Robot swarm democracy: the importance of informed individuals against zealots
Masi, Giulia De ORCID: https://orcid.org/0000-0003-3284-880X, Prasetyo, Judhi, Zakir, Raina, Mankovskii, Nikita, Ferrante, Eliseo and Tuci, Elio
(2021)
Robot swarm democracy: the importance of informed individuals against zealots.
Swarm Intelligence, 15
(4)
.
pp. 315-338.
ISSN 1935-3812
[Article]
(doi:10.1007/s11721-021-00197-3)
|
PDF
- Published version (with publisher's formatting)
Available under License Creative Commons Attribution 4.0. Download (2MB) | Preview |
Abstract
Abstract: In this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule. We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments. We find that (1) if the number of zealots for the lowest quality option is not too high, the decision-making process is driven toward the highest quality option; (2) this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots; (3) when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents.
Item Type: | Article |
---|---|
Keywords (uncontrolled): | Article, Collective decision-making, Swarm intelligence, Swarm robotics, Stubborn agents |
Research Areas: | A. > School of Science and Technology |
Item ID: | 34230 |
Notes on copyright: | © The Author(s) 2021
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Useful Links: | |
Depositing User: | Jisc Publications Router |
Date Deposited: | 01 Dec 2021 15:50 |
Last Modified: | 01 Dec 2021 15:50 |
URI: | https://eprints.mdx.ac.uk/id/eprint/34230 |
Actions (login required)
![]() |
View Item |
Statistics
Additional statistics are available via IRStats2.