What preferences do voters hold over the composition of legislatures? We distinguish between self-centered, proportionality-based, and other-regarding representation preferences and explore which of these preference types best describes public opinion on the composition of legislatures. Using survey experiments fielded in eight democracies (Australia, Canada, Mexico, Spain, Switzerland, Portugal, Turkey, and the United States), we test how citizens (N=13,851) respond to varying levels of legislative misrepresentation across ideological, geographic, and gender dimensions. We find that citizens favor overrepresentation of their own ideological group and region, reflecting self-centered preferences. In contrast, both women and men support female overrepresentation. These patterns appear rooted in how citizens link descriptive representation and key elements of substantive representation such as policy responsiveness and outcomes. Our findings shed light on how misrepresentation of political and social groups in parliament affects democratic attitudes.

Artificial intelligence (AI) has become both a general-purpose economic technology and an increasingly salient military capability, while geopolitical rivalry has intensified across trade, standards, and security. This paper connects these shifts by tracking when competition-centered AI narratives arose and evaluating whether military adoption followed. We construct a newspaper-based measure of geopolitical competition in AI from New York Times articles (1996–present) using large language model classifiers, then measure U.S. government AI adoption through federal procurement data (2018–2024), distinguishing defense from non-defense AI awards. We show that competitive AI narratives surge beginning in 2016 and are dominated by U.S.–China rivalry. Over the same period, Defense Department AI procurement grows disproportionately relative to non-defense AI, reshaping which firms and places receive defense contracts and deepening supplier relationships. The findings highlight how narratives may legitimize spending and potentially redirect innovation. These patterns also suggest that U.S. allies face meaningful choices about their AI strategies, both as general innovation policies and also as military decisions, as U.S. defense AI procurement continues to expand.

The advent of artificial intelligence (AI) enables researchers to qualitatively study preference formation through unstructured question formats that were previously too costly for inclusion in large-scale surveys. We evaluate whether AI interviewers, through their empathic capabilities, can elicit richer survey responses and reduce social desirability bias. Our experimental design randomly assigns respondents to one of three interview modes: (i) self-administered, open-ended questions, (ii) a non-empathic chatbot interviewer, or (iii) a highly empathic chatbot interviewer. We then examine response behavior across sensitive and non-sensitive topics, including vote intentions, abortion, hiring discrimination, and favorite candy. The findings reveal whether AI chatbots can qualitatively explore preference determinants while reducing social desirability bias compared to traditional interviews.