Breiman's two cultures: You don't have to choose sides
This work offers a framework for researchers and practitioners to integrate scientific theory into data analysis, potentially resolving issues like the Rashomon effect, though it is incremental in extending existing paradigms.
The paper addresses the limitation of Breiman's two-culture dichotomy in data analysis by introducing mechanistic models as a third category, proposing hybrid approaches that combine data modeling, algorithmic modeling, and scientific knowledge to achieve flexible, interpretable, and accurate predictions.
Breiman's classic paper casts data analysis as a choice between two cultures: data modelers and algorithmic modelers. Stated broadly, data modelers use simple, interpretable models with well-understood theoretical properties to analyze data. Algorithmic modelers prioritize predictive accuracy and use more flexible function approximations to analyze data. This dichotomy overlooks a third set of models $-$ mechanistic models derived from scientific theories (e.g., ODE/SDE simulators). Mechanistic models encode application-specific scientific knowledge about the data. And while these categories represent extreme points in model space, modern computational and algorithmic tools enable us to interpolate between these points, producing flexible, interpretable, and scientifically-informed hybrids that can enjoy accurate and robust predictions, and resolve issues with data analysis that Breiman describes, such as the Rashomon effect and Occam's dilemma. Challenges still remain in finding an appropriate point in model space, with many choices on how to compose model components and the degree to which each component informs inferences.