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Vector Opinion Dynamics In A Bounded Confidence Consensus Model

Author

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  • SANTO FORTUNATO

    (Fakultät für Physik, Universität Bielefeld, D-33501 Bielefeld, Germany;
    School of Informatics, Indiana University, Bloomington, IN 47408, USA;
    Dipartimento di Fisica e Astronomia and INFN sezione di Catania, Universita' di Catania, Catania I-95123, Italy)

  • VITO LATORA

    (Dipartimento di Fisica e Astronomia and INFN sezione di Catania, Universita' di Catania, Catania I-95123, Italy)

  • ALESSANDRO PLUCHINO

    (Dipartimento di Fisica e Astronomia and INFN sezione di Catania, Universita' di Catania, Catania I-95123, Italy)

  • ANDREA RAPISARDA

    (Dipartimento di Fisica e Astronomia and INFN sezione di Catania, Universita' di Catania, Catania I-95123, Italy)

Abstract

We study the continuum opinion dynamics of the compromise model of Krause and Hegselmann for a community of mutually interacting agents by solving numerically a rate equation. The opinions are here represented by two-dimensional vectors with real-valued components. We study the situation starting from a uniform probability distribution for the opinion configuration and for different shapes of the confidence range. In all cases, we find that the thresholds for consensus and cluster merging either coincide with their one-dimensional counterparts, or are very close to them. The symmetry of the final opinion configuration, when more clusters survive, is determined by the shape of the opinion space. If the latter is a square, which is the case we consider, the clusters in general occupy the sites of a square lattice, although we sometimes observe interesting deviations from this general pattern, especially near the center of the opinion space.

Suggested Citation

  • Santo Fortunato & Vito Latora & Alessandro Pluchino & Andrea Rapisarda, 2005. "Vector Opinion Dynamics In A Bounded Confidence Consensus Model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 16(10), pages 1535-1551.
    • Handle: RePEc:wsi:ijmpcx:v:16:y:2005:i:10:n:s0129183105008126
    DOI: 10.1142/S0129183105008126
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    Cited by:

    1. Christos Mavridis & Nikolas Tsakas, 2021. "Social Capital, Communication Channels and Opinion Formation," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 56(4), pages 635-678, May.
    2. Antonio Parravano & Ascensión Andina-Díaz & Miguel A Meléndez-Jiménez, 2016. "Bounded Confidence under Preferential Flip: A Coupled Dynamics of Structural Balance and Opinions," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-23, October.
    3. Mehrdad Agha Mohammad Ali Kermani & Reza Ghesmati & Masoud Jalayer, 2018. "Opinion-Aware Influence Maximization: How To Maximize A Favorite Opinion In A Social Network?," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-27, September.
    4. Petter Törnberg & Claes Andersson & Kristian Lindgren & Sven Banisch, 2021. "Modeling the emergence of affective polarization in the social media society," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-17, October.
    5. Biondo, A.E. & Brosio, G. & Pluchino, A. & Zanola, R., 2022. "Authoritarianism vs. democracy: Simulating responses to disease outbreaks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    6. G Jordan Maclay & Moody Ahmad, 2021. "An agent based force vector model of social influence that predicts strong polarization in a connected world," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-42, November.
    7. Rainer Hegselmann & Stefan König & Sascha Kurz & Christoph Niemann & Jörg Rambau, 2015. "Optimal Opinion Control: The Campaign Problem," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(3), pages 1-18.
    8. Jiangbo Zhang, 2019. "Opinion limits study for the multi-selection bounded confidence model," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-14, January.

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    Keywords

    Sociophysics; Monte Carlo simulations;

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