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| 정책 시나리오 민감도 분석× | 강건 민감도 분석× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도 | 1990s–2000s | 1990s–2000s |
| 창시자≠ | Saltelli, A. et al.; Lempert, R. J. et al. | Saltelli, A. and colleagues |
| 유형≠ | Analytical framework combining scenario planning with sensitivity analysis | Simulation-based robustness assessment pipeline |
| 원전≠ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons, Chichester. ISBN: 9780470059975 | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 9780470059975 |
| 별칭 | PSSA, Policy Sensitivity Analysis, Scenario-Based Sensitivity Analysis, Policy Robustness Analysis | RSA, Robust SA, Sensitivity Analysis under Uncertainty, Uncertainty-robust sensitivity analysis |
| 관련≠ | 5 | 3 |
| 요약≠ | Policy Scenario Sensitivity Analysis (PSSA) combines structured scenario planning with formal sensitivity analysis to determine which model inputs and policy parameters most strongly drive outcomes across a set of distinct policy alternatives or future states. It is widely used in public health, climate, energy, and economic policy modeling to identify robust interventions that perform well even when key assumptions vary. | Robust Sensitivity Analysis (RSA) systematically evaluates how much variation in model outputs can be attributed to uncertainty or variation in model inputs, with an explicit focus on conclusions that remain valid across a wide range of plausible input conditions. It goes beyond standard sensitivity analysis by asking not only which inputs matter most, but which findings are truly robust — stable regardless of assumptions made under uncertainty. |
| ScholarGate데이터셋 ↗ |
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