Data Drift is underlying data distribution changes over time. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0086, within the Workflow family. The core principle: underlying data distribution changes over time. 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
Underlying data distribution changes over time.
Plain-English Definition
Underlying data distribution changes over time.
Feynman Explanation
The world moves. Your model stays still.
Core Principle
Underlying data distribution changes over time.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
The world moves. Your model stays still.
Examples
- COVID broke many production models overnight.
- Production AI requires ongoing data observability.
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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 world moves. Your model stays still. 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, production AI requires ongoing data observability. It is amplified whenever production AI requires ongoing data observability. Inside organizations that shows up as production AI requires ongoing data observability. 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 statistical monitoring of input distributions. 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
- Statistical monitoring of input distributions.
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.
- Statistical monitoring of input distributions.
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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.
The relationship between inputs and outputs changes.
Evaluation suites that no longer reflect real-world conditions.
Performance degradation as real-world data shifts.
Generated training data that mimics real data.
AI system that takes actions to achieve goals, often across tools.
Designing processes from scratch around AI capability.
How much input the model can process at once.
Vector representation of content for similarity and search.
Systematic testing of model quality, safety, and capability.
Models learning from examples in the prompt.
How much delay the user experience tolerates.
Train a smaller model to imitate a larger one.
Where Data Drift 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.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
Questions about Data Drift
- What is Data Drift?
- Data Drift is underlying data distribution changes over time. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0086, within the Workflow family. The core principle: underlying data distribution changes over time. 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 Data Drift?
- Production AI requires ongoing data observability. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0086).
- How is Data Drift exploited?
- Production AI requires ongoing data observability.
- How do you design around Data Drift?
- Statistical monitoring of input distributions.
- Which behavioral dimension does Data Drift belong to?
- Data Drift is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Workflow", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0086 and its evidence grade is C.