This article summarizes work by Yaomin Jianga, Qingtian Mia and Lusha Zhu on how people learn in social networks. Their paper in Nature Neuroscience is paywalled but you can read the preprint here (23 page PDF). It gets more and more interesting the deeper you delve into it. "The researchers found that when making decisions in networked environments, the extent to which participants updated their beliefs varies according to the network locations of their neighbors. This extent varied based on how connected the participants were to neighbors in their network - something that is very different to belief updating in non-network environments." In social networks, this phenomenon is described by DeGroot learning, in which people "communicate with other agents (where) links between agents (who knows whom) and the weight they put on each other's opinions is represented by a trust matrix ... (which is) in a one-to-one relationship with a weighted, directed graph."
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