Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Politikas scenāriju analīze× | Monte Carlo simulācija× | |
|---|---|---|
| Nozare≠ | Simulācija | Lēmumu pieņemšana |
| Saime≠ | Process / pipeline | MCDM |
| Izcelsmes gads≠ | 1967–1990s | 1949 |
| Autors≠ | Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD | Metropolis, N., Ulam, S. |
| Tips≠ | Qualitative-quantitative hybrid scenario method | Robustness wrapper — Monte Carlo uncertainty propagation |
| Pirmavots≠ | Swart, R., Raskin, P., Robinson, J. (2004). The problem of the future: sustainability science and scenario analysis. Global Environmental Change, 14(2), 137–146. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Citi nosaukumi≠ | PSA, Policy Scenarios, Policy Impact Scenario Analysis, Counterfactual Policy Analysis | — |
| Saistītās≠ | 5 | 0 |
| Kopsavilkums≠ | 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. | 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. |
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