Algorithmic Aversion is discounting algorithmic advice even when superior. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0031, within the Adoption family. The core principle: discounting algorithmic advice even when superior. 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
Discounting algorithmic advice even when superior.
Plain-English Definition
Discounting algorithmic advice even when superior.
Feynman Explanation
We forgive humans for errors and punish algorithms for the same ones.
Core Principle
Discounting algorithmic advice even when superior.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
We forgive humans for errors and punish algorithms for the same ones.
Examples
- Even when algorithms outperform, users prefer humans.
- Adoption failure of high-quality AI.
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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
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 operates in the Incentives dimension — what makes behavior more or less likely?. You can recognize it in the field by its signature: we forgive humans for errors and punish algorithms for the same ones. 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, adoption failure of high-quality AI. It is amplified whenever adoption failure of high-quality AI. Inside organizations that shows up as adoption failure of high-quality AI. 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 display calibration data. Allow user adjustment. Build trust gradually. The leverage is not in explaining the behavior to people. It is in changing what the behavior earns.
Famous Experiments
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Design Principles
- Display calibration data. Allow user adjustment. Build trust gradually.
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.
- Display calibration data. Allow user adjustment. Build trust gradually.
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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.
Innovators → early adopters → majority → laggards, AI-specific.
AI bolted onto existing workflows to look forward-leaning.
Redesigning roles around AI capability.
AI pilots that succeed and never scale.
Pre-deployment analysis of who loses what.
Teams under-reporting AI capability to protect comp or status.
Employees using unauthorized AI tools to get work done.
AI doesn't just replace tasks — it threatens identities.
Written rules about how AI may be used internally.
AI system that takes actions to achieve goals, often across tools.
When the agent acts, who's responsible?
Augmentation strategy vs. substitution strategy.
Where Algorithmic Aversion 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 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 Algorithmic Aversion
- What is Algorithmic Aversion?
- Algorithmic Aversion is discounting algorithmic advice even when superior. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0031, within the Adoption family. The core principle: discounting algorithmic advice even when superior. 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 Algorithmic Aversion?
- Adoption failure of high-quality AI. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0031).
- How is Algorithmic Aversion exploited?
- Adoption failure of high-quality AI.
- How do you design around Algorithmic Aversion?
- Display calibration data. Allow user adjustment. Build trust gradually.
- Which behavioral dimension does Algorithmic Aversion belong to?
- Algorithmic Aversion is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Adoption", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0031 and its evidence grade is C.