Social Learning with Uninformed Agents:Convergence and Efficiency
Almost all existing social learning models assume that each agent can perceive her private signal which is used in updating her belief.In this work,we assume that there are some uninformed agents in the network which cannot observe their private signals and update their beliefs just based on the beliefs of their neighbors.We prove that under mild assumptions,even one informed agent is enough to lead all agents in the network eventually learn the true state of the world almost surely.Furthermore,we show through simulation that in a heterogeneous undirected network,it is more efficient to have a few hub agents as the informed agents which can observe their signals,and the convergence speed is almost the same as that when all agents are informed agents.
HUANG He LIU Qipeng WANG Lin WANG Xiaofan
Shanghai Jiao Tong University,and Key Laboratory of System Control and Information Processing,Ministry of Education of China,Shanghai 200240,P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-5
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）