Some students begin their postsecondary education without a clear sense of purpose; often scrambling to determine who they are, and what role they will play in the world. Others arrive with a vision — an internally motivated drive to solve problems and better the world around them. John Lamb and Adam Kurz, graduate students at MIT, fit firmly in the latter category.
In August of 2020, Lamb and Kurz wrote the first lines of code for their bold new project — an algorithm that seeks to beat the market using what MIT’s predictive processing lab refers to as “Dual-Process Bayesian Interpretive Feedback Modeling”, or DBIF modeling for short. By February 2021, Lamb and Kurz’s algorithm was performing above the market by 6%, and has only shown signs of improving since then.
If you clicked this article looking to use the algorithm, you’ve hit an unfortunate dead end — but despair not! The algorithm serves a noble cause. To hear Kurz explain the project’s goals: “The algorithm will address a long ignored problem, and by the end of 2022, we will have seen an almost 30% percent increase in the performance of stock portfolios of MIT graduate students; a historically disenfranchised group.”
So how does the algorithm work? What is DBIF modeling? We spoke to the Director of MIT’s predictive processing lab, who told us DBIF is “None of your goddamn business, kid” before hanging up and blocking our number. It seems MIT truly does exist 10 years in the future — and will take great steps to keep it that way.
More on MIT’s latest projects, such as [REDACTED] and [REDACTED], can be found here on Hard Money.