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Kinetic models of collective decision-making in the presence of equality bias

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  • Pareschi, Lorenzo
  • Vellucci, Pierluigi
  • Zanella, Mattia

Abstract

We introduce and discuss kinetic models describing the influence of the competence in the evolution of decisions in a multi-agent system. The original exchange mechanism, which is based on the human tendency to compromise and change opinion through self-thinking, is here modified to include the role of the agents’ competence. In particular, we take into account the agents’ tendency to behave in the same way as if they were as good, or as bad, as their partner: the so-called equality bias. This occurred in a situation where a wide gap separated the competence of group members. We discuss the main properties of the kinetic models and numerically investigate some examples of collective decision under the influence of the equality bias. The results confirm that the equality bias leads the group to suboptimal decisions.

Suggested Citation

  • Pareschi, Lorenzo & Vellucci, Pierluigi & Zanella, Mattia, 2017. "Kinetic models of collective decision-making in the presence of equality bias," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 201-217.
    • Handle: RePEc:eee:phsmap:v:467:y:2017:i:c:p:201-217
    DOI: 10.1016/j.physa.2016.10.003
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    References listed on IDEAS

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    1. Galam, Serge & Zucker, Jean-Daniel, 2000. "From individual choice to group decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 644-659.
    2. Lorenzo Pareschi & Giuseppe Toscani, 2014. "Wealth distribution and collective knowledge. A Boltzmann approach," Papers 1401.4550, arXiv.org.
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    Cited by:

    1. Loretta Mastroeni & Maurizio Naldi & Pierluigi Vellucci, 2019. "Personal Finance Decisions with Untruthful Advisors: an Agent-Based Model," Papers 1909.06759, arXiv.org.
    2. Zanella, Mattia, 2020. "Structure preserving stochastic Galerkin methods for Fokker–Planck equations with background interactions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 168(C), pages 28-47.
    3. Wang, Lingling & Lai, Shaoyong & Sun, Rongmei, 2022. "Optimal control about multi-agent wealth exchange and decision-making competence," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    4. Giacomo Dimarco & Giuseppe Toscani & Mattia Zanella, 2024. "A multi-agent description of the influence of higher education on social stratification," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 19(3), pages 493-521, July.
    5. Pierluigi Vellucci, 2021. "A critique of financial neoliberalism: a perspective combining multidisciplinary methods and commodity markets," SN Business & Economics, Springer, vol. 1(3), pages 1-11, March.
    6. J. Franceschi & L. Pareschi & M. Zanella, 2022. "From agent-based models to the macroscopic description of fake-news spread: the role of competence in data-driven applications," Partial Differential Equations and Applications, Springer, vol. 3(6), pages 1-26, December.

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