Сравнение методов

	Программирование целей в сценарном анализе политики	Анализ политических сценариев
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1961 (goal programming); policy scenario application 1980s–present	1967–1990s
Автор метода	Charnes, A., Cooper, W. W. (goal programming); policy scenario integration developed in OR/policy literature	Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD
Тип	Optimization under multiple conflicting goals across policy scenarios	Qualitative-quantitative hybrid scenario method
Основополагающий источник	Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471153405	Swart, R., Raskin, P., Robinson, J. (2004). The problem of the future: sustainability science and scenario analysis. Global Environmental Change, 14(2), 137–146.
Другие названия	PSGP, Policy GP, Scenario-based Goal Programming, Multi-scenario Goal Programming	PSA, Policy Scenarios, Policy Impact Scenario Analysis, Counterfactual Policy Analysis
Связанные	5	5
Сводка	Policy Scenario Goal Programming (PSGP) integrates goal programming optimization with policy scenario analysis to evaluate how well competing policy objectives can be achieved under distinct future conditions. Decision-makers define multiple goals and several plausible policy scenarios, then solve a goal programming model for each scenario to identify which policy strategies best satisfy priority targets across the full scenario space.	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.