Hallucination is confident outputs that are factually wrong. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0143, within the Trust family. The core principle: confident outputs that are factually wrong. 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
Confident outputs that are factually wrong.
Plain-English Definition
Confident outputs that are factually wrong.
Feynman Explanation
Confidence and accuracy are different variables. Models can max either.
Core Principle
Confident outputs that are factually wrong.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Confidence and accuracy are different variables. Models can max either.
Examples
- LLMs inventing citations, case law, or factual claims.
- Major source of enterprise AI risk.
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 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: confidence and accuracy are different variables. Models can max either. 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, major source of enterprise AI risk. It is amplified whenever major source of enterprise AI risk. Inside organizations that shows up as major source of enterprise AI risk. 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 retrieval grounding. Citation requirements. User training. The test of any redesign here is simple: after the change, can you name what the organization is now doing less of? If not, the payoff structure did not actually move.
Famous Experiments
Pending editorial review.
Design Principles
- Retrieval grounding. Citation requirements. User training.
Measurement Approaches
Pending editorial review.
Evidence
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The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Retrieval grounding. Citation requirements. User training.
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.
Treating AI as more humanlike than it is.
Reduced vigilance with automated systems.
Quantifying uncertainty in model outputs.
Matching trust in a system to its actual reliability.
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.
Logging of AI inputs, outputs, and decisions.
Inventory of models, data, tools, and dependencies in an AI system.
Tracing AI components for risk and compliance.
Where Hallucination 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.
- 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 Hallucination
- What is Hallucination?
- Hallucination is confident outputs that are factually wrong. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0143, within the Trust family. The core principle: confident outputs that are factually wrong. 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 Hallucination?
- Major source of enterprise AI risk. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0143).
- How is Hallucination exploited?
- Major source of enterprise AI risk.
- How do you design around Hallucination?
- Retrieval grounding. Citation requirements. User training.
- Which behavioral dimension does Hallucination belong to?
- Hallucination is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Trust", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0143 and its evidence grade is C.