Understanding Human Perception of Representation in Citizens' Assemblies: An Empirical Study

2026-09-23

AI Summary

研究通过随机联合实验解决公民大会代表性属性选择问题，发现政治立场和特定背景属性比人口统计学属性更重要，并建议设计时应考虑这些因素。

Abstract

Citizens' assemblies are deliberative bodies intended to form a microcosm of the population. Organizers rely on quota-based stratification and must decide which attributes define resemblance to the public. Yet meeting every quota can still leave a dimension citizens value unrepresented. We study this attribute-selection problem in general-purpose and climate-focused assemblies through randomized conjoint experiments. We find that demographic attributes matter for perceived representation, but political alignment and context-specific attributes such as climate concern exert a stronger influence. When both are shown in a climate-focused setting, each remains influential, with political alignment having the larger estimated marginal effect. We also examine the omission of a relevant stratification attribute. Panels stratified on demographics, even with political alignment included, match the observed pool's climate-concern distribution no better than uniform random samples. These results suggest that representation on a relevant topic-specific attribute cannot always be recovered through correlated demographic or political quotas, and may therefore require explicit stratification. Finally, we ask whether representation preferences can be learned from the observed profiles. We compare predictive models, from simple, interpretable matching rules to a learned metric and a respondent-conditioned utility model. Both learned models predict choices for respondents excluded from training with substantial accuracy, revealing generalizable structure without fully capturing these judgments. Together, these findings guide attribute selection in citizens' assemblies: designers should consider political and topic-specific dimensions alongside demographics, avoid assuming correlated proxies protect omitted dimensions, and use predictive models to diagnose how profiles shape representation choices.

Problem

• citizens' assemblies
• representation
• attribute selection
• political alignment
• climate concern

Innovation

• conjoint experiments
• perceived representation
• stratification
• predictive models
• representation preferences