Sensing Enhancement on Social Networks: The Role of Network Topology

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Abstract

Sensing and processing information from dynamically changing environments is essential for the survival of animal collectives and the functioning of human society. In this context, previous work has shown that communication between networked agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that, whilst bestowing advantages on a small group of agents, degree heterogeneity tends to impede overall sensing enhancement. In contrast, clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that ring graphs exhibit superior enhancement for large connectivity and that random graphs outperform for small connectivity. Further exploring the role of clustering and path lengths in small-world models, we find that sensing enhancement tends to be boosted in the small-world regime.
Original languageEnglish
Article number738
Number of pages21
JournalEntropy
Volume24
Issue number5
DOIs
Publication statusPublished - 22 May 2022

Keywords

  • complex networks
  • opinion dynamics
  • sensing enhancement
  • collective decision making
  • social learning
  • collective intelligence

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