Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Агентное моделирование сценариев политики× | Динамика системной политики× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1990s–2000s | 1960s–1990s |
| Автор метода≠ | Axelrod, R. and colleagues in computational social science | Forrester, J. W. (system dynamics); scenario integration formalized by Sterman and others |
| Тип≠ | Simulation-based policy comparison | Simulation-based policy analysis |
| Основополагающий источник≠ | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. ISBN: 9780691015675 | Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill. ISBN: 9780072389159 |
| Другие названия | Policy ABM, Policy Scenario ABM, Scenario-Based ABM, PS-ABM | PSSD, Policy SD Simulation, Scenario-Based System Dynamics, Policy Systems Modeling |
| Связанные | 5 | 5 |
| Сводка≠ | Policy Scenario Agent-Based Modeling (PS-ABM) is a simulation method that uses agent-based models to evaluate and compare multiple policy scenarios. Heterogeneous autonomous agents interact under different policy regimes, and emergent system-level outcomes are compared across scenarios to inform evidence-based policy decisions. It is widely used in public health, urban planning, economics, and social policy research. | Policy Scenario System Dynamics combines system dynamics modeling with structured scenario analysis to evaluate how different policy interventions affect complex, feedback-driven systems over time. By running multiple policy scenarios through a calibrated stock-and-flow model, analysts can compare long-run outcomes, identify leverage points, and anticipate unintended consequences before real-world implementation. |
| ScholarGateНабор данных ↗ |
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