[mnky9800n]

i think a partial explanation is that people don't move away from parametric representations of reality. We simply must be organized into a nice, neat gaussian distribution with very easy to calculate means and standard deviations. The idea that organization of data could be relational or better handled by a decision tree or whatever is not really presented to most people in school or university. Especially not as frequently or holistically as is simply thinking the average represents the middle of a distribution.

you see this across social sciences where you can see a lot of fields have papers that come out every decade or so since the 1980s saying that linear regression models are wrong because they don't take into account several concepts such as hierarchy (e.g., students go to different schools), frailty (there is likely unmeasured reasons why some people do the things they do), latent effects (there is likely non-linear processes that are more than the sum of the observations, e.g., traffic flows like a fluid and can have turbulence), auto-correlations/spatial correlations/etc.

In fact, I would argue that a decision tree based model (i.e., gradient boosted trees) will always arrive at a better solution to a human system than any linear regression. But at this point I suppose I have digressed from the original point.