Asymptotic learning on Bayesian social networks

• 成果类型: Article
• 署名作者: Mossel, Elchanan; Sly, Allan; Tamuz, Omer
• 署名单位: Weizmann Institute of Science; University of California System; University of California Berkeley
• 刊物名称: PROBABILITY THEORY AND RELATED FIELDS
• ISSN/ISSBN: 0178-8051
• DOI: 10.1007/s00440-013-0479-y
• 发表日期: 2014
• 页码: 127-157
• 关键词: CONSENSUS; COMMUNICATION; KNOWLEDGE

摘要

We study a standard model of economic agents on the nodes of a social network graph who learn a binary state of the world , from initial signals, by repeatedly observing each other's best guesses. Asymptotic learning is said to occur on a family of graphs with if with probability tending to as all agents in eventually estimate correctly. We identify sufficient conditions for asymptotic learning and contruct examples where learning does not occur when the conditions do not hold.