Including learning and forgetting processes in agent-based simulation models: application to police intervention in out-of-hospital cardiac arrests

dc.contributor.authorBaigorri Iguzquiaguirre, Miguel
dc.contributor.authorMallor Giménez, Fermín
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.contributor.departmentInstitute of Smart Cities - ISCen
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
dc.date.accessioned2024-11-20T12:09:40Z
dc.date.available2024-11-20T12:09:40Z
dc.date.issued2025-01-01
dc.date.updated2024-11-20T11:57:53Z
dc.description.abstractAgent-based modeling has become increasingly popular in recent decades; however, defining agents that accurately depict human behavior remains a significant challenge. This paper contributes to the precise definition of human-like agents by incorporating learning and forgetting processes from the medical and psychological literature into agent-based simulation models. Specifically, the mathematical model for forgetting is developed to be compatible with empirical findings. The empirical evidence also supports the decomposition of the learning process into training sessions and the application of skills in real situations, as followed in this model. The resulting model of learning agents is then applied to study police intervention in out-of-hospital cardiac arrests. In numerous urban areas, there's ongoing discussion regarding the provision of defibrillators in patrol cars and CPR training for police officers. The results demonstrate that including learning and forgetting processes in simulation models provide a more accurate understanding of the benefits of using local police to attend cardiac arrests.en
dc.description.sponsorshipThis work was supported by the Ministerio de Ciencia e Innovación, Spain (PID2020-114031RB-I00 (AEI, FEDER EU)) and Ayuda Formación de Profesorado Universitario (FPU22/00461). Open access funding provided by Universidad Pública de Navarra.
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBaigorri, M., Mallor, F. (2025). Including learning and forgetting processes in agent-based simulation models: application to police intervention in out-of-hospital cardiac arrests. Expert Systems with Applications, 260, 1-15. https://doi.org/10.1016/j.eswa.2024.125394.
dc.identifier.doi10.1016/j.eswa.2024.125394
dc.identifier.issn0957-4174
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/52548
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofExpert Systems with Applications (2025), vol. 260, 125394
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114031RB-I00/ES/
dc.relation.publisherversionhttps://doi.org/10.1016/j.eswa.2024.125394
dc.rights© 2024 The Author(s). This is an open access article under the CC BY-NC license.
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectAgent-based simulationen
dc.subjectbehavioural ORen
dc.subjectHealthcareen
dc.subjectHybrid simulation modelen
dc.subjectLearning and forgetting modelingen
dc.titleIncluding learning and forgetting processes in agent-based simulation models: application to police intervention in out-of-hospital cardiac arrestsen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
relation.isAuthorOfPublicationdbb57a09-595d-4219-899f-8167f1d48691
relation.isAuthorOfPublication7c01f74d-0369-4f57-b8c2-c6c579b76b38
relation.isAuthorOfPublication.latestForDiscoverydbb57a09-595d-4219-899f-8167f1d48691

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