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Behavioural specialization and learning in social networks

Interactions in social groups can promote behavioural specialization. One way this can happen is when individuals engage in activities with two behavioural options and learn which option to choose. We analyse interactions in groups where individuals learn from playing games with two actions and nega...

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Detalles Bibliográficos
Autores principales: Leimar, Olof, Dall, Sasha R. X., Houston, Alasdair I., McNamara, John M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363987/
https://www.ncbi.nlm.nih.gov/pubmed/35946152
http://dx.doi.org/10.1098/rspb.2022.0954
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author Leimar, Olof
Dall, Sasha R. X.
Houston, Alasdair I.
McNamara, John M.
author_facet Leimar, Olof
Dall, Sasha R. X.
Houston, Alasdair I.
McNamara, John M.
author_sort Leimar, Olof
collection PubMed
description Interactions in social groups can promote behavioural specialization. One way this can happen is when individuals engage in activities with two behavioural options and learn which option to choose. We analyse interactions in groups where individuals learn from playing games with two actions and negatively frequency-dependent payoffs, such as producer–scrounger, caller–satellite, or hawk–dove games. Group members are placed in social networks, characterized by the group size and the number of neighbours to interact with, ranging from just a few neighbours to interactions between all group members. The networks we analyse include ring lattices and the much-studied small-world networks. By implementing two basic reinforcement-learning approaches, action–value learning and actor–critic learning, in different games, we find that individuals often show behavioural specialization. Specialization develops more rapidly when there are few neighbours in a network and when learning rates are high. There can be learned specialization also with many neighbours, but we show that, for action–value learning, behavioural consistency over time is higher with a smaller number of neighbours. We conclude that frequency-dependent competition for resources is a main driver of specialization. We discuss our theoretical results in relation to experimental and field observations of behavioural specialization in social situations.
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spelling pubmed-93639872022-08-12 Behavioural specialization and learning in social networks Leimar, Olof Dall, Sasha R. X. Houston, Alasdair I. McNamara, John M. Proc Biol Sci Behaviour Interactions in social groups can promote behavioural specialization. One way this can happen is when individuals engage in activities with two behavioural options and learn which option to choose. We analyse interactions in groups where individuals learn from playing games with two actions and negatively frequency-dependent payoffs, such as producer–scrounger, caller–satellite, or hawk–dove games. Group members are placed in social networks, characterized by the group size and the number of neighbours to interact with, ranging from just a few neighbours to interactions between all group members. The networks we analyse include ring lattices and the much-studied small-world networks. By implementing two basic reinforcement-learning approaches, action–value learning and actor–critic learning, in different games, we find that individuals often show behavioural specialization. Specialization develops more rapidly when there are few neighbours in a network and when learning rates are high. There can be learned specialization also with many neighbours, but we show that, for action–value learning, behavioural consistency over time is higher with a smaller number of neighbours. We conclude that frequency-dependent competition for resources is a main driver of specialization. We discuss our theoretical results in relation to experimental and field observations of behavioural specialization in social situations. The Royal Society 2022-08-10 2022-08-10 /pmc/articles/PMC9363987/ /pubmed/35946152 http://dx.doi.org/10.1098/rspb.2022.0954 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Behaviour
Leimar, Olof
Dall, Sasha R. X.
Houston, Alasdair I.
McNamara, John M.
Behavioural specialization and learning in social networks
title Behavioural specialization and learning in social networks
title_full Behavioural specialization and learning in social networks
title_fullStr Behavioural specialization and learning in social networks
title_full_unstemmed Behavioural specialization and learning in social networks
title_short Behavioural specialization and learning in social networks
title_sort behavioural specialization and learning in social networks
topic Behaviour
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363987/
https://www.ncbi.nlm.nih.gov/pubmed/35946152
http://dx.doi.org/10.1098/rspb.2022.0954
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