--- name: monte-carlo-engine-fbb39a612d79 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 ```python # Define input distributions 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 ```python # Define correlations between variables correlations = { "variables": ["revenue", "cost", "market_share"], "matrix": [ [1.0, 0.6, 0.3], # revenue correlations [0.6, 1.0, 0.2], # cost correlations [0.3, 0.2, 1.0] # market_share correlations ] } ``` ### Model Function ```python # Define the model to simulate def profit_model(inputs): revenue = inputs["revenue"] cost = inputs["cost"] profit = revenue - cost return {"profit": profit, "margin": profit / revenue} ``` ### Sampling Strategies 1. **Simple Random Sampling**: Standard Monte Carlo 2. **Latin Hypercube Sampling**: Better coverage with fewer samples 3. **Quasi-Monte Carlo**: Low-discrepancy sequences (Sobol, Halton) 4. **Importance Sampling**: Focus on tail events ### Convergence Monitoring The skill monitors: - Running mean and standard deviation - Coefficient of variation convergence - Percentile stability - Adaptive stopping criteria ## Input Schema ```json { "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 ```json { "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 1. Use at least 10,000 iterations for stable percentile estimates 2. Apply Latin Hypercube Sampling for efficiency 3. Validate input distributions with subject matter experts 4. Include correlations for realistic results 5. Monitor convergence before accepting results 6. Perform sensitivity analysis to identify key drivers 7. 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 --- Source attribution: a5c-ai Original project: https://github.com/a5c-ai/babysitter License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: a5c-ai Original project: https://github.com/a5c-ai/babysitter Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Finance & Accounting, Marketing, Product Management, Sales Roles: Business Development, Data Analyst, Financial Analyst, Marketing Analyst, Product Manager Specialties: API, Cross Platform, Testing Tools: Salib, Chaospy, Numpy, Pymc, Scipy.Stats Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: ee367d06a4d57d4b48d1e409e3852adbac5f89f00ac4c3695a69e4910e2a4fea