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Inevitability of Polarization in Geometric Opinion Exchange

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Listed:
  • Abdou Majeed Alidou
  • J'ulia Balig'acs
  • Max Hahn-Klimroth
  • Jan Hk{a}z{l}a
  • Lukas Hintze
  • Olga Scheftelowitsch

Abstract

Polarization and unexpected correlations between opinions on diverse topics (including in politics, culture and consumer choices) are an object of sustained attention. However, numerous theoretical models do not seem to convincingly explain these phenomena. This paper is motivated by a recent line of work, studying models where polarization can be explained in terms of biased assimilation and geometric interplay between opinions on various topics. The agent opinions are represented as unit vectors on a multidimensional sphere and updated according to geometric rules. In contrast to previous work, we focus on the classical opinion exchange setting, where the agents update their opinions in discrete time steps, with a pair of agents interacting randomly at every step. The opinions are updated according to an update rule belonging to a general class. Our findings are twofold. First, polarization appears to be ubiquitous in the class of models we study, requiring only relatively modest assumptions reflecting biased assimilation. Second, there is a qualitative difference between two-dimensional dynamics on the one hand, and three or more dimensions on the other. Accordingly, we prove almost sure polarization for a large class of update rules in two dimensions. Then, we prove polarization in three and more dimensions in more limited cases and try to shed light on central difficulties that are absent in two dimensions.

Suggested Citation

  • Abdou Majeed Alidou & J'ulia Balig'acs & Max Hahn-Klimroth & Jan Hk{a}z{l}a & Lukas Hintze & Olga Scheftelowitsch, 2024. "Inevitability of Polarization in Geometric Opinion Exchange," Papers 2402.08446, arXiv.org.
  • Handle: RePEc:arx:papers:2402.08446
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    References listed on IDEAS

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    1. Michel Grabisch & Agnieszka Rusinowska, 2020. "A Survey on Nonstrategic Models of Opinion Dynamics," Games, MDPI, vol. 11(4), pages 1-29, December.
    2. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
    3. Jacopo Perego & Sevgi Yuksel, 2022. "Media Competition and Social Disagreement," Econometrica, Econometric Society, vol. 90(1), pages 223-265, January.
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