# Categorizing Variants of Goodhart's Law

Source: https://arxiv.org/abs/1803.04585
Kiwi News: https://news.kiwistand.com/stories/Categorizing-Variants-of-Goodharts-Law?index=0x681aefdf10e049e4e5d71663ca6d7b10ae153b7cf6511ba32f3a91e0c3cf6f9772f9f1ca
Submitted by tim on 2025-05-07 UTC, 1 upvote, 0 comments

## Summary

Manheim and Garrabrant categorize several distinct failure modes of overoptimizing systems based on metrics, a class often called Goodhart's Law, and argue that the distinctions between them are often ignored in discussion. The paper expands on an earlier Garrabrant discussion that identified "(at least) four different mechanisms" relating to Goodhart's Law and aims to explore these mechanisms further and specify how they occur. The authors say the analysis applies to economic regulation, public policy, machine learning, and AI alignment. They also state that the importance of Goodhart effects depends on the optimization power directed at the proxy, which makes artificial intelligence especially critical for that field.

## Article

Categorizing Variants of Goodhart's Law

Authors: David Manheim, Scott Garrabrant

There are several distinct failure modes for overoptimization of systems on the basis of metrics. This occurs when a metric which can be used to improve a system is used to an extent that further optimization is ineffective or harmful, and is sometimes termed Goodhart's Law. This class of failure is often poorly understood, partly because terminology for discussing them is ambiguous, and partly because discussion using this ambiguous terminology ignores distinctions between different failure modes of this general type. This paper expands on an earlier discussion by Garrabrant, which notes there are "(at least) four different mechanisms" that relate to Goodhart's Law. This paper is intended to explore these mechanisms further, and specify more clearly how they occur. This discussion should be helpful in better understanding these types of failures in economic regulation, in public policy, in machine learning, and in Artificial Intelligence alignment. The importance of Goodhart effects depends on the amount of power directed towards optimizing the proxy, and so the increased optimization power offered by artificial intelligence makes it especially critical for that field.