Comparer des méthodes

	Simulation Monte Carlo par Scénarios de Politique	Analyse de sensibilité
Domaine	Simulation	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	1990s–2000s	2004
Auteur d'origine	Developed within health economics and policy modeling communities; foundational work by Briggs, Claxton, and Sculpher	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
Type	Probabilistic scenario simulation	Robustness wrapper — parameter / weight perturbation sensitivity indices
Source fondatrice	Briggs, A. H., Claxton, K., & Sculpher, M. J. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press. ISBN: 9780198526629	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester
Alias	PS-MCS, Policy MC Simulation, Scenario-Based Monte Carlo, Policy Uncertainty Simulation	—
Apparentées	4	0
Résumé	Policy Scenario Monte Carlo Simulation combines pre-defined discrete policy scenarios with probabilistic Monte Carlo sampling to quantify uncertainty in outcomes across each scenario. Rather than evaluating a single stochastic model, analysts define two or more policy alternatives and run thousands of Monte Carlo iterations within each, producing probability distributions of outcomes that support evidence-based policy comparison.	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.