Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Агентное моделирование сценариев политики× | Метод Монте-Карло× | |
|---|---|---|
| Область≠ | Имитационное моделирование | Принятие решений |
| Семейство≠ | Process / pipeline | MCDM |
| Год появления≠ | 1990s–2000s | 1949 |
| Автор метода≠ | Axelrod, R. and colleagues in computational social science | Metropolis, N., Ulam, S. |
| Тип≠ | Simulation-based policy comparison | Robustness wrapper — Monte Carlo uncertainty propagation |
| Основополагающий источник≠ | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. ISBN: 9780691015675 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Другие названия≠ | Policy ABM, Policy Scenario ABM, Scenario-Based ABM, PS-ABM | — |
| Связанные≠ | 5 | 0 |
| 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. | 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. |
| ScholarGateНабор данных ↗ |
|
|