Projects per year
Abstract
In decentralised autonomous systems it is the interactions between individual agents which govern the collective behaviours of the system. These local-level interactions are themselves often governed by an underlying network structure.
These networks are particularly important for collective learning and decision-making whereby agents must gather evidence from their environment and propagate this information to other agents in the system. Models for collective behaviours may often rely upon the assumption of total connectivity between agents to provide effective information sharing within the system, but this assumption may be ill-advised. In this paper we investigate the impact that the underlying network has on performance in the context of collective learning. Through simulations we study small-world networks with varying levels of connectivity and randomness and conclude that totally-connected networks result in higher average error when compared to networks with less connectivity. Furthermore, we show that networks of high regularity outperform networks with increasing levels of random connectivity.
These networks are particularly important for collective learning and decision-making whereby agents must gather evidence from their environment and propagate this information to other agents in the system. Models for collective behaviours may often rely upon the assumption of total connectivity between agents to provide effective information sharing within the system, but this assumption may be ill-advised. In this paper we investigate the impact that the underlying network has on performance in the context of collective learning. Through simulations we study small-world networks with varying levels of connectivity and randomness and conclude that totally-connected networks result in higher average error when compared to networks with less connectivity. Furthermore, we show that networks of high regularity outperform networks with increasing levels of random connectivity.
| Original language | English |
|---|---|
| Title of host publication | The Impact of Network Connectivity on Collective Learning |
| Number of pages | 13 |
| Publication status | Accepted/In press - 15 Feb 2021 |
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Dive into the research topics of 'The Impact of Network Connectivity on Collective Learning'. Together they form a unique fingerprint.Projects
- 1 Finished
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T-B PHASE: Prosperity Partnership with Thales
Richards, A. G. (Principal Investigator), Wilson, R. E. (Co-Investigator), Johnson, A. (Collaborator), Bullock, S. (Co-Investigator), Lawry, J. (Co-Investigator), Noyes, J. M. (Co-Investigator), Hauert, S. (Co-Investigator), Bode, N. W. F. (Co-Investigator), Pitonakova, L. (Researcher), Kent, T. (Researcher), Crosscombe, M. (Researcher), Zanatto, D. (Researcher), Alkan, B. (Researcher), Drury, K. L. (Manager), Hogg, E. (Student), Bonnell, W. D. (Student), Bennett, C. (Student), Clarke, C. E. M. (Student), Potts, M. W. (Student), Sartor, P. N. (Collaborator), Harvey, D. (Collaborator), Rayneau-Kirkhope, B. (Collaborator), Galvin, K. (Collaborator), Lam, J. (Collaborator), Barden, E. (Collaborator), Chattington, M. (Collaborator), Radanovic, M. (Researcher), Morey, E. J. (Student), Ball, M. (Co-Principal Investigator), Hunt, E. R. (Collaborator), Richards, A. G. (Principal Investigator), Radanovic, M. (Researcher), Morey, E. J. (Student), Steane, V. (Collaborator), Reed Edworthy, J. (Collaborator) & Hart, S. G. (Student)
1/10/17 → 31/03/23
Project: Research