A simulation of the wealth of college graduates.

Salary vs. Time (87 Majors)
Wealth vs. Time (87 Majors)

Inspiration

We started by discussing simulating a person's financial future as part of a mobile app, but we decided that form didn't make sense. We fleshed out the mathematical model more, working on it for several hours, the entire time assuming it depended on some type of user input. Then, we looked back at the board and realized that no part of the model actually required or accommodated any user input anymore: the behavior of the model was completely determined by empirical data and intuitively correct relationships.

What it does

The model simulates the wealth of a person grouped by different college majors and educational paths (e.g. different starting salaries and growth trajectories).

How we built it

First, we modeled the growth of wealth with a first order differential equation to calculate the long term effects of compound interest combined with additional periodic investments. We then took data from well-known sources describing salary growth based on field of work. With all of this data, we combined the calculations and also took inflation into account to determine the most financially sound plan of attack for attending college and investments.

Challenges we ran into

We needed to find, reformat, and clean lots of empirical data about salaries, spending habits, quantile boundaries, etc.

Accomplishments that we're proud of

The curves are odd and beautiful, revealing complex dynamics that arise from the simple equations and constraints. With the great number of specializations we were able to analyze, the graphs are extremely intricate and overwhelmingly detailed.

What we learned

Tiny misunderstandings or careless assumptions in the development of a model like this can result in completely incorrect nonsensical answers. However, the fact that a model converges and behaves in a probable way is not proof that the math and reason that underlies it is correct.