Multi-Agent Inference in Social Networks: A Finite Population Learning Approach

Jianqing Fan, Xin Tong, Yao Zeng

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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 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.

Original languageEnglish (US)
Pages (from-to)149-158
Number of pages10
JournalJournal of the American Statistical Association
Volume110
Issue number509
DOIs
StatePublished - Jan 2 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Bayesian learning
  • Finite population learning
  • Learning rates
  • Perfect learning

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