Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Стохастическое микромоделирование× | Стохастическая системная динамика× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1957 | 1980s–2000s |
| Автор метода≠ | Guy H. Orcutt | Jay W. Forrester (base SD); stochastic extensions developed through 1980s–2000s by multiple researchers |
| Тип≠ | Stochastic individual-level simulation | Continuous stochastic simulation |
| Основополагающий источник≠ | Orcutt, G. H. (1957). A new type of socio-economic system. The Review of Economics and Statistics, 39(2), 116–123. DOI ↗ | Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. ISBN: 978-0072389159 |
| Другие названия | Probabilistic Microsimulation, Monte Carlo Microsimulation, Stochastic Micro-simulation, SMSM | SSD, stochastic stock-flow modelling, probabilistic system dynamics, random system dynamics |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Stochastic Microsimulation tracks a large population of individual units — people, households, or firms — through time by applying random draws from empirically estimated probability distributions at each transition event. Unlike deterministic counterparts, every state change is decided by chance, preserving realistic heterogeneity and allowing rigorous uncertainty quantification across multiple simulation runs. | Stochastic System Dynamics (SSD) extends conventional system dynamics by replacing fixed parameter values and deterministic flow equations with probability distributions and random draws. Running many replications of the stock-flow model yields probabilistic trajectories — confidence bands rather than single lines — enabling rigorous uncertainty quantification and risk analysis in complex feedback systems such as epidemic models, supply chains, and energy policy scenarios. |
| ScholarGateНабор данных ↗ |
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