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| 결정론적 민감도 분석× | 확률적 민감도 분석× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1950s–1970s (formalized) | 1990s–2000s |
| 창시자≠ | Saltelli, A. et al.; widely formalized across operations research and health economics | Saltelli, A. et al.; Claxton, K. et al. (health economics stream) |
| 유형≠ | Parameter variation / robustness testing | Probabilistic uncertainty quantification technique |
| 원전≠ | Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley & Sons, Chichester. ISBN: 9780470870938 | 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 | DSA, One-Way Sensitivity Analysis, Tornado Diagram Analysis, Parametric Sensitivity Analysis | PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis |
| 관련≠ | 2 | 5 |
| 요약≠ | Deterministic Sensitivity Analysis (DSA) tests how model outputs change when individual or combined input parameters are varied across plausible ranges, one at a time or in structured combinations, without invoking probabilistic sampling. It is the standard approach in economic modeling, decision trees, and mathematical programming to identify which parameters drive conclusions and to demonstrate model robustness to regulators, reviewers, and stakeholders. | Stochastic Sensitivity Analysis (PSA) extends classical one-at-a-time sensitivity testing by representing uncertain model inputs as probability distributions and propagating them through the model via Monte Carlo sampling. The result is a full distribution of possible outputs, together with rankings of which inputs drive output variance the most — enabling robust, evidence-grounded conclusions under uncertainty. |
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