@inproceedings{b88ca7de72db427c92d38a9b4ed07fc2,
title = "Bayesian learning in a network with multi-hypothesis decision exchanges",
abstract = "Opinion dynamics and its understanding in social networks is an emerging field of research in recent years. Existing work mainly considers direct exchanges of opinions among agents under certain conditions. This paper addresses a problem where the agents of a network make and exchange decisions repeatedly in a multi-hypothesis scenario and learn from the neighbors' decisions. Two models are proposed where the agents of the network use quasi-Bayesian learning to extract information about the true hypothesis from the neighbors' decisions. Theoretical analysis is provided about the conditions of a setting when agents become stubborn, that is, when they do not change their opinions anymore. We have run computer simulations to demonstrate the asymptotical properties of the proposed models. With our simulations we also show that, under one of the models, the agents of the network reach a consensus, and under the other, they form clusters.",
keywords = "Bayesian learning, DeGroot model, Decision exchanges, Opinion dynamics",
author = "Lingqing Gan and Yunlong Wang and Djuric, \{Petar M.\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952925",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4089--4093",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}