Regression to the Mean is extreme outcomes tend to be followed by more average ones. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0575, within the Probability family. The core principle: extreme outcomes tend to be followed by more average ones. 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
Extreme outcomes tend to be followed by more average ones.
Plain-English Definition
Extreme outcomes tend to be followed by more average ones.
Feynman Explanation
The best quarter is usually followed by an average one. Not because you got worse.
Core Principle
Extreme outcomes tend to be followed by more average ones.
Mechanisms
Pending editorial review.
Extreme outcomes tend to be followed by more average ones.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Misattributing mean reversion to skill or intervention is common.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
The best quarter is usually followed by an average one. Not because you got worse.
Examples
- A star hire's second year is less stellar than their first.
- Misattributing mean reversion to skill or intervention is common.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
The mistake with this element is treating it as irrationality. It is almost always a rational response to a payoff nobody wrote down. The mechanism underneath it is straightforward: extreme outcomes tend to be followed by more average ones. You can recognize it in the field by its signature: the best quarter is usually followed by an average one. Not because you got worse. Every element in the Cognition 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, misattributing mean reversion to skill or intervention is common. It is amplified whenever misattributing mean reversion to skill or intervention is common. Inside organizations that shows up as misattributing mean reversion to skill or intervention is common. 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 expect reversion and distinguish it from real performance changes. Measure the behavior, not the sentiment. A survey will tell you how people feel about this; only observed action tells you whether it changed.
Famous Experiments
Pending editorial review.
Design Principles
- Expect reversion and distinguish it from real performance changes.
Measurement Approaches
Pending editorial review.
Evidence
Pending editorial review.
Pending editorial review.
The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Expect reversion and distinguish it from real performance changes.
Pending editorial review.
Pending editorial review.
Interactive Mini Network
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Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
Extreme results tend to be followed by less extreme ones.
Ignoring general statistics in favor of specific, vivid information.
Posterior = (likelihood × prior) / evidence.
Update beliefs in proportion to the strength of new evidence.
Start with a prior; update with new evidence.
Novices experiencing early success, often due to variance and small samples.
High-impact, hard-to-predict, retrospectively explainable events.
Striking pattern that is statistically expected in large samples.
Reasoning in distributions — ranges and probabilities — rather than points.
Average outcomes across the population differ from outcomes across time for one person.
A rough calculation using order-of-magnitude reasoning.
In quantum mechanics, certain pairs of properties cannot both be precisely known.
Where Regression to the Mean is cited in the corpus
Essays, field guides, and diagnostics from The Incentives Lab that apply this element.
- EssayGoodhart's Law in the Real World
How measurable proxies capture judgment.
- EssayThe Perverse Incentives Hiding in Your KPIs
Cognitive shortcuts turned into scorecards.
- EssayAI Agents Inherit Your Incentives
How this element propagates into automated systems.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about Regression to the Mean
- What is Regression to the Mean?
- Regression to the Mean is extreme outcomes tend to be followed by more average ones. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0575, within the Probability family. The core principle: extreme outcomes tend to be followed by more average ones. 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 Regression to the Mean?
- Misattributing mean reversion to skill or intervention is common. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0575).
- How is Regression to the Mean exploited?
- Misattributing mean reversion to skill or intervention is common.
- How do you design around Regression to the Mean?
- Expect reversion and distinguish it from real performance changes.
- Which behavioral dimension does Regression to the Mean belong to?
- Regression to the Mean is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Probability", class "Mental Model". Its permanent identifier is HBT-COG-0575 and its evidence grade is B.