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 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.
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
22 May 2022
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).
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.
Text
entropy-24-00738-v2
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Accepted/In Press date: 18 May 2022
Published date: 22 May 2022
Additional Information:
Funding Information:
Funding: MB acknowledges support from the Alan Turing Institute (EPSRC grant EP/N510129/1) and the Royal Society (grant IES R2 192206).
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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
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Date deposited: 16 Jun 2022 00:13
Last modified: 17 Mar 2024 03:54
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Author:
Markus Brede
Author:
Guillermo Romero Moreno
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