Information Heterogeneity and the Speed of Learning in Social Networks
Information Heterogeneity and the Speed of Learning in Social Networks
复制标题
社交网络中的信息异质性和学习速度
DOI：
10.2139/ssrn.2266979
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
A. Tahbaz
中科院分区：
文献类型：
--
作者：
A. Jadbabaie;Pooya Molavi;A. Tahbaz
本文探讨了社会网络的结构和不同代理人可用的信息质量如何决定社会学习的速度。为此，我们研究了DeGroot（1974）开创性模型的一个变体，根据该模型，智能体线性地将他们的个人经验与邻居的观点联合收割机结合起来。我们表明，学习率有一个简单的分析表征代理的信号结构和特征向量中心的相对熵。我们的特征确定了信息在整个社交网络中传播的方式对学习速率有着重要的影响。特别是，我们表明，当不同代理的信号结构的信息量在Blackwell（1953）的意义上是可比的，那么一个积极的信号质量和特征向量中心匹配最大化的学习率。另一方面，如果信息结构是这样的，每个人都拥有一些对学习至关重要的信息，那么当具有最佳信号的代理位于网络的外围时，学习的速度会更高。最后，我们表明，在社会网络结构的不对称程度起着关键作用的长期动态的信念。
This paper examines how the structure of a social network and the quality of information available to different agents determine the speed of social learning. To this end, we study a variant of the seminal model of DeGroot (1974), according to which agents linearly combine their personal experiences with the views of their neighbors. We show that the rate of learning has a simple analytical characterization in terms of the relative entropy of agents’ signal structures and their eigenvector centralities. Our characterization establishes that the way information is dispersed throughout the social network has non-trivial implications for the rate of learning. In particular, we show that when the informativeness of different agents’ signal structures are comparable in the sense of Blackwell (1953), then a positive assortative matching of signal qualities and eigenvector centralities maximizes the rate of learning. On the other hand, if information structures are such that each individual possesses some information crucial for learning, then the rate of learning is higher when agents with the best signals are located at the periphery of the network. Finally, we show that the extent of asymmetry in the structure of the social network plays a key role in the long-run dynamics of the beliefs.