Data modeling culture versus algorithmic modeling culture

A critical choice for any data scientist

Picture from Pexels

Leo Breiman wrote an interesting article about the two cultures in the use of statistical modeling to reach conclusions from data (Breimam, 2001): the data modeling culture and the algorithmic modeling culture. The differences between these two models have recently come to the fore again in a discussion between Noam Chomsky (Katz, Y. , 2017) and Google’s director of AI research, Peter Norvig (Norvig, 2022). To recap these two cultures we can say the following:

• The data modeling culture argues that nature can be understood as a black box with a very simple underlying model (that can be assumed) that translates from input variables to output variables.
• The algorithmic modeling culture’s approach is to identify a function able to predict the output from a given input. But the inside of the box is unknown from this culture’s point of view.

The difference between these two approaches us that the conclusions made by data modeling are about the model, not about the nature of phenomena.

Usually, simple parametric models (from data modeling culture) imposed on data generated by complex systems result in a loss of…