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Por favor, use este identificador para citar o enlazar este ítem: https://biblioteca-repositorio.clacso.edu.ar/handle/CLACSO/169932
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dc.creator/Chapuis, Kevin-
dc.creator/Taillandier, Patrick-
dc.creator/Drogoul, Alexis-
dc.date2022-
dc.date.accessioned2022-10-14T18:38:30Z-
dc.date.available2022-10-14T18:38:30Z-
dc.identifierhttps://www.documentation.ird.fr/hor/fdi:010084768-
dc.identifieroai:ird.fr:fdi:010084768-
dc.identifierChapuis Kevin, Taillandier Patrick, Drogoul Alexis. Generation of synthetic populations in social simulations : a review of methods and practices. 2022, 25 (2), p. 6 [23 p.]-
dc.identifier.urihttps://biblioteca-repositorio.clacso.edu.ar/handle/CLACSO/169932-
dc.descriptionTo build realistic models of social systems, designers of agent-based models tend to incorporate a considerable amount of data, which influence the model outcomes. Data concerning the attributes of social agents, which compose synthetic populations, are particularly important but usually difficult to collect and therefore use in simulations. In this paper, we have reviewed state of the art methodologies and theories for building realistic synthetic populations for agent-based simulation models and practices in social simulations. We also highlight the discrepancies between theory and practice and outline the challenges in bridging this gap through a quantitative and narrative review of work published in JASSS between 2011 and 2021. Finally, we present several recommendations that could help modellers adopt best practices for synthetic population generation.-
dc.languageEN-
dc.subjectSynthetic Population-
dc.subjectAgent-Based Simulation-
dc.subjectModel Initialization-
dc.subjectData-Driven Social Simulation-
dc.titleGeneration of synthetic populations in social simulations : a review of methods and practices-
dc.typetext-
Aparece en las colecciones: Institut de Recherche pour le Développement - IRD - Cosecha

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