Merging and testing opinions

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

We study the merging and the testing of opinions in the context of a prediction model. In the absence of incentive problems, opinions can be tested and rejected, regardless of whether or not data produces consensus among Bayesian agents. In contrast, in the presence of incentive problems, opinions can only be tested and rejected when data produces consensus among Bayesian agents. These results show a strong connection between the testing and the merging of opinions. They also relate the literature on Bayesian learning and the literature on testing strategic experts.

Original languageEnglish (US)
Pages (from-to)1003-1028
Number of pages26
JournalAnnals of Statistics
Volume42
Issue number3
DOIs
StatePublished - Jun 2014

Keywords

  • Bayesian learning
  • Test manipulation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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