Network Structure and Social Learning

被引：0

作者：

Dasaratha, Krishna ^{[1
]}

He, Kevin ^{[2
]}

机构：

[1] Yale Univ, New Haven, CT 06520 USA

[2] Univ Penn, Philadelphia, PA 19104 USA

来源：

ACM SIGECOM EXCHANGES | 2021年 / 19卷 / 02期

关键词：

Economics; Theory Social networks; Sequential social learning; Bayesian learning; Confounding;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We describe results from Dasaratha and He [DH21a] and Dasaratha and He [DH20] about how network structure influences social learning outcomes. These papers share a tractable sequential model that lets us compare learning dynamics across networks. With Bayesian agents, incomplete networks can generate informational confounding that makes learning arbitrarily inefficient. With naive agents, related forces can lead to mislearning.

引用

页码：62 / 67

页数：6

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