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| Programmazione Dinamica per Scenari di Policy× | Analisi di Scenari Politici× | |
|---|---|---|
| Campo | Simulazione | Simulazione |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1957 | 1967–1990s |
| Ideatore≠ | Bellman, Richard E. | Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD |
| Tipo≠ | Sequential optimization with scenario branching | Qualitative-quantitative hybrid scenario method |
| Fonte seminale≠ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 | 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 ↗ |
| Alias | PSDP, Policy-Scenario DP, Scenario-Based Dynamic Programming, Policy DP | PSA, Policy Scenarios, Policy Impact Scenario Analysis, Counterfactual Policy Analysis |
| Correlati | 5 | 5 |
| Sintesi≠ | Policy Scenario Dynamic Programming (PSDP) applies Bellman's recursive optimization framework to a set of pre-specified policy scenarios, enabling decision-makers to compare staged, sequential decisions under distinct future conditions. It decomposes a complex, multi-period policy choice into tractable sub-problems solved backward through time, yielding optimal action sequences for each scenario and a structured basis for scenario 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. |
| ScholarGateInsieme di dati ↗ |
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