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| Simulation-gestützte Querschnittsforschung× | Agentenbasiertes Modellieren (ABM)× | |
|---|---|---|
| Fachgebiet≠ | Forschungsdesign | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2000s–2010s (consolidated as a named hybrid approach) | 1970s–1990s (formalized as a field) |
| Urheber≠ | 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) |
| Typ≠ | Quantitative hybrid research design | Computational simulation method |
| Wegweisende Quelle≠ | Pearce, N. (2012). Classification of epidemiological study designs. International Journal of Epidemiology, 41(2), 393–397. DOI ↗ | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. DOI ↗ |
| Aliasnamen | 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 |
| Verwandt≠ | 3 | 5 |
| Zusammenfassung≠ | 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. |
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