显其潜力与文化情感建模的改进方向。
摘要：
本研究探究大语言模型（LLMs）能否基于人口统计信息模拟具有文化根基的心理模式。我们使用DeepSeek生成了2943名虚拟参与者，其人口统计分布与CFPS2018（中国家庭追踪调查2018）的分布相匹配，并在中国七个地区的大五人格特质与主观幸福感维度上，将虚拟参与者的回答与人类回答进行对比。人格采用15题项的中国大五人格量表测量，幸福感采用单题项评分测量。结果显示，真实数据集与模拟数据集之间存在广泛相似性，尤其是在区域变化趋势方面。但也出现了系统性差异：模拟参与者在外向性和开放性上得分更低，在宜人性和神经质上得分更高，且始终报告更低的幸福感水平。预测结构也存在分歧：人类数据识别出尽责性、外向性和开放性是幸福感的正向预测因子，而AI则强调开放性和宜人性，且外向性呈负向预测作用。这些差异表明，尽管LLMs能够近似群体层面的心理分布，但它们对文化特定维度和情感维度的表征不足。这些发现既凸显了基于LLM的虚拟参与者在大规模心理学研究中的潜力与局限性，也强调了丰富文化训练数据、改进情感建模的必要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
