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    Discrete-Time Polar Opinion Dynamics with Heterogeneous Individuals

    84260.pdf (344.5Kb)
    Access Status
    Open access
    Authors
    Liu, J.
    Ye, Mengbin
    Anderson, B.D.O.
    Basar, T.
    Nedic, A.
    Date
    2019
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Liu, J. and Ye, M. and Anderson, B.D.O. and Basar, T. and Nedic, A. 2018. Discrete-Time Polar Opinion Dynamics with Heterogeneous Individuals. In 57th IEEE Conference on Decision and Control (CDC), 17-19 Dec 2018, Miami, FL, USA.
    Source Title
    Proceedings of the IEEE Conference on Decision and Control
    DOI
    10.1109/CDC.2018.8619071
    ISBN
    9781538613955
    ISSN
    0743-1546
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160104500
    URI
    http://hdl.handle.net/20.500.11937/84356
    Collection
    • Curtin Research Publications
    Abstract

    This paper considers a discrete-time opinion dynamics model in which each individual's susceptibility to being influenced by others is dependent on her current opinion. We first propose a general opinion dynamics model based on the DeGroot model, with a general function to describe the functional dependence of each individual's susceptibility to her own opinion, and characterize the set of all equilibria and stability of nontrivial equilibria. We then consider two classes of functions in which the individual's susceptibility depends on the polarity of her opinion (i.e., how extreme her opinion is), and provide motivating social examples. First, we consider stubborn positives, who have reduced susceptibility if their opinions are at one end of the interval and increased susceptibility if their opinions are at the opposite end. Second, we consider stubborn extremists, who are less susceptible when they hold opinions at either end of the opinion interval. For each susceptibility model, we establish limiting behavior for different initial conditions. Networks consisting of individuals with both types of susceptibility functions are also considered.

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