REU Project

Social learning studies how people combine their own private information with the choices of others. Although observing others can improve decision-making, it can also create information cascades, where early mistakes lead an entire population away from the truth. We investigate three aspects of this problem. First, we show that predicting the behavior and accuracy of social-learning networks is computationally difficult, even in several restricted settings. Second, we introduce a multipass model in which agents may repeatedly revise their decisions. While repeated updates can improve learning on some networks, we construct examples where they make learning strictly worse. Third, we study incentives that reward agents differently depending on how many others have already made the same correct prediction. We show that slowly decreasing rewards can eliminate permanent herding and lead almost everyone to learn the truth, whereas rewards that decrease too quickly can prevent learning.