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...
- 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)
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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 |
_version_ |
1818106757842468864 |