Designing for the Bottom is designing systems for the lowest-capability user produces resilience for everyone. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0231, within the Systems family. The core principle: designing systems for the lowest-capability user produces resilience for everyone. 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
Designing systems for the lowest-capability user produces resilience for everyone.
Plain-English Definition
Designing systems for the lowest-capability user produces resilience for everyone.
Feynman Explanation
If your system requires geniuses, it will fail with mortals.
Core Principle
Designing systems for the lowest-capability user produces resilience for everyone.
Mechanisms
Pending editorial review.
Designing systems for the lowest-capability user produces resilience for everyone.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Robust org systems work with average performers, not just stars.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
If your system requires geniuses, it will fail with mortals.
Examples
- Curb cuts: built for wheelchairs, used by parents, delivery workers, travelers.
- Robust org systems work with average performers, not just stars.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
Most organizations meet this element as a personnel problem. It is not one. The mechanism underneath it is straightforward: designing systems for the lowest-capability user produces resilience for everyone. You can recognize it in the field by its signature: if your system requires geniuses, it will fail with mortals. 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, robust org systems work with average performers, not just stars. It is amplified whenever robust org systems work with average performers, not just stars. Inside organizations that shows up as robust org systems work with average performers, not just stars. 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 stress-test processes against the median user, not the ideal one. Design against it the way you would design against a known failure mode — assume it will appear, and price the exploit before someone finds it.
Famous Experiments
Pending editorial review.
Design Principles
- Stress-test processes against the median user, not the ideal one.
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.
- Stress-test processes against the median user, not the ideal one.
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.
Combining substances to create a new material stronger than its parts.
A single constraint limits the throughput of an entire system.
A system's throughput is constrained by its single slowest step.
Small visible disorders signal that bigger ones will be tolerated.
Adding manpower to a late software project makes it later.
Adding people to a late project makes it later.
A substance that speeds up a reaction without being consumed.
Nonlinear systems are highly sensitive to initial conditions.
Inputs combine under conditions to produce new outputs — sometimes irreversibly.
Software architecture mirrors org structure.
Working together has a cost that scales with the number of people.
The threshold at which a system becomes self-sustaining.
Where Designing for the Bottom 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 Designing for the Bottom
- What is Designing for the Bottom?
- Designing for the Bottom is designing systems for the lowest-capability user produces resilience for everyone. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0231, within the Systems family. The core principle: designing systems for the lowest-capability user produces resilience for everyone. 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 Designing for the Bottom?
- Robust org systems work with average performers, not just stars. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0231).
- How is Designing for the Bottom exploited?
- Robust org systems work with average performers, not just stars.
- How do you design around Designing for the Bottom?
- Stress-test processes against the median user, not the ideal one.
- Which behavioral dimension does Designing for the Bottom belong to?
- Designing for the Bottom is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Systems", class "Mental Model". Its permanent identifier is HBT-COG-0231 and its evidence grade is B.