Stationary Social Learning in a Changing Environment
成果类型：
Article
署名作者：
Levy, Raphael; Peski, Marcin; Vieille, Nicolas
署名单位：
Hautes Etudes Commerciales (HEC) Paris; University of Toronto
刊物名称：
ECONOMETRICA
ISSN/ISSBN：
0012-9682
DOI：
10.3982/ECTA20475
发表日期：
2024
页码：
1939-1966
关键词：
摘要：
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.