Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Имитационное моделирование дискретно-событийных сценариев политики× | Метод Монте-Карло× | |
|---|---|---|
| Область≠ | Имитационное моделирование | Принятие решений |
| Семейство≠ | Process / pipeline | MCDM |
| Год появления≠ | 1960s–1990s | 1949 |
| Автор метода≠ | Tocher, K. D. and Gordon, G. (early DES); policy scenario extension emerged through operations research and health policy modeling communities | Metropolis, N., Ulam, S. |
| Тип≠ | Simulation-based policy evaluation | Robustness wrapper — Monte Carlo uncertainty propagation |
| Основополагающий источник≠ | Law, A. M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill Education. ISBN: 9780073401324 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Другие названия≠ | Policy DES, Scenario-based DES, Policy simulation DES, DES policy analysis | — |
| Связанные≠ | 5 | 0 |
| Сводка≠ | Policy Scenario Discrete-Event Simulation combines the event-by-event fidelity of Discrete-Event Simulation with systematic policy scenario analysis to evaluate how different interventions, regulations, or resource allocations change system performance. By running multiple well-defined policy scenarios through the same DES model, analysts can compare outcomes — throughput, waiting times, costs — across alternatives before real-world implementation. | 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 ↗ |
|
|