Background
Mathematical modeling competitions require more than calculation. A good solution needs clear assumptions, a model that matches the decision context, sensitivity analysis, and a readable final report.
Method
This practice project uses a mock competition prompt and turns it into a complete modeling workflow: define variables, choose simplifying assumptions, construct a tractable model, test parameter sensitivity, and communicate limitations.
Results
The strongest learning outcome was not a single formula. It was the habit of connecting each modeling choice to the original question and making uncertainty explicit.
Technical Stack
- Python for simulation and plotting
- Excel for quick tabular checks
- LaTeX for mathematical writing
- Matplotlib for figures
Next Steps
- Build reusable templates for model assumptions and sensitivity analysis
- Practice one full timed writeup
- Add peer review notes and revision history