Categorizing Variants of Goodhart's Law.

Manheim, D., & Garrabrant, S. (2018). Categorizing Variants of Goodhart's Law. arXiv preprint.

Type

Preprint

Intellectual base

Grey literature

Year

2018

Link

https://arxiv.org/abs/1803.04585

Cited from

computer_science, economics_finance, philosophy

Cited by

3 citations across 3 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Agency
  • The same structure governs platform content-moderation (creators re-plan around any published rule), sanctions regimes (targeted states develop counter-strategies and grey-market routes), and clinical-quality metrics (clinicians avoid high-risk patients to protect their scores) — in each, treating an agent as an object guarantees the surprise.
• Goodhart's Law
  • The Goodhart diagnostic identifies all of these as exploitation of the construct-proxy wedge under control-loop pressure, and the same structural moves appear in the Soviet nail-factory legend, account-opening quotas, GDP-target manipulation, and language-model truthfulness proxies optimized into confident-sounding plausibility.

    This sourceTaxonomizes Goodhart effects (regressional, extremal, causal, adversarial), unifying Campbell's law, the McNamara fallacy, surrogation, reward hacking, and p-hacking as one mechanism in different substrates.

• Side Effect
  • Public health and machine learning: campaigns and deployed models aimed at one outcome shift others (weight gain, vaping uptake; user behavior, content ecosystems), the reward-hacking and Goodhart literatures being a class of side effects.

    This sourceTaxonomy of Goodhart and reward-hacking failure modes where optimizing a declared metric degrades the underlying substrate.

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