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From Authority-Respect to Grassroots-Dissent: Degree-Weighted Social Learning and Convergence Speed

Author

Listed:
  • Chen Cheng
  • Xiao Han
  • Xin Tong
  • Yusheng Wu
  • Yiqing Xing

Abstract

Opinions are influenced by neighbors, with varying degrees of emphasis based on their connections. Some may value more connected neighbors' views due to authority respect, while others might lean towards grassroots perspectives. The emergence of ChatGPT could signify a new ``opinion leader'' whose views people put a lot of weight on. This study introduces a degree-weighted DeGroot learning model to examine the effects of such belief updates on learning outcomes, especially the speed of belief convergence. We find that greater respect for authority doesn't guarantee faster convergence. The influence of authority respect is non-monotonic. The convergence speed, influenced by increased authority-respect or grassroots dissent, hinges on the unity of elite and grassroots factions. This research sheds light on the growing skepticism towards public figures and the ensuing dissonance in public debate.

Suggested Citation

  • Chen Cheng & Xiao Han & Xin Tong & Yusheng Wu & Yiqing Xing, 2023. "From Authority-Respect to Grassroots-Dissent: Degree-Weighted Social Learning and Convergence Speed," Papers 2311.07010, arXiv.org.
    • Handle: RePEc:arx:papers:2311.07010
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    File URL: http://arxiv.org/pdf/2311.07010
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