Generation of Synthetic Populations in Social Simulations: A Review of Methods and Practices

Kevin Chapuis (), Patrick Taillandier () and Alexis Drogoul ()
Additional contact information
Kevin Chapuis: https://scholar.google.com/citations?user=_fbPeNwAAAAJ&hl=fr
Patrick Taillandier: http://www.researchgate.net/profile/Patrick_Taillandier
Alexis Drogoul: https://ideas4development.org/en/auteur/alexis-drogoul/

Journal of Artificial Societies and Social Simulation, 2022, vol. 25, issue 2, 6

Abstract: 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.

Keywords: Synthetic Population; Agent-Based Simulation; Model Initialisation; Data-Driven Social Simulation (search for similar items in EconPapers)
Date: 2022-03-31
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2021-100-2

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