Learning Dynamics in Social Networks
提出了一个可处理的贝叶斯学习模型，研究大型随机网络中个体是否采纳创新的决策，分析网络结构对学习动态和产品扩散的影响，发现无向网络和团结构会降低学习效果和福利。
This paper proposes a tractable model of Bayesian learning on large random networks where agents choose whether to adopt an innovation. We study the impact of the network structure on learning dynamics and product diffusion. In directed networks, all direct and indirect links contribute to agents' learning. In comparison, learning and welfare are lower in undirected networks and networks with cliques. In a rich class of networks, behavior is described by a small number of differential equations, making the model useful for empirical work.