A model of cultural transmission by direct instruction: An exercise on replication and extension

This article replicates and extends an agent-based model of cultural transmission (Acerbi and Parisi, 2006). The original model uses artificial neural networks to inquire about the role of noise and selective cultural reproduction in imitation learning dynamics, both for static and dynamic environme...

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Autores:
Tipo de recurso:
Fecha de publicación:
2018
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/23703
Acceso en línea:
https://doi.org/10.1016/j.cogsys.2018.07.019
https://repository.urosario.edu.co/handle/10336/23703
Palabra clave:
Autonomous agents
Computational methods
Neural networks
Agent-based model
Cultural transmission
Direct instruction
Extension
Replication
Social learning
Teaching
Article
Exercise
Human
Imitation
Social learning
Sociology
Agent-based modeling
Cultural transmission
Direct instruction
Extension
Replication
Social learning
Rights
License
Abierto (Texto Completo)
id EDOCUR2_e771a73fa8f5e9a7e2a7f1cb3e6f9175
oai_identifier_str oai:repository.urosario.edu.co:10336/23703
network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
spelling 80041204600c4a62054-75a6-40ab-b7e8-fb54b0bd97042020-05-26T00:04:39Z2020-05-26T00:04:39Z2018This article replicates and extends an agent-based model of cultural transmission (Acerbi and Parisi, 2006). The original model uses artificial neural networks to inquire about the role of noise and selective cultural reproduction in imitation learning dynamics, both for static and dynamic environments. The replication tests the robustness of the original results, whereas the extension focuses on implementing an alternative type of learning: Direct instruction. The results of the extension suggest this type of learning could negatively affect the emergence of adaptive behavioral traits at the population level. Because of its reliance on explicit one-way communication and its reduced chance to question the traits transmitted, direct instruction might increase the time taken to find effective behavioral variants, in comparison with imitation. Yet, if the limit that defines inadequate behavior is chosen loosely enough, a sufficient amount of behavioral variations could be introduced in the behavioral pool so to ensure the development of highly adaptive variations. The text uses the implementation of direct instruction to discuss the role of extension in scientific endeavor, especially in interdisciplinary areas of research, such as the science of cultural evolution or agent-based computational social science. © 2018 Elsevier B.V.application/pdfhttps://doi.org/10.1016/j.cogsys.2018.07.01913890417https://repository.urosario.edu.co/handle/10336/23703engElsevier B.V.465450Cognitive Systems ResearchVol. 52Cognitive Systems Research, ISSN:13890417, Vol.52,(2018); pp. 450-465https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050976626&doi=10.1016%2fj.cogsys.2018.07.019&partnerID=40&md5=4a314228a82b6819d6853fb4bfba12f6Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURAutonomous agentsComputational methodsNeural networksAgent-based modelCultural transmissionDirect instructionExtensionReplicationSocial learningTeachingArticleExerciseHumanImitationSocial learningSociologyAgent-based modelingCultural transmissionDirect instructionExtensionReplicationSocial learningA model of cultural transmission by direct instruction: An exercise on replication and extensionarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Anzola Pinzón, David EnriqueRodríguez-Cárdenas D.10336/23703oai:repository.urosario.edu.co:10336/237032022-05-02 07:37:13.064659https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co
dc.title.spa.fl_str_mv A model of cultural transmission by direct instruction: An exercise on replication and extension
title A model of cultural transmission by direct instruction: An exercise on replication and extension
spellingShingle A model of cultural transmission by direct instruction: An exercise on replication and extension
Autonomous agents
Computational methods
Neural networks
Agent-based model
Cultural transmission
Direct instruction
Extension
Replication
Social learning
Teaching
Article
Exercise
Human
Imitation
Social learning
Sociology
Agent-based modeling
Cultural transmission
Direct instruction
Extension
Replication
Social learning
title_short A model of cultural transmission by direct instruction: An exercise on replication and extension
title_full A model of cultural transmission by direct instruction: An exercise on replication and extension
title_fullStr A model of cultural transmission by direct instruction: An exercise on replication and extension
title_full_unstemmed A model of cultural transmission by direct instruction: An exercise on replication and extension
title_sort A model of cultural transmission by direct instruction: An exercise on replication and extension
dc.subject.keyword.spa.fl_str_mv Autonomous agents
Computational methods
Neural networks
Agent-based model
Cultural transmission
Direct instruction
Extension
Replication
Social learning
Teaching
Article
Exercise
Human
Imitation
Social learning
Sociology
Agent-based modeling
Cultural transmission
Direct instruction
Extension
Replication
Social learning
topic Autonomous agents
Computational methods
Neural networks
Agent-based model
Cultural transmission
Direct instruction
Extension
Replication
Social learning
Teaching
Article
Exercise
Human
Imitation
Social learning
Sociology
Agent-based modeling
Cultural transmission
Direct instruction
Extension
Replication
Social learning
description This article replicates and extends an agent-based model of cultural transmission (Acerbi and Parisi, 2006). The original model uses artificial neural networks to inquire about the role of noise and selective cultural reproduction in imitation learning dynamics, both for static and dynamic environments. The replication tests the robustness of the original results, whereas the extension focuses on implementing an alternative type of learning: Direct instruction. The results of the extension suggest this type of learning could negatively affect the emergence of adaptive behavioral traits at the population level. Because of its reliance on explicit one-way communication and its reduced chance to question the traits transmitted, direct instruction might increase the time taken to find effective behavioral variants, in comparison with imitation. Yet, if the limit that defines inadequate behavior is chosen loosely enough, a sufficient amount of behavioral variations could be introduced in the behavioral pool so to ensure the development of highly adaptive variations. The text uses the implementation of direct instruction to discuss the role of extension in scientific endeavor, especially in interdisciplinary areas of research, such as the science of cultural evolution or agent-based computational social science. © 2018 Elsevier B.V.
publishDate 2018
dc.date.created.spa.fl_str_mv 2018
dc.date.accessioned.none.fl_str_mv 2020-05-26T00:04:39Z
dc.date.available.none.fl_str_mv 2020-05-26T00:04:39Z
dc.type.eng.fl_str_mv article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.spa.spa.fl_str_mv Artículo
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.cogsys.2018.07.019
dc.identifier.issn.none.fl_str_mv 13890417
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/23703
url https://doi.org/10.1016/j.cogsys.2018.07.019
https://repository.urosario.edu.co/handle/10336/23703
identifier_str_mv 13890417
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationEndPage.none.fl_str_mv 465
dc.relation.citationStartPage.none.fl_str_mv 450
dc.relation.citationTitle.none.fl_str_mv Cognitive Systems Research
dc.relation.citationVolume.none.fl_str_mv Vol. 52
dc.relation.ispartof.spa.fl_str_mv Cognitive Systems Research, ISSN:13890417, Vol.52,(2018); pp. 450-465
dc.relation.uri.spa.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050976626&doi=10.1016%2fj.cogsys.2018.07.019&partnerID=40&md5=4a314228a82b6819d6853fb4bfba12f6
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.acceso.spa.fl_str_mv Abierto (Texto Completo)
rights_invalid_str_mv Abierto (Texto Completo)
http://purl.org/coar/access_right/c_abf2
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Elsevier B.V.
institution Universidad del Rosario
dc.source.instname.spa.fl_str_mv instname:Universidad del Rosario
dc.source.reponame.spa.fl_str_mv reponame:Repositorio Institucional EdocUR
repository.name.fl_str_mv Repositorio institucional EdocUR
repository.mail.fl_str_mv edocur@urosario.edu.co
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