SOCIAL LEARNING WITH TIME-VARYING WEIGHTS  

作　　者：LIU Qipeng FANG Aili WANG Lin WANG Xiaofan 

机构地区：[1]Department of Automation,Shanghai Jiao Tong University,and Key Laboratory of System Control and Information Processing,Ministry of Education of China

出　　处：《Journal of Systems Science & Complexity》2014年第3期581-593,共13页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.61074125 and 61104137;the Science Fund for Creative Research Groups of the National Natural Science Foundation of China under Grant No.61221003;the National Key Basic Research Program (973 Program) of China under Grant No.2010CB731403

摘　　要：This paper investigates a non-Bayesian social learning model, in which each individual updates her beliefs based on private signals as well as her neighbors＇ beliefs. The private signM is involved in the updating process through Bayes＇ rule, and the neighbors＇ beliefs are embodied in through a weighted average form, where the weights are time-varying. The authors prove that agents eventually have correct forecasts for upcoming signals, and all the beliefs of agents reach a consensus. In addition, if there exists no state that is observationally equivalent to the true state from the point of view of all agents, the authors show that the consensus belief of the whole group eventually reflects the true state.

关 键 词：CONSENSUS social learning social networks time-varying weights. 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]