Сравнение методов

	Оптимизация роя частиц для сценарного анализа политики	Анализ политических сценариев
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1995 (PSO); applied to policy scenarios from 2000s onward	1967–1990s
Автор метода	Kennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literature	Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD
Тип	Metaheuristic optimization within policy scenario framework	Qualitative-quantitative hybrid scenario method
Основополагающий источник	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.	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	PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization	PSA, Policy Scenarios, Policy Impact Scenario Analysis, Counterfactual Policy Analysis
Связанные	6	5
Сводка	Policy Scenario Particle Swarm Optimization integrates Particle Swarm Optimization (PSO) with explicit policy scenario analysis. A swarm of candidate policy solutions is evaluated under multiple defined future scenarios, and PSO's velocity-position update rules guide the swarm toward solutions that perform well—or robustly—across all considered scenarios. It is used in energy, environmental, infrastructure, and public resource planning.	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.