Hirofumi Miwa

Does permitting voters to select multiple candidates in majoritarian elections increase diversity among those elected? While majoritarian systems typically use single-vote ballots, research suggests that allowing multiple selections may increase the representation of women and racial minorities. However, empirical evidence regarding actual voter behavior remains limited. To address this gap, we conducted a survey experiment that varied the number of selectable candidates from one to three in multimember local elections. The results revealed that, under the multiple-vote condition, respondents were more likely to alternate by gender, particularly in their second- and third-ranked choices, supporting the theory that multiple voting fosters more diverse representation. Nevertheless, men often emerged as the first-ranked choice, giving them an overall advantage at the aggregate level.

Does permitting voters to select multiple candidates in majoritarian elections increase diversity among those elected? While majoritarian systems typically use single-vote ballots, research suggests that allowing for multiple selections may increase the representation of women and racial minorities. However, empirical evidence regarding actual voter behavior remains limited. To address this gap, we conducted a survey experiment that varied the number of selectable candidates from one to three in multi-member local elections. The results revealed that, under the multiple-vote condition, respondents were more likely to alternate by gender, particularly in their second- and third-ranked choices, supporting the theory that multiple voting fosters more diverse representation. Nevertheless, men often emerged as the first-ranked choice, giving them an overall advantage at the aggregate level.

Understanding the ideological positions of political parties is crucial for empirically testing various theories in political science. In Japan, some studies have assessed each party’s policy outlook through electoral platforms, candidate surveys, or expert surveys, with most studies focusing on the past thirty years. However, limited efforts have comprehensively and longitudinally quantified the conflicts between political parties. Although previous studies have used election-related data to reveal interparty conflicts during elections, interparty conflicts between elections remain unclear. This study determines the stance of Japanese political parties during parliamentary debates. We collected text data from committee speeches from the Diet Proceedings Database from 1959 to 2019 and conducted a quantitative text analysis. By applying the Wordshoal method, we first estimated political party positions on specific policy topics in the House of Representatives and the House of Councillors. Then, we aggregated these policy-specific positions yearly via dynamic factor analysis. Consequently, we succeeded in discerning the axis of conflict in Japanese parliamentary debates and evaluated the longitudinal one-dimensional ideal points for political parties. Additionally, we visualized the changes in each party’s ideological position, identified controversial topics, and explored how these topics changed over time, providing new insights into the study of Japanese politics.

Voter preferences for a politician’s gender have been considered a potential factor of women’s underrepresentation in politics. Although recent studies demonstrate that voters do not generally dislike women candidates in elections, they may still doubt women’s competence and view persuasive behavior as counter-stereotypical for women. This may lead to the prediction that female politicians are less effective in persuading voters through their policy statements compared to men. However, evidence on this issue is scarce and mixed, with existing studies often having notable weaknesses in their experimental design. To address these problems, we carefully designed and conducted two preregistered survey experiments in the U.S. and Japan, where electoral rules are candidate-centered, and women’s underrepresentation is severe compared to other developed countries. We asked respondents how much they agreed with the policy statement of a fictitious politician whose gender was randomized in a mocked campaign website or a municipal council newsletter. We also varied the statement’s issue area and ideological position to examine whether persuasion is more effective when it aligns with gender stereotypes. The results showed no significant difference in policy persuasiveness between women and men politicians, and post hoc equivalence tests suggest the nonexistence of even minimal effects in both countries. Moreover, we found partial evidence that women politicians had an advantage when they claimed gender-stereotype statements in Japan, while the opposite was true in the U.S.

Detecting and addressing discrimination is one of the most crucial ways for ethnic and sexual minorities and women to fully integrate into the society. However, humans often fail to correctly judge what constitutes discrimination. The recent rapid development of artificial intelligence (AI) based on large language models (LLMs) has shown promise in assisting with this task, although its performance is understudied. This study investigates how humans and LLM-based AI, such as OpenAI’s ChatGPT, detect the concept of ‘discrimination’ and how their representation differs. Specifically, surveys were conducted asking humans (Japanese respondents) and ChatGPT (GPT-4) to evaluate the degree of discrimination in hypothetical unequal treatment scenarios presented to them. The scenarios varied in terms of targets, including their ethnicity, gender, and sexuality, and types of discrimination, such as those based on tastes, stereotypes, and statistics. The results show that ChatGPT generally classifies the scenarios as more discriminatory than humans do. However, ChatGPT also shares a tendency with humans to be more tolerant of unequal treatment based on ethnicity and gender compared to sexuality, and it is less likely to detect statistical discrimination than taste- or stereotype-based discrimination. Although LLM-based AI presents a potential tool for addressing discrimination and can offer temporary solutions, it may not fully capture all types of discrimination.