MCDMRankingcrisp
Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model
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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When to use it
Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model
Strengths & limitations
Strengths
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
Limitations
- Assumes full compensation — a strong score on one criterion can offset a weak score on another.
Sources
- Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI: 10.1080/01621459.1949.10483310 ↗
How to cite this page
ScholarGate. (2026, June 2). Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model. ScholarGate. https://scholargate.app/en/decision-making/monte-carlo-simulation
Referenced by
Agent-based Discrete-Event SimulationAgent-Based ModelingAgent-based queueing simulationAgent-based scenario analysisAgent-based sensitivity analysisApproximate Bayesian ComputationBayesian Agent-Based ModelingBayesian Cellular AutomataBayesian Discrete-Event SimulationBayesian Markov ModelBayesian MicrosimulationBayesian Monte Carlo SimulationBayesian Queueing SimulationBayesian Scenario AnalysisBayesian Sensitivity AnalysisBayesian System DynamicsBootstrap SimulationCellular AutomataDeterministic Cellular AutomataDeterministic Markov ModelDeterministic MicrosimulationDeterministic Scenario AnalysisDeterministic Sensitivity AnalysisDigital Twin SimulationDiscrete Choice SimulationDiscrete-Event SimulationDiscrete-Event System SimulationGlobal Sensitivity AnalysisHybrid Reliability AnalysisImportance SamplingJackknife EstimationLatin Hypercube SamplingMarkov Chain Monte CarloMarkov ModelMicrosimulationMulti-objective discrete-event simulationMulti-objective microsimulationMulti-objective sensitivity analysisMultilevel Monte Carlo SimulationPolicy Scenario Agent-Based ModelingPolicy Scenario AnalysisPolicy Scenario Discrete-Event SimulationPolicy Scenario MicrosimulationPolicy Scenario Monte Carlo SimulationPolicy Scenario Sensitivity AnalysisProbabilistic Seismic Hazard AnalysisQueueing SimulationRisk-based Taguchi methodRobust Agent-Based ModelingRobust Discrete-Event SimulationRobust Markov ModelRobust MicrosimulationRobust Monte Carlo SimulationRobust Queueing SimulationRobust Scenario AnalysisRobust Sensitivity AnalysisScenario AnalysisSensitivity analysis with fault tree analysisSensitivity Analysis with Process Capability AnalysisSensitivity analysis with root cause analysisSimulation-assisted causal-comparative researchSimulation-assisted confirmatory researchSimulation-assisted control chartSimulation-assisted cross-sectional researchSimulation-assisted ex post facto designSimulation-assisted failure mode and effects analysisSimulation-assisted fault tree analysisSimulation-assisted hypothesis testing researchSimulation-assisted process capability analysisSimulation-assisted quantitative content analysisSimulation-assisted reliability analysisSimulation-assisted statistical process controlSimulation-Assisted Trend ResearchStochastic Cellular AutomataStochastic Differential EquationsStochastic Discrete-Event SimulationStochastic Dynamic ProgrammingStochastic Linear ProgrammingStochastic Markov ModelStochastic MicrosimulationStochastic Mixed-Integer ProgrammingStochastic Multi-Objective OptimizationStochastic Queueing SimulationStochastic Scenario AnalysisStochastic Sensitivity AnalysisStochastic System DynamicsSystem DynamicsUncertainty QuantificationValue at RiskVariance Reduction for Monte Carlo