方法对比
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| 随机情景分析× | 蒙特卡洛模拟× | |
|---|---|---|
| 领域≠ | 仿真 | 决策 |
| 方法族≠ | Process / pipeline | MCDM |
| 起源年份≠ | 1955–1980s | 1949 |
| 提出者≠ | Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition | Metropolis, N., Ulam, S. |
| 类型≠ | Probabilistic scenario enumeration and evaluation | Robustness wrapper — Monte Carlo uncertainty propagation |
| 开创性文献≠ | Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association 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. | 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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