Convexity vs. Concavity is convex payoffs gain more than they lose; concave do the opposite. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0189, within the Risk family. The core principle: convex payoffs gain more than they lose; concave do the opposite. 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
Convex payoffs gain more than they lose; concave do the opposite.
Plain-English Definition
Convex payoffs gain more than they lose; concave do the opposite.
Feynman Explanation
Hunt for convex. Run from concave.
Core Principle
Convex payoffs gain more than they lose; concave do the opposite.
Mechanisms
Pending editorial review.
Convex payoffs gain more than they lose; concave do the opposite.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Choosing strategies whose error distributions are kind.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
Hunt for convex. Run from concave.
Examples
- Venture capital is convex. Insurance underwriting is concave.
- Choosing strategies whose error distributions are kind.
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: convex payoffs gain more than they lose; concave do the opposite. You can recognize it in the field by its signature: hunt for convex. Run from concave. 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, choosing strategies whose error distributions are kind. It is amplified whenever choosing strategies whose error distributions are kind. Inside organizations that shows up as choosing strategies whose error distributions are kind. 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 plot every initiative on the convex/concave axis before committing. Watch for it at the boundaries: handoffs, promotions, incident reviews, and budget cycles are where this element gets its power.
Famous Experiments
Pending editorial review.
Design Principles
- Plot every initiative on the convex/concave axis before committing.
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.
- Plot every initiative on the convex/concave axis before committing.
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.
Preferring options with known probabilities over options with unknown ones.
We overweight outcomes that are certain relative to merely probable ones.
Influence tactics weaponized — manipulation, coercion, exploitation of trust.
Dread weighs roughly double in our calculus what the equivalent gain does.
Build buffers so small mistakes don't become fatal.
Frequent evaluation amplifies loss aversion and produces overly conservative behavior.
The moment after which reversing a course becomes impossible or extremely costly.
We overweight tiny probabilities of large gains or losses.
We ignore probability when outcomes are emotionally charged.
People take more risks when they feel safer.
Risk has known probabilities. Uncertainty doesn't.
Risk decisions are driven by current emotion — not just by computed probabilities.
Where Convexity vs. Concavity 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.
- EssayThe Comp Plan Is the Strategy
Where this element meets compensation design.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
Questions about Convexity vs. Concavity
- What is Convexity vs. Concavity?
- Convexity vs. Concavity is convex payoffs gain more than they lose; concave do the opposite. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0189, within the Risk family. The core principle: convex payoffs gain more than they lose; concave do the opposite. 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 Convexity vs. Concavity?
- Choosing strategies whose error distributions are kind. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0189).
- How is Convexity vs. Concavity exploited?
- Choosing strategies whose error distributions are kind.
- How do you design around Convexity vs. Concavity?
- Plot every initiative on the convex/concave axis before committing.
- Which behavioral dimension does Convexity vs. Concavity belong to?
- Convexity vs. Concavity is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Risk", class "Mental Model". Its permanent identifier is HBT-COG-0189 and its evidence grade is B.