Stationary Social Learning in a Changing Environment
研究了在状态不断变化的环境中，社会学习如何运作。发现当状态接近持久时，多数人会达成共识，但共识行动与状态不完全相关，且信号精确时学习不完全，因为过去行动样本内相关性降低信息量和福利。
We consider social learning in a changing world. With changing states, societies can be responsive only if agents regularly act upon fresh information, which significantly limits the value of observational learning. When the state is close to persistent, a consensus whereby most agents choose the same action typically emerges. However, the consensus action is not perfectly correlated with the state, because societies exhibit inertia following state changes. When signals are precise enough, learning is incomplete, even if agents draw large samples of past actions, as actions then become too correlated within samples, thereby reducing informativeness and welfare.