Abstract
Opinion dynamics in social networks has been widely studied in recent years, mostly by considering exchanges of opinions among neighboring agents. This paper addresses a scenario where the agents make decisions repeatedly on two hypotheses and where agents only exchange decisions. Motivated by the Bayesian models in the literature of human cognition, we model this learning procedure by the Bayes' rule. The social belief of each agent is defined to be the posterior of one of the hypotheses conditioned on the information it obtained from the society. We show that under certain conditions, once the social belief evolves to some region, the agent will refuse to change its belief. We demonstrate the asymptotical properties of the proposed model by computer simulations.
Original language | English (US) |
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Title of host publication | 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4588-4592 |
Number of pages | 5 |
ISBN (Electronic) | 9781479999880 |
DOIs | |
State | Published - May 18 2016 |
Event | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China Duration: Mar 20 2016 → Mar 25 2016 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2016-May |
ISSN (Print) | 1520-6149 |
Other
Other | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 |
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Country/Territory | China |
City | Shanghai |
Period | 3/20/16 → 3/25/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Bayesian learning
- Opinion dynamics
- voter model