Pathological Outcomes of Observational Learning
研究贝叶斯理性个体如何从他人的离散行为中顺序学习，发现异质性偏好可能导致类型特定的“羊群”行为，甚至出现一种新的稳健可能性：混淆学习，即信念收敛到历史无法提供明确教训的极限点。
This paper explores how Bayes-rational individuals learn sequentially from the discrete actions of others. Unlike earlier informational herding papers, we admit heterogeneous preferences. Not only may type-specific 'herds' eventually arise, but a new robust possibility emerges: confounded learning. Beliefs may converge to a limit point where history offers no decisive lessons for anyone, and each type's actions forever nontrivially split between two actions. To verify that our identified limit outcomes do arise, we exploit the Markov-martingale character of beliefs. Learning dynamics are stochastically stable near a fixed point in many Bayesian learning models like this one.