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| Simulation-gestützte Querschnittsforschung× | Monte-Carlo-Simulation× | |
|---|---|---|
| Fachgebiet≠ | Forschungsdesign | Entscheidungsfindung |
| Familie≠ | Process / pipeline | MCDM |
| Entstehungsjahr≠ | 2000s–2010s (consolidated as a named hybrid approach) | 1949 |
| Urheber≠ | Emerged from epidemiology and systems science (no single originator; synthesises Pearce-type cross-sectional designs with simulation modelling traditions from Sterman and colleagues) | Metropolis, N., Ulam, S. |
| Typ≠ | Quantitative hybrid research design | Robustness wrapper — Monte Carlo uncertainty propagation |
| Wegweisende Quelle≠ | Pearce, N. (2012). Classification of epidemiological study designs. International Journal of Epidemiology, 41(2), 393–397. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Aliasnamen≠ | simulation-enhanced cross-sectional study, hybrid simulation cross-sectional design, cross-sectional simulation study, SACSR | — |
| Verwandt≠ | 3 | 0 |
| 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. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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