Consensus and Disagreement: Information Aggregation under (Not So) Naive Learning

• 成果类型: Article
• 署名作者: Banerjee, Abhijit; Compte, Olivier
• 署名单位: Massachusetts Institute of Technology (MIT); Paris School of Economics; Institut Polytechnique de Paris; Ecole des Ponts ParisTech
• 刊物名称: JOURNAL OF POLITICAL ECONOMY
• ISSN/ISSBN: 0022-3808
• DOI: 10.1086/729448
• 发表日期: 2024
• 页码: 2790-2829
• 关键词: social-influence; others; news

摘要

We explore a model of non-Bayesian information aggregation in networks. Agents noncooperatively choose among Friedkin-Johnsen-type aggregation rules to maximize payoffs. The DeGroot rule is chosen in equilibrium if and only if there is noiseless information transmission, leading to consensus. With noisy transmission, while some disagreement is inevitable, the optimal choice of rule amplifies the disagreement: even with little noise, individuals place substantial weight on their own initial opinion in every period, exacerbating the disagreement. We use this framework to think about equilibrium versus socially efficient choice of rules and its connection to polarization of opinions across groups.