A General Analysis of Sequential Social Learning
成果类型：
Article
署名作者：
Arieli, Itai; Mueller-Frank, Manuel
署名单位：
Technion Israel Institute of Technology; University of Navarra; IESE Business School
刊物名称：
MATHEMATICS OF OPERATIONS RESEARCH
ISSN/ISSBN：
0364-765X
DOI：
10.1287/moor.2020.1093
发表日期：
2021
页码：
1235-1249
关键词：
摘要：
This paper analyzes a sequential social learning game with a general utility function, state, and action space. We show that asymptotic learning holds for every utility function if and only if signals are totally unbounded, that is, the support of the private posterior probability of every event contains both zero and one. For the case of finitely many actions, we provide a sufficient condition for asymptotic learning depending on the given utility function. Finally, we establish that for the important class of simple utility functions with finitely many actions and states, pairwise unbounded signals, which generally are a strictly weaker notion than unbounded signals, are necessary and sufficient for asymptotic learning.