1. Repositorio institucional de CLACSO
2. Colección de los Centros Asociados
3. Francia
4. Institut de Recherche pour le Développement - IRD - Cosecha

Título :	Generation of synthetic populations in social simulations : a review of methods and practices
Palabras clave :	Synthetic Population;Agent-Based Simulation;Model Initialization;Data-Driven Social Simulation
Descripción :	To 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.
URI :	https://biblioteca-repositorio.clacso.edu.ar/handle/CLACSO/169932
Otros identificadores :	https://www.documentation.ird.fr/hor/fdi:010084768 oai:ird.fr:fdi:010084768 Chapuis 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.]
Aparece en las colecciones:	Institut de Recherche pour le Développement - IRD - Cosecha

Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.