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dc.contributor.authorXia, W.
dc.contributor.authorYe, Mengbin
dc.contributor.authorLiu, J.
dc.contributor.authorCao, M.
dc.contributor.authorSun, X.M.
dc.date.accessioned2021-02-23T01:22:10Z
dc.date.available2021-02-23T01:22:10Z
dc.date.issued2020
dc.identifier.citationXia, W. and Ye, M. and Liu, J. and Cao, M. and Sun, X.M. 2020. Analysis of a nonlinear opinion dynamics model with biased assimilation. Automatica. 120.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/82651
dc.identifier.doi10.1016/j.automatica.2020.109113
dc.description.abstract

© 2020 Elsevier Ltd This paper analyzes a nonlinear opinion dynamics model which generalizes the DeGroot model by introducing a bias parameter for each individual. The original DeGroot model is recovered when the bias parameter is equal to zero. The magnitude of this parameter reflects an individual's degree of bias when assimilating new opinions, and depending on the magnitude, an individual is said to have weak, intermediate, and strong bias. The opinions of the individuals lie between 0 and 1. It is shown that for strongly connected networks, the equilibria with all elements equal identically to the extreme value 0 or 1 is locally exponentially stable, while the equilibrium with all elements equal to the neutral consensus value of 1/2 is unstable. Regions of attraction for the extreme consensus equilibria are given. For the equilibrium consisting of both extreme values 0 and 1, which corresponds to opinion polarization according to the model, it is shown that the equilibrium is unstable for all strongly connected networks if individuals all have weak bias, becomes locally exponentially stable for complete and two-island networks if individuals all have strong bias, and its stability heavily depends on the network topology when individuals have intermediate bias. Analysis on star graphs and simulations show that additional equilibria may exist where individuals form clusters.

dc.titleAnalysis of a nonlinear opinion dynamics model with biased assimilation
dc.typeJournal Article
dcterms.source.volume120
dcterms.source.issn0005-1098
dcterms.source.titleAutomatica
dc.date.updated2021-02-23T01:22:09Z
curtin.departmentSchool of Elec Eng, Comp and Math Sci (EECMS)
curtin.accessStatusIn process
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidYe, Mengbin [0000-0003-1698-0173]
curtin.contributor.scopusauthoridYe, Mengbin [56203529600]


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