Regret Aversion is we choose to minimize the regret we anticipate — not the expected value. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0576, within the Decision family. The core principle: we choose to minimize the regret we anticipate — not the expected value. 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 choose to minimize the regret we anticipate — not the expected value.
Plain-English Definition
We choose to minimize the regret we anticipate — not the expected value.
Feynman Explanation
The decision you can defend in hindsight beats the decision that maximizes EV.
Core Principle
We choose to minimize the regret we anticipate — not the expected value.
Mechanisms
Pending editorial review.
We choose to minimize the regret we anticipate — not the expected value.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Boards and execs systematically under-take asymmetric upside bets.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
The decision you can defend in hindsight beats the decision that maximizes EV.
Examples
- Buying the safer stock to avoid 'I told you so' after a loss.
- Boards and execs systematically under-take asymmetric upside bets.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
When this element shows up in a diagnostic, the instinct is to train people out of it. Training rarely moves it. The mechanism underneath it is straightforward: we choose to minimize the regret we anticipate — not the expected value. You can recognize it in the field by its signature: the decision you can defend in hindsight beats the decision that maximizes EV. 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, boards and execs systematically under-take asymmetric upside bets. It is amplified whenever boards and execs systematically under-take asymmetric upside bets. Inside organizations that shows up as boards and execs systematically under-take asymmetric upside bets. 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 separate decision quality from outcome quality in retros. Reward the first. Treat it as infrastructure. Once you can see it in your own system, most of the argument about culture resolves itself.
Famous Experiments
Pending editorial review.
Design Principles
- Separate decision quality from outcome quality in retros. Reward the first.
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.
- Separate decision quality from outcome quality in retros. Reward the first.
Pending editorial review.
Pending editorial review.
Interactive Mini Network
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Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
Choose the option whose worst-case regret is least bad.
Choose the option you'll regret least at 80.
How will I feel about this in 10 minutes / 10 months / 10 years?
Inattention or forgetfulness caused by low attention, hyperfocus, or distraction.
Forgetting to compare an offer with the next-best alternative.
Outcomes that could have happened but did not.
Overthinking a situation so that decision-making stalls.
The first number on the table silently sets the range for every number after it.
Tversky & Kahneman's classic: identical outcomes flip from 'risk averse' to 'risk seeking' when framed as lives saved vs. lives lost.
Our perception is shaped by what we selectively pay attention to.
Favoring suggestions from automated systems over conflicting human judgment.
A color appears different depending on adjacent colors.
Where Regret 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.
- 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 Regret Aversion
- What is Regret Aversion?
- Regret Aversion is we choose to minimize the regret we anticipate — not the expected value. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0576, within the Decision family. The core principle: we choose to minimize the regret we anticipate — not the expected value. 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 Regret Aversion?
- Boards and execs systematically under-take asymmetric upside bets. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0576).
- How is Regret Aversion exploited?
- Boards and execs systematically under-take asymmetric upside bets.
- How do you design around Regret Aversion?
- Separate decision quality from outcome quality in retros. Reward the first.
- Which behavioral dimension does Regret Aversion belong to?
- Regret Aversion is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Decision", class "Mental Model". Its permanent identifier is HBT-COG-0576 and its evidence grade is B.