Salīdzināt metodes

	Stohastiskā sistēmu dinamika	Stohastiskās diferenciālvienādojumi (SDV)
Nozare	Simulācija	Simulācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1980s–2000s	1944 (theory); 1992 (numerical framework)
Autors	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)
Tips	Continuous stochastic simulation	Continuous-time stochastic process model
Pirmavots	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.
Citi nosaukumi	SSD, stochastic stock-flow modelling, probabilistic system dynamics, random system dynamics	SDE, Itô equations, Stokastik Diferansiyel Denklemler (SDE)
Saistītās	5	4
Kopsavilkums	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.