Comparer des méthodes

	Recherche transversale assistée par simulation	Modélisation Basée sur les Agents (MBA)
Domaine	Conception de la recherche	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s–2010s (consolidated as a named hybrid approach)	1970s–1990s (formalized as a field)
Auteur d'origine	Emerged from epidemiology and systems science (no single originator; synthesises Pearce-type cross-sectional designs with simulation modelling traditions from Sterman and colleagues)	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Type	Quantitative hybrid research design	Computational simulation method
Source fondatrice	Pearce, N. (2012). Classification of epidemiological study designs. International Journal of Epidemiology, 41(2), 393–397.	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Alias	simulation-enhanced cross-sectional study, hybrid simulation cross-sectional design, cross-sectional simulation study, SACSR	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
Apparentées	3	5
Résumé	Simulation-assisted cross-sectional research combines the one-time, population-wide snapshot of a classic cross-sectional survey with computational simulation — such as agent-based modelling or Monte Carlo methods — to extend what can be inferred from data collected at a single point in time. Empirical cross-sectional data calibrate the simulation, which then explores counterfactuals, rare subgroups, or dynamic processes that the survey alone cannot reveal.	Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions with each other and with their environment collectively produce global, system-level patterns that could not be predicted from any single agent's rules alone.