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monte-carlo-engine Monte Carlo simulation engine skill for probabilistic modeling, risk quantification, and uncertainty propagation
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name monte-carlo-engine description Monte Carlo simulation engine skill for probabilistic modeling, risk quantification, and uncertainty propagation allowed-tools ["Read","Write","Glob","Grep","Bash"] metadata {"specialization":"decision-intelligence","domain":"business","category":"simulation","priority":"high","shared-candidate":true,"tools-libraries":["numpy","scipy.stats","pymc","chaospy","SALib"]} graph {"domains":["domain:business-intelligence"],"skillAreas":["skill-area:quantitative-modeling","skill-area:statistical-analysis","skill-area:financial-risk-modeling"],"roles":["role:data-scientist","role:quantitative-analyst","role:risk-analyst"]}
Monte Carlo Engine
Overview
The Monte Carlo Engine skill provides comprehensive probabilistic simulation capabilities for quantifying uncertainty, assessing risk, and propagating variability through complex models. It supports multiple sampling strategies, correlation handling, and statistical analysis of simulation outputs for data-driven decision support.
Capabilities
Random variate generation (normal, triangular, PERT, uniform, lognormal, beta, etc.)
Latin Hypercube Sampling (LHS)
Correlation structure handling (Cholesky decomposition, copulas)
Convergence monitoring and adaptive iteration
Statistical output analysis (mean, variance, percentiles)
Tornado diagram generation
Value at Risk (VaR) and CVaR calculation
Parallel simulation execution
Used By Processes
Monte Carlo Simulation for Decision Support
Strategic Scenario Development
What-If Analysis Framework
Predictive Analytics Implementation
Usage
Distribution Specification
input_variables = {
"revenue" : {
"distribution" : "triangular" ,
"parameters" : {"min" : 800000 , "mode" : 1000000 , "max" : 1500000 }
},
"cost" : {
"distribution" : "normal" ,
"parameters" : {"mean" : 600000 , "std" : 50000 }
},
"market_share" : {
"distribution" : "PERT" ,
"parameters" : {"min" : 0.05 , "mode" : 0.10 , "max" : 0.20 }
},
"unit_price" : {
"distribution" : "uniform" ,
"parameters" : {"min" : 45 , "max" : 55 }
}
}
Correlation Matrix
correlations = {
"variables" : ["revenue" , "cost" , "market_share" ],
"matrix" : [
[1.0 , 0.6 , 0.3 ],
[0.6 , 1.0 , 0.2 ],
[0.3 , 0.2 , 1.0 ]
]
}
Model Function
def profit_model (inputs ):
revenue = inputs["revenue" ]
cost = inputs["cost" ]
profit = revenue - cost
return {"profit" : profit, "margin" : profit / revenue}
Sampling Strategies
Simple Random Sampling : Standard Monte CarloLatin Hypercube Sampling : Better coverage with fewer samplesQuasi-Monte Carlo : Low-discrepancy sequences (Sobol, Halton)Importance Sampling : Focus on tail events
Convergence Monitoring
Running mean and standard deviation
Coefficient of variation convergence
Percentile stability
Adaptive stopping criteria
Input Schema {
"input_variables" : {
"variable_name" : {
"distribution" : "string" ,
"parameters" : "object"
}
} ,
"correlations" : {
"variables" : [ "string" ] ,
"matrix" : "2D array"
} ,
"model" : "function or expression" ,
"simulation_options" : {
"iterations" : "number" ,
"sampling_method" : "random|lhs|quasi_mc" ,
"random_seed" : "number" ,
"parallel" : "boolean" ,
"convergence_threshold" : "number"
} ,
"output_options" : {
"percentiles" : [ "number" ] ,
"risk_metrics" : [ "VaR" , "CVaR" ] ,
"confidence_level" : "number"
}
}
Output Schema {
"summary_statistics" : {
"output_variable" : {
"mean" : "number" ,
"std" : "number" ,
"median" : "number" ,
"min" : "number" ,
"max" : "number" ,
"percentiles" : "object"
}
} ,
"risk_metrics" : {
"VaR" : "number" ,
"CVaR" : "number" ,
"probability_of_loss" : "number"
} ,
"convergence_info" : {
"iterations_run" : "number" ,
"converged" : "boolean" ,
"stability_scores" : "object"
} ,
"raw_results" : "array (optional)" ,
"tornado_data" : "object" ,
"visualization_paths" : [ "string" ]
}
Best Practices
Use at least 10,000 iterations for stable percentile estimates
Apply Latin Hypercube Sampling for efficiency
Validate input distributions with subject matter experts
Include correlations for realistic results
Monitor convergence before accepting results
Perform sensitivity analysis to identify key drivers
Document all distribution assumptions
Integration Points
Receives distributions from Risk Distribution Fitter
Feeds into Sensitivity Analyzer for importance analysis
Supports Value at Risk Calculator for risk metrics
Connects with Decision Tree Builder for decision node valuation
Integrates with Real Options Analyzer for option valuation
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