方法对比
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| 策略情景多目标优化× | 政策情景分析× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s | 1967–1990s |
| 提出者≠ | Evolved from multi-objective optimization and policy scenario analysis communities | Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD |
| 类型≠ | Scenario-conditioned multi-objective search | Qualitative-quantitative hybrid scenario method |
| 开创性文献≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester. ISBN: 9780471873396 | Swart, R., Raskin, P., Robinson, J. (2004). The problem of the future: sustainability science and scenario analysis. Global Environmental Change, 14(2), 137–146. DOI ↗ |
| 别名 | PS-MOO, Policy-Driven MOO, Scenario-Based Multi-Objective Optimization, Policy MOO | PSA, Policy Scenarios, Policy Impact Scenario Analysis, Counterfactual Policy Analysis |
| 相关≠ | 4 | 5 |
| 摘要≠ | Policy Scenario Multi-Objective Optimization (PS-MOO) integrates explicit policy scenario construction with multi-objective optimization to identify Pareto-optimal policy options across plausible future states. Decision-makers evaluate trade-offs between competing objectives — such as economic efficiency, equity, and environmental impact — for each distinct policy scenario, then compare Pareto fronts to select robust or scenario-contingent strategies. | Policy Scenario Analysis is a structured method for evaluating how different policy interventions perform across a range of plausible future states. By pairing specific policy levers with alternative scenarios, analysts can assess robustness, trade-offs, and unintended consequences of policy choices before implementation — making it a cornerstone of evidence-based policy design in fields from climate to public health. |
| ScholarGate数据集 ↗ |
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