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| 정책 시나리오 분석× | 민감도 분석× | |
|---|---|---|
| 분야≠ | 시뮬레이션 | 의사결정 |
| 계열≠ | Process / pipeline | MCDM |
| 기원 연도≠ | 1967–1990s | 2004 |
| 창시자≠ | Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD | Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. |
| 유형≠ | Qualitative-quantitative hybrid scenario method | Robustness wrapper — parameter / weight perturbation sensitivity indices |
| 원전≠ | 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 ↗ | Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester DOI ↗ |
| 별칭≠ | PSA, Policy Scenarios, Policy Impact Scenario Analysis, Counterfactual Policy Analysis | — |
| 관련≠ | 5 | 0 |
| 요약≠ | 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. | SENSITIVITY-ANALYSIS (Sensitivity Analysis — Systematic assessment of output variation w.r.t. input perturbations) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
| ScholarGate데이터셋 ↗ |
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