Goodhart's Law (AI form)
Optimizing a proxy of the goal degrades the actual goal.
"The metric is not the mission."
What is Goodhart's Law (AI form)? Optimizing a proxy of the goal degrades the actual goal. AI rolled out against proxy KPIs that diverge from real value.
Click-through rates that drive worse user experience.
AI rolled out against proxy KPIs that diverge from real value.
Continuously validate proxies against true business outcomes.
Pick what to reward the model for.
Optimizing a proxy of the goal degrades the actual goal. In the wild: Click-through rates that drive worse user experience.
Pick a lever. There are no neutral ones — every incentive funds a behavior somewhere.
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The full taxonomy entry
Every concept in the Atlas uses the same structure — so Goodhart's Law (AI form) can be compared, recombined, and cited like an element on a periodic table.
- Business
- Leadership
- Government
- Healthcare
- Education
- Sales
- Marketing
- AI
- Negotiation
- Media
- Public Policy
- Relationships
- Where in our org would Goodhart's Law (AI form) most often show up unnoticed?
- Which metric, ritual, or contract clause quietly rewards Goodhart's Law (AI form)?
- If we removed every payoff for Goodhart's Law (AI form), what behavior would replace it?
- Who benefits when Goodhart's Law (AI form) persists — and who pays the cost?
- People defend the status quo using the language of goodhart's law (ai form).
- Decisions cluster around the easiest narrative rather than the strongest evidence.
- New data changes the slide deck but not the decision.
- Anyone naming the pattern is treated as the problem.
Every Atlas entry is a node in a knowledge graph. See the related rail below to follow the connections.
See Goodhart's Law (AI form) through 2 lenses
Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.
Long-form essays that cite Goodhart's Law (AI form)
From the Research Center — where this concept gets argued, applied, and stress-tested.
Do you actually know Goodhart's Law (AI form)?
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Which best describes Goodhart's Law (AI form)?
Worked example, counter-example & concept map
On-demand AI analysis grounded in the Lab's research. Cached on your device after first run.
When you encounter Goodhart's Law (AI form), your prefrontal cortex has to do extra work to override the automatic response — and that override budget is finite.
Executive control, planning, impulse override, working memory, System 2. First thing to go offline under stress, fatigue, or low blood sugar. Why your 4pm decisions are worse than your 9am ones.
See Prefrontal in the Brain Atlas →Picked for you, from the Atlas
Ranked by shared learning paths, overlapping chips, and what you've saved.
Models trained to follow a written set of principles.
Outer: the spec matches our intent. Inner: the model actually pursues the spec.
The trained model develops its own internal optimizer.
What the model is actually optimizing.
Maximizing the reward signal in unintended ways.
Reinforcement learning from human feedback.
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More definitions to follow
Every term in the Atlas connects to a dozen others. Pick any of these and see where it takes you.
Shared identity ('we') drives compliance harder than shared interest.
Discounting algorithmic advice even when superior.
Passion is inversely proportional to the amount of real information available.
Pausing System 1 to engage System 2.
We don't decide once — we narrow, then evaluate, then commit.
Subjects often converge to neoclassical predictions only after many rounds of feedback — not on the first try.
Seeing things only in their conventional use.
Without active design, incentives decay toward gameable proxies.
Hospital group-purchasing and opaque contracting inflate device costs far above marginal cost.
Resolve an internal conflict by treating each side as a 'part' with a positive intent, then negotiating a shared higher outcome.
The mental zero from which gains and losses are judged. Move it, and the same outcome changes meaning.
Small changes that are not noticeable can add up to a significant change.