Merging and testing opinions
Statistics Theory
2014-05-30 v1 Statistics Theory
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.
Cite
@article{arxiv.1405.7481,
title = {Merging and testing opinions},
author = {Luciano Pomatto and Nabil Al-Najjar and Alvaro Sandroni},
journal= {arXiv preprint arXiv:1405.7481},
year = {2014}
}
Comments
Published in at http://dx.doi.org/10.1214/14-AOS1212 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)