This study seeks to identify and quantify biases in simulating political samples with Large Language Models, specifically focusing on vote choice and public opinion. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao Dataset, and China Family Panel Studies to simulate voting behaviors and public opinions. This methodology enables us to examine three types of representation bias: disparities based on the the country's language, demographic groups, and political regime types. The findings reveal that simulation performance is generally better for vote choice than for public opinions, more accurate in English-speaking countries, more effective in bipartisan systems than in multi-partisan systems, and stronger in democratic settings than in authoritarian regimes. These results contribute to enhancing our understanding and developing strategies to mitigate biases in AI applications within the field of computational social science.

翻译：本研究旨在识别并量化使用大语言模型模拟政治样本时存在的偏差，特别聚焦于投票选择与公共舆论两个维度。通过采用GPT-3.5-Turbo模型，并整合美国国家选举研究、德国纵向选举研究、坐标数据集及中国家庭追踪调查的数据，我们模拟了投票行为与公众意见。该方法使我们能够系统考察三类表征偏差：基于国家语言、人口统计群体及政治体制类型的差异。研究结果表明：模拟效果在投票选择方面普遍优于公共舆论；在英语国家更为准确；在两党制体系中比多党制体系更有效；在民主环境中比威权政体下表现更强。这些发现有助于深化我们对计算社会科学领域人工智能应用偏差的理解，并为制定缓解策略提供依据。