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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.

SUBMITTER: Fan J 

PROVIDER: S-EPMC4827608 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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

Fan Jianqing J   Tong Xin X   Zeng Yao Y  

Journal of the American Statistical Association 20150422 509


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. T  ...[more]

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