Comments on "Two Cultures": What have changed over 20 years?

TL;DR

This paper reviews the evolution of the divide between stochastic and algorithmic data models over 20 years, highlighting positive changes and an optimistic outlook for the data science discipline.

Contribution

It provides a reflective analysis of the progress and shifts in modeling paradigms since Breiman's 2001 call for integration of data analysis approaches.

Findings

Increased adoption of algorithmic models in data analysis.

Greater recognition of empirical science in data modeling.

Positive cultural shifts towards hybrid modeling approaches.

Abstract

Twenty years ago Breiman (2001) called to our attention a significant cultural division in modeling and data analysis between the stochastic data models and the algorithmic models. Out of his deep concern that the statistical community was so deeply and "almost exclusively" committed to the former, Breiman warned that we were losing our abilities to solve many real-world problems. Breiman was not the first, and certainly not the only statistician, to sound the alarm; we may refer to none other than John Tukey who wrote almost 60 years ago "data analysis is intrinsically an empirical science." However, the bluntness and timeliness of Breiman's article made it uniquely influential. It prepared us for the data science era and encouraged a new generation of statisticians to embrace a more broadly defined discipline. Some might argue that "The cultural division between these two statistical learning frameworks has been growing at a steady pace in recent years", to quote Mukhopadhyay and Wang (2020). In this commentary, we focus on some of the positive changes over the past 20 years and offer an optimistic outlook for our profession.