Collective decision making in dynamic environments

Prasetyo, Judhi, De Masi, Giulia and Ferrante, Eliseo ORCID: https://orcid.org/0000-0002-2213-8356 (2019) Collective decision making in dynamic environments. Swarm Intelligence, 13 (3-4) . pp. 217-243. ISSN 1935-3820 (doi:10.1007/s11721-019-00169-8)

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Abstract

Abstract: Collective decision making is the ability of individuals to jointly make a decision without any centralized leadership, but only relying on local interactions. A special case is represented by the best-of-n problem, whereby the swarm has to select the best option among a set of n discrete alternatives. In this paper, we perform a thorough study of the best-of-n problem in dynamic environments, in the presence of two options (n=2). Site qualities can be directly measured by agents, and we introduce abrupt changes to these qualities. We introduce two adaptation mechanisms to deal with dynamic site qualities: stubborn agents and spontaneous opinion switching. Using both computer simulations and ordinary differential equation models, we show that: (i) The mere presence of the stubborn agents is enough to achieve adaptability, but increasing its number has detrimental effects on the performance; (ii) the system adaptation increases with increasing swarm size, while it does not depend on agents’ density, unless this is below a critical threshold; (iii) the spontaneous switching mechanism can also be used to achieve adaptability to dynamic environments, and its key parameter, the probability of switching, can be used to regulate the trade-off between accuracy and speed of adaptation.

Item Type: Article
Keywords (uncontrolled): Article, Dynamic environments, Collective decision making, Best-of-n, Swarm robotics, Complex adaptive systems
Research Areas: A. > School of Science and Technology
Item ID: 30576
Notes on copyright: © The Author(s) 2019
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Depositing User: Jisc Publications Router
Date Deposited: 26 Jun 2020 10:50
Last Modified: 26 Jun 2020 10:50
URI: https://eprints.mdx.ac.uk/id/eprint/30576

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