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Weighted Axelrod model: Different but similar

Author

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  • Kalinowska, Zuzanna
  • Dybiec, Bartłomiej

Abstract

The Axelrod model is a cellular automaton which can be used to describe the emergence and development of cultural domains, where culture is represented by a fixed number of cultural features taking a discrete set of possible values (traits). The Axelrod model is based on two sociological phenomena: homophily (a tendency for individuals to form bonds with people similar to themselves) and social influence (the way how individuals change their behavior due to social pressure). However, the Axelrod model does not take into account the fact that cultural attributes may have different significance for a given individual. This is a limitation in the context of how the model reflects mechanisms driving the evolution of real societies. The study aims to modify the Axelrod model by giving individual features different weights that have a decisive impact on the possibility of aligning cultural traits between (interacting) individuals. The comparison of the results obtained for the classic Axelrod model and its modified version shows that introduced weights have a significant impact on the course of the system development, in particular, increasing the final fragmentation of the system and increasing the time needed to reach the final state.

Suggested Citation

  • Kalinowska, Zuzanna & Dybiec, Bartłomiej, 2023. "Weighted Axelrod model: Different but similar," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    • Handle: RePEc:eee:phsmap:v:630:y:2023:i:c:s0378437123008361
    DOI: 10.1016/j.physa.2023.129281
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    References listed on IDEAS

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    1. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    2. Kacperski, Krzysztof & Hołyst, Janusz A., 2000. "Phase transitions as a persistent feature of groups with leaders in models of opinion formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 631-643.
    3. Pádraig MacCarron & Paul J Maher & Susan Fennell & Kevin Burke & James P Gleeson & Kevin Durrheim & Michael Quayle, 2020. "Agreement threshold on Axelrod’s model of cultural dissemination," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-13, June.
    4. Verma, Gunjan & Swami, Ananthram & Chan, Kevin, 2014. "The impact of competing zealots on opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 310-331.
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