方法对比
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| 策略情景遗传算法× | 政策情景分析× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1975 (GA); 2000s (policy scenario application) | 1967–1990s |
| 提出者≠ | Holland, J. H. (GA foundation); Lempert, Popper & Bankes (policy scenario search) | Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD |
| 类型≠ | Evolutionary metaheuristic for policy scenario exploration | Qualitative-quantitative hybrid scenario method |
| 开创性文献≠ | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI. ISBN: 9780262581110 | 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 | PSGA, Policy-GA, Policy Optimization Genetic Algorithm, Evolutionary Policy Scenario Search | PSA, Policy Scenarios, Policy Impact Scenario Analysis, Counterfactual Policy Analysis |
| 相关≠ | 4 | 5 |
| 摘要≠ | The Policy Scenario Genetic Algorithm applies evolutionary search to systematically explore large, combinatorial policy alternative spaces under multiple future scenarios. Rather than exhaustively enumerating options, it breeds successive generations of candidate policies, retaining those that perform well across scenario conditions, yielding robust, high-performing policy recommendations. | 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. |
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