by: Brandon Chang
From my three years of high school so far, I’d undoubtedly say that the Modeling the Future Challenge has taught me the most outside of the classroom. It’s amazing how a fully digital, remote competition has managed to reach so many students across the nation with a rigorous training and learning curriculum that allows even beginners, new to the field of math modeling and statistical forecasting, to get a good feel for and strong introduction to how everything works.
During my freshman year, I was completely new to the idea of data science. After about two months and a lot of exploration on the Modeling the Future Challenge website, however, I began to familiarize myself with the key terms, concepts, and ideas that make up data analysis and modeling. In fact, I was even teaching my friends outside of my team about math modeling! On that note, before I dive deeper into the MTF Challenge, it’s important to highlight that math modeling, in general, is the next frontier. It’s a key field that allows us to solve some of today’s most pressing issues. It gives us the unique ability to predict and, therefore, prevent some of the biggest risks, bringing a sense of certainty to the uncertainty of our constantly changing world. Math modeling applies to practically every field, from finance to actuarial science in insurance, and even to space exploration, like forecasting solar events such as geomagnetic storms.
That being said, MTF is the best introduction to the field of math modeling for high school students. Firstly, it provides an extremely strong support system that makes everyone feel included and comfortable throughout the entire learning process. From the very beginning, there are constant informational webinars, online resources available on the website, social media tips, and much more—all of which made me feel that my team was well-supported, especially during our first year. If your team makes it to the semifinalist stage, the support becomes even stronger. With an actual actuarial mentor to guide you and suggest improvements to your research paper, you quite literally have expert support. In fact, even teams that don’t advance from the semifinalist to finalist stage have used the extensive feedback from their mentors to make real change in the world!
On the math side of things, I learned everything from time series analysis to Monte Carlo simulations and agent-based modeling over my two years participating in MTF. Each project involves specific models or terminologies to master, so no matter how experienced or new you are, the learning process is constant. This mirrors the field of math modeling as an industry, where everyone is always discovering and adopting new, innovative methods.
Last but certainly not least, MTF does an amazing job fostering a sense of real-life connection. At the end of the day, it really isn’t just about numbers. These are more than data points on a scatterplot or numbers in Excel. The stories behind the data sets we use to model cannot be reduced to mere equations or emotionless predictions. Neither can the solutions to these issues. For every paper, MTF requires a “recommendations” section, where results and predictions are applied to real-life solutions for the issue being addressed. This brings a sense of responsibility and accountability to the modeling process. It’s not enough to simply get results—it’s equally important to have the ethical responsibility to share findings with the public and policymakers, creating real change instead of leaving them as static plots in Python.