Chapuis Kevin auteur aut IRD Taillandier Patrick auteur aut IRD Drogoul Alexis auteur aut IRD text journalArticle eng text/pdf reformatted digital access 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. specialized Synthetic Population Agent-Based Simulation Model Initialization Data-Driven Social Simulation 020 108 25 2 6 [23 ] 2022 1460-7425 https://www.documentation.ird.fr/hor/fdi:010084768 10.18564/jasss.4762 1460-7425 [F B010084768] https://www.documentation.ird.fr/hor/fdi:010084768 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2022-06/010084768.pdf IRD - Base Horizon / Pleins textes 2022-06-03 2025-02-24 fdi:010084768 fre