Preferences, Homophily, and Social Learning
研究了网络中偏好异质性和同质性对贝叶斯社会学习的影响，发现网络连接密度决定了偏好多样性和同质性的作用方向：稀疏网络中同质性有益，密集网络中偏好多样性有益。
We study a sequential model of Bayesian social learning in networks in which agents have heterogeneous preferences, and neighbors tend to have similar preferences—a phenomenon known as homophily. We find that the density of network connections determines the impact of preference diversity and homophily on learning. When connections are sparse, diverse preferences are harmful to learning, and homophily may lead to substantial improvements. In contrast, in a dense network, preference diversity is beneficial. Intuitively, diverse ties introduce more independence between observations while providing less information individually. Homophilous connections individually carry more useful information, but multiple observations become redundant.