方法对比
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| 贝叶斯情景分析× | 稳健情景分析× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2000s | 1950 (foundations); 2003 (modern RDM formulation) |
| 提出者≠ | Developed iteratively across Bayesian statistics and scenario planning communities; formalized in risk and decision analysis (Aven, Lempert et al., 2000s) | Wald, A. (minimax foundation); Lempert et al. (RDM framework) |
| 类型≠ | Probabilistic hybrid — Bayesian inference integrated with structured scenario analysis | Scenario-based robustness evaluation |
| 开创性文献≠ | Aven, T., & Reniers, G. (2013). How to define and interpret a probability in a risk and safety setting. Safety Science, 51(1), 223–231. DOI ↗ | Wald, A. (1950). Statistical Decision Functions. Wiley, New York. link ↗ |
| 别名 | BSA, Bayesian scenario planning, probabilistic scenario analysis, Bayesian-weighted scenario analysis | RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis |
| 相关 | 5 | 5 |
| 摘要≠ | Bayesian Scenario Analysis (BSA) combines structured scenario planning with Bayesian probability theory, assigning explicit prior probabilities to alternative futures and updating them as new evidence or expert judgments become available. The result is a probability-weighted distribution of outcomes across scenarios rather than a set of equally-weighted or arbitrarily-weighted futures. | 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. |
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