Methoden vergleichen

	Politikszenario-Monte-Carlo-Simulation	Policy Scenario Analysis
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1990s–2000s	1967–1990s
Urheber	Developed within health economics and policy modeling communities; foundational work by Briggs, Claxton, and Sculpher	Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD
Typ	Probabilistic scenario simulation	Qualitative-quantitative hybrid scenario method
Wegweisende Quelle	Briggs, A. H., Claxton, K., & Sculpher, M. J. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press. ISBN: 9780198526629	Swart, R., Raskin, P., Robinson, J. (2004). The problem of the future: sustainability science and scenario analysis. Global Environmental Change, 14(2), 137–146.
Aliasnamen	PS-MCS, Policy MC Simulation, Scenario-Based Monte Carlo, Policy Uncertainty Simulation	PSA, Policy Scenarios, Policy Impact Scenario Analysis, Counterfactual Policy Analysis
Verwandt	4	5
Zusammenfassung	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.	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.