Campbell's Law
The more a quantitative indicator drives decisions, the more it distorts the process it measures.
"Measure something hard enough and you'll deform it."
What is Campbell's Law? The more a quantitative indicator drives decisions, the more it distorts the process it measures. Quarterly earnings driving behaviors that destroy long-term value.
Standardized testing in schools producing teaching-to-the-test.
Quarterly earnings driving behaviors that destroy long-term value.
Distinguish leading from lagging indicators. Protect intent from measurement.
Standardized metrics in a system where humans set the standards and humans hit the metrics.
This term appears in this learning path
A school district ties teacher pay to standardized test scores. Three years in:
What does the data show?
Pick a reaction to Campbell's Law
One tap. We'll point you at the most useful next surface based on how this hits.
The full taxonomy entry
Every concept in the Atlas uses the same structure — so Campbell's Law 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 Campbell's Law most often show up unnoticed?
- Which metric, ritual, or contract clause quietly rewards Campbell's Law?
- If we removed every payoff for Campbell's Law, what behavior would replace it?
- Who benefits when Campbell's Law persists — and who pays the cost?
- People defend the status quo using the language of campbell's law.
- 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 Campbell's Law through this lens
Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.
Do you actually know Campbell's Law?
Three quick questions. Result is saved into your review streak — come back when the term is due to lock it in.
Which best describes Campbell's Law?
Worked example, counter-example & concept map
On-demand AI analysis grounded in the Lab's research. Cached on your device after first run.
Your nervous system has a region for this.
When you encounter Campbell's Law, your striatum has built a reward association — and the next time the cue appears, it will push you toward the behavior whether you decide to or not.
Reward learning, habit formation, anticipation, craving, action selection. Habits live here. So do addictions. Variable rewards train this circuit faster than fixed ones.
See Striatum in the Brain Atlas →Picked for you, from the Atlas
Ranked by shared learning paths, overlapping chips, and what you've saved.
When a measure becomes a target, it ceases to be a good measure.
A reward designed to reduce X produces more X.
Annual bonuses reward annual results — risk that blows up in year four with damage in year seven is rational.
Quarterly bonuses create end-of-quarter behavior changes.
Pay tied to stock price encourages short-term price management.
Once-a-year feedback rewards once-a-year behavior.
Send the card, not just the link
A pre-rendered social card with the title, eyebrow, and URL. Copy the link, post it anywhere, or download the SVG for slides.
More definitions to follow
Every term in the Atlas connects to a dozen others. Pick any of these and see where it takes you.
Match the target's current state, then gradually shift it toward the desired one.
A group assigned to challenge plans and find weaknesses.
Ideas, behaviors, and attitudes spread through social networks.
Shared resources get over-consumed by individual rational actors.
Autonomous agents deployed before liability frameworks exist.
Update beliefs in proportion to the strength of new evidence.
Associating a neutral stimulus with a meaningful one until the neutral one triggers the response.
With this, therefore because of this.
We believe we will change less in the future than we actually will.
We overweight one aspect of an event when predicting its impact.
Most of us think we're above average. Statistically, we can't be.
Describing a choice in terms of potential losses to trigger loss aversion.