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Breiman's classic paper casts data analysis as a choice between two cultures:data modelers and algorithmic modelers. Stated broadly, data modelers usesimple, interpretable models with well-understood theoretical properties toanalyze data. Algorithmic modelers prioritize predictive accuracy and use moreflexible function approximations to analyze data. This dichotomy overlooks athird set of models $-$ mechanistic models derived from scientific theories(e.g., ODE/SDE simulators). Mechanistic models encode application-specificscientific knowledge about the data. And while these categories representextreme points in model space, modern computational and algorithmic toolsenable us to interpolate between these points, producing flexible,interpretable, and scientifically-informed hybrids that can enjoy accurate androbust predictions, and resolve issues with data analysis that Breimandescribes, such as the Rashomon effect and Occam's dilemma. Challenges stillremain in finding an appropriate point in model space, with many choices on howto compose model components and the degree to which each component informsinferences.

Breiman's two cultures: You don't have to choose sides