Ambiguity Aversion is we prefer known risks to unknown ones, even when the unknown is better. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0031, within the Probability Bias family. The core principle: we prefer known risks to unknown ones, even when the unknown is better. 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
We prefer known risks to unknown ones, even when the unknown is better.
Plain-English Definition
We prefer known risks to unknown ones, even when the unknown is better.
Feynman Explanation
The devil we know consumes more capital than the angel we don't.
Core Principle
We prefer known risks to unknown ones, even when the unknown is better.
Mechanisms
We prefer known risks to unknown ones, even when the unknown is better.
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
The devil we know consumes more capital than the angel we don't.
Examples
- Sticking with a measurable but declining channel over an unmeasured but growing one.
- Innovation underfunded because ROI 'isn't proven yet.'
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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 element is common enough to feel like human nature and specific enough to be engineered around. The mechanism underneath it is straightforward: we prefer known risks to unknown ones, even when the unknown is better. You can recognize it in the field by its signature: the devil we know consumes more capital than the angel we don't. 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, aI investment routed to vendors with PDFs, not to better unproven systems. It is amplified whenever innovation underfunded because ROI 'isn't proven yet.'. Inside organizations that shows up as innovation underfunded because ROI 'isn't proven yet.'. 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 fund discovery as a separate budget with its own success criteria. 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
- Fund discovery as a separate budget with its own success criteria.
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.
- Fund discovery as a separate budget with its own success criteria.
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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.
We ignore underlying probabilities in favor of vivid specifics.
Believing a specific scenario is more likely than its more general one.
Believing past random events influence future independent ones.
Believing streaks predict future streaks.
Underestimating the probability of bad outcomes — especially to us.
Overestimating the probability of bad outcomes.
Worst-cases assumed as base-cases because they 'feel responsible.'
We underestimate time and cost; we overestimate benefit.
We prefer eliminating a small risk completely over reducing a larger one partially.
Systematic deviations from rationality in judgment.
Losses hurt roughly twice as much as equivalent gains feel good.
Doing something feels safer than doing nothing — even when nothing wins.
Where Ambiguity Aversion 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.
- EssayIncentives Under Crisis
How this element behaves under pressure.
- 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 Ambiguity Aversion
- What is Ambiguity Aversion?
- Ambiguity Aversion is we prefer known risks to unknown ones, even when the unknown is better. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0031, within the Probability Bias family. The core principle: we prefer known risks to unknown ones, even when the unknown is better. 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 Ambiguity Aversion?
- Innovation underfunded because ROI 'isn't proven yet.' The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0031).
- How is Ambiguity Aversion exploited?
- AI investment routed to vendors with PDFs, not to better unproven systems.
- How do you design around Ambiguity Aversion?
- Fund discovery as a separate budget with its own success criteria.
- Which behavioral dimension does Ambiguity Aversion belong to?
- Ambiguity Aversion is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Probability Bias", class "Cognitive Bias". Its permanent identifier is HBT-COG-0031 and its evidence grade is B.