Goodhart's Law (AI form) is optimizing a proxy of the goal degrades the actual goal. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0138, within the Alignment family. The core principle: optimizing a proxy of the goal degrades the actual goal. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
Scientific Definition
Optimizing a proxy of the goal degrades the actual goal.
Plain-English Definition
Optimizing a proxy of the goal degrades the actual goal.
Feynman Explanation
The metric is not the mission.
Core Principle
Optimizing a proxy of the goal degrades the actual goal.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
The metric is not the mission.
Examples
- Click-through rates that drive worse user experience.
- AI rolled out against proxy KPIs that diverge from real value.
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Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
This is one of the elements leaders describe as a values gap. It is a payoff gap. The mechanism underneath it operates in the Incentives dimension — what makes behavior more or less likely?. You can recognize it in the field by its signature: the metric is not the mission. Every element in the Incentives dimension changes the perceived payoff of an action before the action happens, which is exactly where incentive design has leverage.
How it gets exploited
Left undesigned, aI rolled out against proxy KPIs that diverge from real value. It is amplified whenever aI rolled out against proxy KPIs that diverge from real value. Inside organizations that shows up as aI rolled out against proxy KPIs that diverge from real value. The pattern is the same one Goodhart's Law describes: the measurable proxy attracts the effort, and the purpose behind it quietly loses funding.
How the Lab designs around it
The redesign move is to continuously validate proxies against true business outcomes. Watch for it at the boundaries: handoffs, promotions, incident reviews, and budget cycles are where this element gets its power.
Famous Experiments
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Design Principles
- Continuously validate proxies against true business outcomes.
Measurement Approaches
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Evidence
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The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Continuously validate proxies against true business outcomes.
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Interactive Mini Network
Click any neighbor to re-center the graph and follow the threads of connection.
Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
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.
The model achieves the goal as stated, not as intended.
Written rules about how AI may be used internally.
Innovators → early adopters → majority → laggards, AI-specific.
AI system that takes actions to achieve goals, often across tools.
When the agent acts, who's responsible?
Augmentation strategy vs. substitution strategy.
Where Goodhart's Law (AI form) is cited in the corpus
Essays, field guides, and diagnostics from The Incentives Lab that apply this element.
- Field guideIncentives: definition, types, examples
The parent field guide for this element.
- ReferenceThe laws of incentives
Goodhart, Campbell, and the Cobra Effect.
- EssayAI Agents Inherit Your Incentives
How this element propagates into automated systems.
- EssayThe Perverse Incentives Hiding in Your KPIs
The measurement failure mode for this element.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about Goodhart's Law (AI form)
- What is Goodhart's Law (AI form)?
- Goodhart's Law (AI form) is optimizing a proxy of the goal degrades the actual goal. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0138, within the Alignment family. The core principle: optimizing a proxy of the goal degrades the actual goal. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
- What is an example of Goodhart's Law (AI form)?
- AI rolled out against proxy KPIs that diverge from real value. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0138).
- How is Goodhart's Law (AI form) exploited?
- AI rolled out against proxy KPIs that diverge from real value.
- How do you design around Goodhart's Law (AI form)?
- Continuously validate proxies against true business outcomes.
- Which behavioral dimension does Goodhart's Law (AI form) belong to?
- Goodhart's Law (AI form) is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Alignment", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0138 and its evidence grade is C.