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| 随机情景分析× | 敏感性分析× | |
|---|---|---|
| 领域≠ | 仿真 | 决策 |
| 方法族≠ | Process / pipeline | MCDM |
| 起源年份≠ | 1955–1980s | 2004 |
| 提出者≠ | Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition | Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. |
| 类型≠ | Probabilistic scenario enumeration and evaluation | Robustness wrapper — parameter / weight perturbation sensitivity indices |
| 开创性文献≠ | Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374 | Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester DOI ↗ |
| 别名≠ | Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis | — |
| 相关≠ | 4 | 0 |
| 摘要≠ | Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is. | SENSITIVITY-ANALYSIS (Sensitivity Analysis — Systematic assessment of output variation w.r.t. input perturbations) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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