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Sensing enhancement on social networks: the role of network topology

Sensing enhancement on social networks: the role of network topology
Sensing enhancement on social networks: the role of network topology
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 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.
collective decision making, collective intelligence, complex networks, opinion dynamics, sensing enhancement, social learning
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Romero Moreno, Guillermo
8c2f32d6-b0b5-4563-af22-c08b410b867f
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Romero Moreno, Guillermo
8c2f32d6-b0b5-4563-af22-c08b410b867f

Brede, Markus and Romero Moreno, Guillermo (2022) Sensing enhancement on social networks: the role of network topology. Entropy, 24 (5), [738]. (doi:10.3390/e24050738).

Record type: Article

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 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.

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Accepted/In Press date: 18 May 2022
Published date: 22 May 2022
Keywords: collective decision making, collective intelligence, complex networks, opinion dynamics, sensing enhancement, social learning

Identifiers

Local EPrints ID: 457708
URI: http://eprints.soton.ac.uk/id/eprint/457708
PURE UUID: 40e54493-2e68-4e48-97bb-f8ce843dc2b0
ORCID for Guillermo Romero Moreno: ORCID iD orcid.org/0000-0002-0316-8306

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Date deposited: 16 Jun 2022 00:13
Last modified: 16 Jun 2022 01:54

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Contributors

Author: Markus Brede
Author: Guillermo Romero Moreno ORCID iD

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