title: Multi-Agent Inference in Social Networks: A Finite Population Learning Approach.
abstract: 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 trade off 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.
authors.affiliation: Frederick L. Moore'18 Professor of Finance, Department of Operations Research and Finance Engineering, Princeton University, Princeton, NJ 08544 ( jqfan@princeton.edu ), and Adjunct Professor, School of International Economics and Management, Capital University of Economics and Business.
authors.forename: Jianqing
authors.initials: J
authors.lastname: Fan
authors.affiliation: Marshall School of Business, University of Southern California, CA 90089 ( xint@marshall.usc.edu ).
authors.forename: Xin
authors.initials: X
authors.lastname: Tong
authors.affiliation: Department of Economics, Harvard University, Cambridge, MA 02138 ( yaozeng@fas.harvard.edu ).
authors.forename: Yao
authors.initials: Y
authors.lastname: Zeng
keywords: Bayesian learning; Social networks; finite population learning; learning rates; multi-agent inference; perfect learning