Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Simulación Monte Carlo de Escenarios de Política× | Simulación de Monte Carlo× | |
|---|---|---|
| Campo≠ | Simulación | Toma de decisiones |
| Familia≠ | Process / pipeline | MCDM |
| Año de origen≠ | 1990s–2000s | 1949 |
| Autor original≠ | Developed within health economics and policy modeling communities; foundational work by Briggs, Claxton, and Sculpher | Metropolis, N., Ulam, S. |
| Tipo≠ | Probabilistic scenario simulation | Robustness wrapper — Monte Carlo uncertainty propagation |
| Fuente seminal≠ | Briggs, A. H., Claxton, K., & Sculpher, M. J. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press. ISBN: 9780198526629 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Alias≠ | PS-MCS, Policy MC Simulation, Scenario-Based Monte Carlo, Policy Uncertainty Simulation | — |
| Relacionados≠ | 4 | 0 |
| Resumen≠ | 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. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
| ScholarGateConjunto de datos ↗ |
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