The hazards and benefits of condescension in social learning

• 成果类型: Article
• 署名作者: Arieli, Itai; Babichenko, Yakov; Muller, Stephan; Pourbabaee, Farzad; Tamuz, Omer
• 署名单位: Technion Israel Institute of Technology; University of Gottingen; California Institute of Technology
• 刊物名称: THEORETICAL ECONOMICS
• ISSN/ISSBN: 1933-6837
• DOI: 10.3982/TE5743
• 发表日期: 2025
• 关键词: others; MODEL

摘要

In a misspecified social learning setting, agents are condescending if they perceive their peers as having private information that is of lower quality than it is in reality. Applying this to a standard sequential model, we show that outcomes improve when agents are mildly condescending. In contrast, too much condescension leads to worse outcomes, as does anti-condescension.