方法对比
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| 多目标情景分析× | 随机情景分析× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2013 (integrated framework); scenario analysis roots: 1967 | 1955–1980s |
| 提出者≠ | Stewart, French & Rios (integration formalized); scenario analysis roots: Kahn & Wiener (1967) | Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition |
| 类型≠ | Structured qualitative-quantitative hybrid | Probabilistic scenario enumeration and evaluation |
| 开创性文献≠ | Stewart, T. J., French, S., & Rios, J. (2013). Integrating multicriteria decision analysis and scenario planning: Review and extension. Omega, 41(4), 679-688. DOI ↗ | Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374 |
| 别名 | MOSA, Multi-criteria scenario analysis, Multi-objective futures analysis, MO-scenario analysis | Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis |
| 相关 | 4 | 4 |
| 摘要≠ | Multi-objective Scenario Analysis (MOSA) is a structured method that constructs a set of plausible future scenarios and evaluates each scenario against multiple competing objectives or criteria. By making trade-offs explicit across objectives and across possible futures, it supports strategic decisions where uncertainty about the future and conflicts between goals co-exist. It is widely applied in energy planning, climate adaptation, public policy, and corporate strategy. | 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. |
| ScholarGate数据集 ↗ |
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