Multi-agent inference in social networks: a finite population learning approach

Jianqing Fan Xin Tong Yao Zeng

Statistics Theory and Methods mathscidoc:1912.43414

Journal of the American Statistical Association, 110, (509), 149-158, 2015.1
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 peoples 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, <i>finite population learning</i>, 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
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@inproceedings{jianqing2015multi-agent,
  title={Multi-agent inference in social networks: a finite population learning approach},
  author={Jianqing Fan, Xin Tong, and Yao Zeng},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114258438977974},
  booktitle={Journal of the American Statistical Association},
  volume={110},
  number={509},
  pages={149-158},
  year={2015},
}
Jianqing Fan, Xin Tong, and Yao Zeng. Multi-agent inference in social networks: a finite population learning approach. 2015. Vol. 110. In Journal of the American Statistical Association. pp.149-158. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114258438977974.
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