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Entangled and correlated photon mixed strategy for social decision making

Collective decision making is important for maximizing total benefits while preserving equality among individuals in the competitive multi-armed bandit (CMAB) problem, wherein multiple players try to gain higher rewards from multiple slot machines. The CMAB problem represents an essential aspect of...

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Autores principales: Maeda, Shion, Chauvet, Nicolas, Saigo, Hayato, Hori, Hirokazu, Bachelier, Guillaume, Huant, Serge, Naruse, Makoto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921384/
https://www.ncbi.nlm.nih.gov/pubmed/33649385
http://dx.doi.org/10.1038/s41598-021-84199-5
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author Maeda, Shion
Chauvet, Nicolas
Saigo, Hayato
Hori, Hirokazu
Bachelier, Guillaume
Huant, Serge
Naruse, Makoto
author_facet Maeda, Shion
Chauvet, Nicolas
Saigo, Hayato
Hori, Hirokazu
Bachelier, Guillaume
Huant, Serge
Naruse, Makoto
author_sort Maeda, Shion
collection PubMed
description Collective decision making is important for maximizing total benefits while preserving equality among individuals in the competitive multi-armed bandit (CMAB) problem, wherein multiple players try to gain higher rewards from multiple slot machines. The CMAB problem represents an essential aspect of applications such as resource management in social infrastructure. In a previous study, we theoretically and experimentally demonstrated that entangled photons can physically resolve the difficulty of the CMAB problem. This decision-making strategy completely avoids decision conflicts while ensuring equality. However, decision conflicts can sometimes be beneficial if they yield greater rewards than non-conflicting decisions, indicating that greedy actions may provide positive effects depending on the given environment. In this study, we demonstrate a mixed strategy of entangled- and correlated-photon-based decision-making so that total rewards can be enhanced when compared to the entangled-photon-only decision strategy. We show that an optimal mixture of entangled- and correlated-photon-based strategies exists depending on the dynamics of the reward environment as well as the difficulty of the given problem. This study paves the way for utilizing both quantum and classical aspects of photons in a mixed manner for decision making and provides yet another example of the supremacy of mixed strategies known in game theory, especially in evolutionary game theory.
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spelling pubmed-79213842021-03-02 Entangled and correlated photon mixed strategy for social decision making Maeda, Shion Chauvet, Nicolas Saigo, Hayato Hori, Hirokazu Bachelier, Guillaume Huant, Serge Naruse, Makoto Sci Rep Article Collective decision making is important for maximizing total benefits while preserving equality among individuals in the competitive multi-armed bandit (CMAB) problem, wherein multiple players try to gain higher rewards from multiple slot machines. The CMAB problem represents an essential aspect of applications such as resource management in social infrastructure. In a previous study, we theoretically and experimentally demonstrated that entangled photons can physically resolve the difficulty of the CMAB problem. This decision-making strategy completely avoids decision conflicts while ensuring equality. However, decision conflicts can sometimes be beneficial if they yield greater rewards than non-conflicting decisions, indicating that greedy actions may provide positive effects depending on the given environment. In this study, we demonstrate a mixed strategy of entangled- and correlated-photon-based decision-making so that total rewards can be enhanced when compared to the entangled-photon-only decision strategy. We show that an optimal mixture of entangled- and correlated-photon-based strategies exists depending on the dynamics of the reward environment as well as the difficulty of the given problem. This study paves the way for utilizing both quantum and classical aspects of photons in a mixed manner for decision making and provides yet another example of the supremacy of mixed strategies known in game theory, especially in evolutionary game theory. Nature Publishing Group UK 2021-03-01 /pmc/articles/PMC7921384/ /pubmed/33649385 http://dx.doi.org/10.1038/s41598-021-84199-5 Text en © 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/.
spellingShingle Article
Maeda, Shion
Chauvet, Nicolas
Saigo, Hayato
Hori, Hirokazu
Bachelier, Guillaume
Huant, Serge
Naruse, Makoto
Entangled and correlated photon mixed strategy for social decision making
title Entangled and correlated photon mixed strategy for social decision making
title_full Entangled and correlated photon mixed strategy for social decision making
title_fullStr Entangled and correlated photon mixed strategy for social decision making
title_full_unstemmed Entangled and correlated photon mixed strategy for social decision making
title_short Entangled and correlated photon mixed strategy for social decision making
title_sort entangled and correlated photon mixed strategy for social decision making
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921384/
https://www.ncbi.nlm.nih.gov/pubmed/33649385
http://dx.doi.org/10.1038/s41598-021-84199-5
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