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| Robust Scenario Analysis× | Monte-Carlo-Simulation× | |
|---|---|---|
| Fachgebiet≠ | Simulation | Entscheidungsfindung |
| Familie≠ | Process / pipeline | MCDM |
| Entstehungsjahr≠ | 1950 (foundations); 2003 (modern RDM formulation) | 1949 |
| Urheber≠ | Wald, A. (minimax foundation); Lempert et al. (RDM framework) | Metropolis, N., Ulam, S. |
| Typ≠ | Scenario-based robustness evaluation | Robustness wrapper — Monte Carlo uncertainty propagation |
| Wegweisende Quelle≠ | Wald, A. (1950). Statistical Decision Functions. Wiley, New York. link ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Aliasnamen≠ | RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis | — |
| Verwandt≠ | 5 | 0 |
| Zusammenfassung≠ | Robust Scenario Analysis evaluates a set of candidate strategies across a structured collection of plausible future scenarios and selects the strategy that performs acceptably well — or best in the worst case — regardless of which scenario materializes. It merges scenario planning with robustness criteria such as maximin, minimax regret, or satisficing to support decisions under deep, irreducible uncertainty. | 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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