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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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Detalles Bibliográficos
Autor principal: Hamann, Heiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
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.
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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