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Degree-Weighted Social Learning

Author

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

Abstract

We study social learning in which agents weight neighbors' opinions differently based on their degrees, capturing situations in which agents place more trust in well-connected individuals or, conversely, discount their influence. We derive asymptotic properties of learning outcomes in large stochastic networks and analyze how the weighting rule affects societal wisdom and convergence speed. We find that assigning greater weight to higher-degree neighbors harms wisdom but has a non-monotonic effect on convergence speed, depending on the diversity of views within high- and low-degree groups, highlighting a potential trade-off between convergence speed and wisdom.

Suggested Citation

  • Chen Cheng & Xiao Han & Xin Tong & Yusheng Wu & Yiqing Xing, 2023. "Degree-Weighted Social Learning," Papers 2311.07010, arXiv.org, revised Dec 2025.
    • Handle: RePEc:arx:papers:2311.07010
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    File URL: http://arxiv.org/pdf/2311.07010
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