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Opinion Dynamics With Mobile Agents: Contrarian Effects by Spatial Correlations
We investigate the dynamics of opinion formation in a group of mobile agents with noisy perceptions. Two models are applied, the 2-state Galam opinion dynamics model with contrarians and an urn model of collective decision-making. It is shown that models built on the well-mixed assumption fail to re...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805993/ https://www.ncbi.nlm.nih.gov/pubmed/33500942 http://dx.doi.org/10.3389/frobt.2018.00063 |
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author | Hamann, Heiko |
author_facet | Hamann, Heiko |
author_sort | Hamann, Heiko |
collection | PubMed |
description | We investigate the dynamics of opinion formation in a group of mobile agents with noisy perceptions. Two models are applied, the 2-state Galam opinion dynamics model with contrarians and an urn model of collective decision-making. It is shown that models built on the well-mixed assumption fail to represent the dynamics of a simple scenario. The challenge of accounting for correlations in the agents' spatial distribution is overcome by different heuristics and supported by empirical investigations. We present a concise, simple 1-dimensional macroscopic modeling approach that can be tuned to correctly model spatial correlations. |
format | Online Article Text |
id | pubmed-7805993 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78059932021-01-25 Opinion Dynamics With Mobile Agents: Contrarian Effects by Spatial Correlations Hamann, Heiko Front Robot AI Robotics and AI We investigate the dynamics of opinion formation in a group of mobile agents with noisy perceptions. Two models are applied, the 2-state Galam opinion dynamics model with contrarians and an urn model of collective decision-making. It is shown that models built on the well-mixed assumption fail to represent the dynamics of a simple scenario. The challenge of accounting for correlations in the agents' spatial distribution is overcome by different heuristics and supported by empirical investigations. We present a concise, simple 1-dimensional macroscopic modeling approach that can be tuned to correctly model spatial correlations. Frontiers Media S.A. 2018-06-06 /pmc/articles/PMC7805993/ /pubmed/33500942 http://dx.doi.org/10.3389/frobt.2018.00063 Text en Copyright © 2018 Hamann. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Hamann, Heiko Opinion Dynamics With Mobile Agents: Contrarian Effects by Spatial Correlations |
title | Opinion Dynamics With Mobile Agents: Contrarian Effects by Spatial Correlations |
title_full | Opinion Dynamics With Mobile Agents: Contrarian Effects by Spatial Correlations |
title_fullStr | Opinion Dynamics With Mobile Agents: Contrarian Effects by Spatial Correlations |
title_full_unstemmed | Opinion Dynamics With Mobile Agents: Contrarian Effects by Spatial Correlations |
title_short | Opinion Dynamics With Mobile Agents: Contrarian Effects by Spatial Correlations |
title_sort | opinion dynamics with mobile agents: contrarian effects by spatial correlations |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805993/ https://www.ncbi.nlm.nih.gov/pubmed/33500942 http://dx.doi.org/10.3389/frobt.2018.00063 |
work_keys_str_mv | AT hamannheiko opiniondynamicswithmobileagentscontrarianeffectsbyspatialcorrelations |