Extension Neglect is ignoring the size of a sample or population in our judgments. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0288, within the Reasoning family. The core principle: ignoring the size of a sample or population in our judgments. 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
Ignoring the size of a sample or population in our judgments.
Plain-English Definition
Ignoring the size of a sample or population in our judgments.
Feynman Explanation
One vivid story beats a thousand silent ones.
Core Principle
Ignoring the size of a sample or population in our judgments.
Mechanisms
Pending editorial review.
Ignoring the size of a sample or population in our judgments.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Decision weight should scale with sample weight. Often doesn't.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
One vivid story beats a thousand silent ones.
Examples
- Reacting to a single customer complaint the same as to a survey of thousands.
- Decision weight should scale with sample weight. Often doesn't.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
This element is common enough to feel like human nature and specific enough to be engineered around. The mechanism underneath it is straightforward: ignoring the size of a sample or population in our judgments. You can recognize it in the field by its signature: one vivid story beats a thousand silent ones. 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, decision weight should scale with sample weight. Often doesn't. It is amplified whenever decision weight should scale with sample weight. Often doesn't. Inside organizations that shows up as decision weight should scale with sample weight. Often doesn't. 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 always note 'how many?' before reacting to 'what?'. Watch for it at the boundaries: handoffs, promotions, incident reviews, and budget cycles are where this element gets its power.
Famous Experiments
Pending editorial review.
Design Principles
- Always note 'how many?' before reacting to 'what?'
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.
- Always note 'how many?' before reacting to 'what?'
Pending editorial review.
Pending editorial review.
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.
Remembering more context around a scene than was actually present.
Deriving general rules from specific examples; the leap from instance to concept.
The brain evolved to reason adaptively, not always truthfully, to reduce the cost of errors.
We solve problems by adding, even when subtracting would be better.
Assuming that if one option is true, another must be false, when both can be true.
Presuming a purposeful actor behind events that may have no actor at all.
What cannot be settled by experiment is not worth debating.
A finite set of well-defined instructions for solving a problem or performing a computation.
Every model simplifies reality; some are still useful.
Researchers favor conclusions aligned with their school, team, or sponsor.
Using personal stories or isolated examples instead of evidence.
Claiming something is true or better because most people believe it.
Where Extension Neglect 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.
- 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 Extension Neglect
- What is Extension Neglect?
- Extension Neglect is ignoring the size of a sample or population in our judgments. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0288, within the Reasoning family. The core principle: ignoring the size of a sample or population in our judgments. 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 Extension Neglect?
- Decision weight should scale with sample weight. Often doesn't. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0288).
- How is Extension Neglect exploited?
- Decision weight should scale with sample weight. Often doesn't.
- How do you design around Extension Neglect?
- Always note 'how many?' before reacting to 'what?'
- Which behavioral dimension does Extension Neglect belong to?
- Extension Neglect is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Reasoning", class "Mental Model". Its permanent identifier is HBT-COG-0288 and its evidence grade is B.