What happens if, instead of doing a linear regression with sums of monomial terms, you do the complete opposite? Instead of regressing the **phenomenon** against , you regressed the phenomenon against an explanation like ?

I first thought of this question several years ago whilst living with my sister. She’s a complex person. **If I asked her how her day went, and wanted to predict her answer with an equation, I definitely couldn’t use linearly separable terms.** That would mean that, if one aspect of her day went well and the other aspect went poorly, the two would even out. Not the case for her. **One or two things could totally swing her day** all-the-way-to-good or all-the-way-to-bad.

The pattern of her moods and emotional affect has nothing to do with irrationality or moodiness. She’s just an intricate person with a complex utility function.

If you don’t know my sister, you can pick up the point from this well-known stereotype about the difference between men and women:

**"Men are simple, women are complex.”** Think about a stereotypical teenage girl describing what made her upset. **"It’s not any one thing, it’s ***everything*.”

I.e., nonseparable interaction terms.

I wonder if there’s a mapping that sensibly inverts strongly-interdependent polynomials with monomials — interchanging interdependent equations with separable ones. If so, that could invert our notions of a parsimonious model.

Who says that a model that’s short to write in one particular space or parameterisation is the best one? or the simplest? Some things are better understood when you consider everything at once.