Multi-Agent Inference in Social Networks: A Finite Population Learning Approach
成果类型：
Article
署名作者：
Fan, Jianqing; Tong, Xin; Zeng, Yao
署名单位：
Princeton University; Capital University of Economics & Business; University of Southern California; Harvard University
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2014.893885
发表日期：
2015
页码：
149-158
关键词：
摘要：
When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to weigh the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data-collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning, to address whether with high probability, a large fraction of people in a given finite population network can make good inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows. Supplementary materials for this article are available online.