Goodhart's law applied to data science I recently learned about Goodhart's Law. Simply put,


  When a measure becomes a target, it ceases to be a good measure.


However, in Data Science, we really do aim at improving our performance by increasing or decreasing a metric, and improve our models based on that. For instance, in Kaggle competitions. Is Goodhart's Law applicable to Data Science? Why or why not?
The function problem which Goodhart's Law described is the changes of the underlying model from which the data is generated.  

One way to look at this is that the data generating process in the past is bias. Say we our try model is a product of two variables $z = xy$. In the past, $y$ has always been constant at $a$, so you come to the conclusion $z$ must equal to $ax$. But this is only true when $y=a$. 

In Goodhart's setting (economic policy and social science in general), it is usually the prediction value of $z$ that causing the changes in $y$. It is a dynamic system of which we only have a bias static slice of data.