MCDMDecision-makingRankingMath steps
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.
Key highlights
- 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.
Intuition
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How it works
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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
- 1.Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
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ScholarGate. (2026, June 2). MONTE-CARLO-SIMULATION. ScholarGate. https://scholargate.app/decision-making/monte-carlo-simulation