- Hello, who’s there?

- It’s the Bell curve. Just rang to say Reis didn’t invent me.

- Gauss, that’s interestin’ news! ]]>

As they build on the old models, they add on terms for more factors, thus making a model that more accurately accounts for the moving parts in the real world. The problem is, they can’t feed the model enough varied training data, and tuning the model to the invomplete, but available data seems to reduce the reliability of its predictions.

The models give very precise answers using very accurate modeling, but unfortunately the data isn’t precice enough, accuate enough or available to reliably predict the real world. You end up with models hypersensitive to outliers, and overspecialized to the data they’re trained on. When the math is redone to make it more generalized, we can then end up with too little predictive power, and models that don’t reconcile the outliers.

I’m reminded of when I first started using graphing calculators in high school. We learned how to do linear regressions on a set of points. Sometimes we were given sets with outliers which skewed the regression. I was unhappy with this, and discovered the calculator could also find quadratic regressions, cubic regressions, quartic, sine-based regressions etc. So I’d plug in the misbehaving set, and run the regressions till I found one that fit the data exactly. Unfortunately it would never predict new points that made sense knowing what the data was from. It was a case of wrong tool for the wrong job, and even though the models fit the data, they were still the wrong models to use.

]]>Sort of like the ladder of pure sciences, sociology is applied psychology, psychology is applied biology, biology is applied chemistry, and chemistry is applied physics. But that doesn’t mean that sociological phenomena can be accurately or reliably predicted using fundamental physical theory (yet)

]]>