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Stokastisk Systemdynamik×Monte Carlo-simulering×
FagområdeSimuleringBeslutningstagning
FamilieProcess / pipelineMCDM
Oprindelsesår1980s–2000s1949
OphavspersonJay W. Forrester (base SD); stochastic extensions developed through 1980s–2000s by multiple researchersMetropolis, N., Ulam, S.
TypeContinuous stochastic simulationRobustness wrapper — Monte Carlo uncertainty propagation
Oprindelig kildeSterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. ISBN: 978-0072389159Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
AliasserSSD, stochastic stock-flow modelling, probabilistic system dynamics, random system dynamics
Relaterede50
ResuméStochastic 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.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.
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ScholarGateSammenlign metoder: Stochastic System Dynamics · MONTE-CARLO-SIMULATION. Hentet 2026-06-17 fra https://scholargate.app/da/compare