Complementary Information and Learning Traps*
研究了短期决策者从大量相关信息源中顺序选择以预测未知状态的社会学习模型，发现长期结果要么是高效信息聚合，要么是学习陷阱，并识别了决定因素。
Abstract We develop a model of social learning from complementary information: short-lived agents sequentially choose from a large set of flexibly correlated information sources for prediction of an unknown state, and information is passed down across periods. Will the community collectively acquire the best kinds of information? Long-run outcomes fall into one of two cases: (i) efficient information aggregation, where the community eventually learns as fast as possible; (ii) “learning traps,” where the community gets stuck observing suboptimal sources and information aggregation is inefficient. Our main results identify a simple property of the underlying informational complementarities that determines which occurs. In both regimes, we characterize which sources are observed in the long run and how often.