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| 随机系统动力学× | 随机微分方程 (SDEs)× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1980s–2000s | 1944 (theory); 1992 (numerical framework) |
| 提出者≠ | Jay W. Forrester (base SD); stochastic extensions developed through 1980s–2000s by multiple researchers | Kiyosi Itô (Itô calculus, 1944); Peter Kloeden & Eckhard Platen (numerical methods, 1992) |
| 类型≠ | Continuous stochastic simulation | Continuous-time stochastic process model |
| 开创性文献≠ | Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. ISBN: 978-0072389159 | Øksendal, B. (2003). Stochastic Differential Equations: An Introduction with Applications (6th ed.). Springer. DOI ↗ |
| 别名≠ | SSD, stochastic stock-flow modelling, probabilistic system dynamics, random system dynamics | SDE, Itô equations, Stokastik Diferansiyel Denklemler (SDE) |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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. | Stochastic differential equations (SDEs) are differential equation models that combine a deterministic drift term — governing the average tendency of a system — with a stochastic diffusion term driven by a Wiener process (Brownian motion). Pioneered through Itô calculus by Kiyosi Itô in 1944 and given a comprehensive numerical treatment by Kloeden and Platen in 1992, SDEs are the standard modelling language for continuous-time systems subject to random noise, including financial asset prices, population dynamics, and physical processes. |
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