Non-Bayesian Learning in Social Networks with Time-varying Weights

摘要：

This paper investigates an social learning model with time-varying weights,in which the individual updates her belief through observing private signal caused by social event and communicating with those regarded as neighbors in the sense of network topology.The private signal is involved in the updating law through Bayes’rule.During the communication with neighbors,the individual obtains weighted average of others’beliefs.Using the convergence property of the transition matrix and coef cient of ergodicity,we show that,under mild assumptions,repeated observation and communications can lead beliefs of the entire group to the true state of the social event.

作者: LIU Qipeng FANG Aili WANG Lin WANG Xiaofan

作者单位: Department of Automation,Shanghai Jiao Tong University,and Key Laboratory of System Control and Information Processing,Ministry of Education of China,Shanghai 200240,P.R.China

会议类型: 国际会议

会议名称: The 30th Chinese Control Conference(第三十届中国控制会议)

会议地点: 烟台

会议语种:英文

页码: 1-4

在线出版日期: 2011-07-01（万方平台首次上网日期，不代表论文的发表时间）