Learning in networks with idiosyncratic agents

• 成果类型: Article
• 署名作者: Khandelwal, Vatsal
• 署名单位: University of Oxford
• 刊物名称: GAMES AND ECONOMIC BEHAVIOR
• ISSN/ISSBN: 0899-8256
• DOI: 10.1016/j.geb.2024.01.010
• 发表日期: 2024
• 页码: 225-249
• 关键词: Social networks; learning; beliefs; Behavioural; DeGroot

摘要

Individuals update their beliefs and respond to new information in idiosyncratic ways. I show that an individual's idiosyncrasies such as under -reaction, over -reaction, or frustration can have spillover effects and adversely affect the long run beliefs of society. I derive sufficient conditions for convergence of beliefs for all possible networks of agents with heterogeneous idiosyncrasies. Beliefs converge if the magnitude of over -reaction and frustration in any agent's network neighbourhood is below a threshold determined by how much they trust their own private signals. I find that the absence of disproportionately influential agents is not sufficient to ensure the accuracy of long -run beliefs if learning idiosyncrasies also grow with the network. Finally, I show that agent under -reaction can partition the network, create bottlenecks, and delay convergence. Simulations on artificial and Indian village networks validate the results.