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Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups

Cooperation in human groups is challenging, and various mechanisms are required to sustain it, although it nevertheless usually decays over time. Here, we perform theoretically informed experiments involving networks of humans (1,024 subjects in 64 networks) playing a public-goods game to which we s...

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Detalles Bibliográficos
Autores principales: Shirado, Hirokazu, Christakis, Nicholas A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7452167/
https://www.ncbi.nlm.nih.gov/pubmed/32823053
http://dx.doi.org/10.1016/j.isci.2020.101438
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author Shirado, Hirokazu
Christakis, Nicholas A.
author_facet Shirado, Hirokazu
Christakis, Nicholas A.
author_sort Shirado, Hirokazu
collection PubMed
description Cooperation in human groups is challenging, and various mechanisms are required to sustain it, although it nevertheless usually decays over time. Here, we perform theoretically informed experiments involving networks of humans (1,024 subjects in 64 networks) playing a public-goods game to which we sometimes added autonomous agents (bots) programmed to use only local knowledge. We show that cooperation can not only be stabilized, but even promoted, when the bots intervene in the partner selections made by the humans, re-shaping social connections locally within a larger group. Cooperation rates increased from 60.4% at baseline to 79.4% at the end. This network-intervention strategy outperformed other strategies, such as adding bots playing tit-for-tat. We also confirm that even a single bot can foster cooperation in human groups by using a mixed strategy designed to support the development of cooperative clusters. Simple artificial intelligence can increase the cooperation of groups.
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spelling pubmed-74521672020-08-31 Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups Shirado, Hirokazu Christakis, Nicholas A. iScience Article Cooperation in human groups is challenging, and various mechanisms are required to sustain it, although it nevertheless usually decays over time. Here, we perform theoretically informed experiments involving networks of humans (1,024 subjects in 64 networks) playing a public-goods game to which we sometimes added autonomous agents (bots) programmed to use only local knowledge. We show that cooperation can not only be stabilized, but even promoted, when the bots intervene in the partner selections made by the humans, re-shaping social connections locally within a larger group. Cooperation rates increased from 60.4% at baseline to 79.4% at the end. This network-intervention strategy outperformed other strategies, such as adding bots playing tit-for-tat. We also confirm that even a single bot can foster cooperation in human groups by using a mixed strategy designed to support the development of cooperative clusters. Simple artificial intelligence can increase the cooperation of groups. Elsevier 2020-08-06 /pmc/articles/PMC7452167/ /pubmed/32823053 http://dx.doi.org/10.1016/j.isci.2020.101438 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shirado, Hirokazu
Christakis, Nicholas A.
Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups
title Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups
title_full Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups
title_fullStr Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups
title_full_unstemmed Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups
title_short Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups
title_sort network engineering using autonomous agents increases cooperation in human groups
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7452167/
https://www.ncbi.nlm.nih.gov/pubmed/32823053
http://dx.doi.org/10.1016/j.isci.2020.101438
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