Testing Models of Social Learning on Networks: Evidence From Two Experiments
研究了一个不完全信息的社会学习模型，通过印度村民和墨西哥学生的实验，发现贝叶斯学习者的比例分别为10%和50%，并分析了导致学习失败的网络结构。
We theoretically and empirically study an incomplete information model of social learning. Agents initially guess the binary state of the world after observing a private signal. In subsequent rounds, agents observe their network neighbors' previous guesses before guessing again. Agents are drawn from a mixture of learning types—Bayesian, who face incomplete information about others' types, and DeGroot, who average their neighbors' previous period guesses and follow the majority. We study (1) learning features of both types of agents in our incomplete information model; (2) what network structures lead to failures of asymptotic learning; (3) whether realistic networks exhibit such structures. We conducted lab experiments with 665 subjects in Indian villages and 350 students from ITAM in Mexico. We perform a reduced‐form analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the Indian‐villager and Mexican‐student samples, respectively.