Social Learning with Coarse Inference
研究了有限理性代理人通过观察前人决策和私人信号进行序列决策的社会学习过程，发现离散行动空间下即使信号精度无限也存在渐进无效率，而连续行动空间下代理人虽过度重视早期信号但最终能学会正确行动。
We study social learning by boundedly rational agents. Agents take a decision in sequence, after observing their predecessors and a private signal. They are unable to make perfect inferences from their predecessors' decisions: they only understand the relation between the aggregate distribution of actions and the state of nature, and make their inferences accordingly. We show that, in a discrete action space, even if agents receive signals of unbounded precision, there are asymptotic inefficiencies. In a continuous action space, compared to the rational case, agents overweight early signals. Despite this behavioral bias, eventually agents learn the realized state of the world and choose the correct action.