# Functional Requirement FR-012: Monte Carlo simulation engine

## Description

The system shall provide a Monte Carlo simulation engine to model uncertainties in mining project parameters. This engine will allow users to define probability distributions for key input variables and run simulations to assess the impact of these uncertainties on project outcomes.

A detailed specification is available in [specifications/monte_carlo_simulation.md](specifications/monte_carlo_simulation.md)

## Rationale

Monte Carlo simulations are a powerful tool for risk analysis and decision-making in mining projects. By modeling the variability of input parameters, users can better understand potential outcomes, identify risks, and make informed decisions based on probabilistic results.

## Acceptance Criteria

1. **Input Parameter Definitions**: The system shall allow users to define probability distributions for key input parameters, including but not limited to:

   - Ore grade
   - Recovery rate
   - Operating costs
   - Metal prices

2. **Simulation Configuration**: The system shall provide options for users to configure the simulation, including:

   - Number of simulation iterations
   - Random seed for reproducibility
   - Output metrics to be recorded

3. **Result Analysis**: The system shall offer tools for analyzing simulation results, including:

   - Summary statistics (mean, median, percentiles)
   - Visualization of result distributions (histograms, box plots)
   - Comparison of different scenarios or input configurations

4. **Integration with Existing Workflows**: The Monte Carlo simulation engine shall be integrated with existing scenario management and reporting tools within the system, allowing users to easily incorporate simulation results into their decision-making processes.

5. **Documentation**: The system shall include comprehensive documentation on how to use the Monte Carlo simulation engine, including examples and best practices.

## Dependencies

- Access to historical data for key input parameters to inform probability distribution definitions.
- Computational resources to support the execution of large-scale simulations.
- Integration with data management systems to facilitate the use of external data sources.
- User interface components for defining input parameters, configuring simulations, and visualizing results.

## Notes

- The Monte Carlo simulation engine should be designed for scalability to accommodate large datasets and complex models.
- Users should be able to easily update probability distributions and other simulation parameters as new information becomes available.
- Collaboration features may be beneficial to allow multiple users to work on simulation scenarios simultaneously.
- Future enhancements may include sensitivity analysis and scenario optimization based on simulation results.