A Random Dictator Is All You Need

• 成果类型: Article
• 署名作者: Arieli, Itai; Babichenko, Yakov; Talgam-cohen, Inbal; Zabarnyi, Konstantin
• 署名单位: Technion Israel Institute of Technology; Tel Aviv University
• 刊物名称: AMERICAN ECONOMIC JOURNAL-MICROECONOMICS
• ISSN/ISSBN: 1945-7669
• DOI: 10.1257/mic.20230255
• 发表日期: 2025
• 页码: 66-96
• 关键词: Condorcet Jury Theorem; information aggregation; forecasts; combination

摘要

We study information aggregation with a decision-maker aggregating binary recommendations from symmetric agents. Each agent's recommendation depends on her private information about a hidden state. While the decision-maker knows the prior distribution over states and the marginal distribution of each agent's recommendation, the recommendations are adversarially correlated. The decision-maker's goal is choosing a robustly optimal aggregation rule. We prove that for a large number of agents for the three standard robustness paradigms (maximin, regret, and approximation ratio), the unique optimal aggregation rule is random dictator. We further characterize the minimal regret for any number of agents through concavification. (JEL D81, D82, D83)