学术讲座：Learning in Local Networks

演讲题目：Learning in Local Networks
演讲人：谭旭 博士
时间：2016年9月5日 14:00
地点：博学925室

演讲人简介：谭旭博士毕业于斯坦福大学，现为华盛顿大学经济系助理教授，研究领域为微观经济理论，博弈论，社会网络。曾在American Economic Review, Games and Economic Behavior, Review of Economic Design, Journal of Economic Theory, Economic Letters等杂志发表论文多篇。曾被《Pacific Standard》评为“The 30 Top Thinkers Under 30”。


Learning in Local Networks

Wei Li and Xu Tan 

Agents in a network want to learn the true state of the world from their own signals and reports from immediate neighbors. Each agent only knows her local network, consisting of her immediate neighbors and any connections among them. In each period, every agent updates her own estimates about the state distribution based on perceived new information. She also forms estimates about each neighbor's estimates given the new information she thinks the neighbor has received. Whenever a neighbor's report differs from what the agent thinks he should report, the agent attributes the difference to new information. Agents learn correctly in any network if their information structures are partitional. They can also do so for more general information structures if the network is a social quilt, a tree-like union of fully connected subnetworks. Otherwise, agents may fail to learn despite an arbitrarily large number of correct signals.