Audio note: this article contains 127 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description.

(Work done at Convergence Analysis. The ideas are due to Justin. Mateusz wrote the post. Thanks to Olga Babeeva for feedback on this post.)

In this post, we introduce the typology of structure, function, and randomness that builds on the framework introduced in the post Goodhart's Law Causal Diagrams. We aim to present a comprehensive categorization of the causes of Goodhart's problems.

But first, why do we care about this?

Goodhart's Law recap

The standard definition of Goodhart's Law is: "when a proxy for some value becomes the target of optimization pressure, the proxy will cease to be a good proxy.".