  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.
