Local-Optimum Optimization is greedy improvement loops climb hills that aren't the highest hill. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0172, within the Universal Pattern Perverse Pattern family. The core principle: greedy improvement loops climb hills that aren't the highest hill. 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
Greedy improvement loops climb hills that aren't the highest hill.
Plain-English Definition
Greedy improvement loops climb hills that aren't the highest hill.
Feynman Explanation
You can be the best at the wrong thing.
Core Principle
Greedy improvement loops climb hills that aren't the highest hill.
Mechanisms
Pending editorial review.
Greedy improvement loops climb hills that aren't the highest hill.
Pending editorial review.
Pending editorial review.
Local-greedy beats global-aware in most orgs.
Pending editorial review.
Pending editorial review.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
You can be the best at the wrong thing.
Examples
- Org units optimizing functional KPIs against system goals.
- Local-greedy beats global-aware in most orgs.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
This is one of the elements leaders describe as a values gap. It is a payoff gap. The mechanism underneath it is straightforward: greedy improvement loops climb hills that aren't the highest hill. You can recognize it in the field by its signature: you can be the best at the wrong thing. 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, local-greedy beats global-aware in most orgs. It is amplified whenever local-greedy beats global-aware in most orgs. Inside organizations that shows up as local-greedy beats global-aware in most orgs. 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 system-level metrics. Cross-unit accountability. The test of any redesign here is simple: after the change, can you name what the organization is now doing less of? If not, the payoff structure did not actually move.
Famous Experiments
Pending editorial review.
Design Principles
- System-level metrics. Cross-unit accountability.
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.
- System-level metrics. Cross-unit accountability.
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.
Once-a-year feedback rewards once-a-year behavior.
The more a quantitative indicator drives decisions, the more it distorts the process it measures.
Donors penalize 'overhead'; charities under-invest in capacity.
Donors penalize 'overhead' and starve capacity that produces outcomes.
A reward designed to reduce X produces more X.
Squeezing all slack from a system optimizes throughput but eliminates resilience.
'Equal value' exchanges incentivize subjective appraisal gaming to trade low-utility land for high-value public assets.
When a measure becomes a target, it ceases to be a good measure.
Deep specialization improves local output but breaks cross-domain understanding.
When rewards don't match stated values, culture quietly decays toward what is rewarded.
Insulation from risk changes the risks people take.
Systems with weak corrective feedback drift unchecked into failure modes.
Where Local-Optimum Optimization 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.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about Local-Optimum Optimization
- What is Local-Optimum Optimization?
- Local-Optimum Optimization is greedy improvement loops climb hills that aren't the highest hill. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0172, within the Universal Pattern Perverse Pattern family. The core principle: greedy improvement loops climb hills that aren't the highest hill. 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 Local-Optimum Optimization?
- Local-greedy beats global-aware in most orgs. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0172).
- How is Local-Optimum Optimization exploited?
- Local-greedy beats global-aware in most orgs.
- How do you design around Local-Optimum Optimization?
- System-level metrics. Cross-unit accountability.
- Which behavioral dimension does Local-Optimum Optimization belong to?
- Local-Optimum Optimization is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Universal Pattern Perverse Pattern", class "Perverse Incentive". Its permanent identifier is HBT-INC-0172 and its evidence grade is C.