Local Maximum is optimizing in a small region while missing a better global solution. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0448, within the Systems family. The core principle: optimizing in a small region while missing a better global solution. 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
Optimizing in a small region while missing a better global solution.
Plain-English Definition
Optimizing in a small region while missing a better global solution.
Feynman Explanation
You climbed the tallest hill in the neighborhood, not the mountain.
Core Principle
Optimizing in a small region while missing a better global solution.
Mechanisms
Pending editorial review.
Optimizing in a small region while missing a better global solution.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Incremental optimization can trap you far from the best answer.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
You climbed the tallest hill in the neighborhood, not the mountain.
Examples
- Refining a current product instead of exploring a disruptive new model.
- Incremental optimization can trap you far from the best answer.
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: optimizing in a small region while missing a better global solution. You can recognize it in the field by its signature: you climbed the tallest hill in the neighborhood, not the mountain. 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, incremental optimization can trap you far from the best answer. It is amplified whenever incremental optimization can trap you far from the best answer. Inside organizations that shows up as incremental optimization can trap you far from the best answer. 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 periodically explore radically different alternatives, not just refinements. 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
- Periodically explore radically different alternatives, not just refinements.
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.
- Periodically explore radically different alternatives, not just refinements.
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 Local Maximum 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.
- 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 Local Maximum
- What is Local Maximum?
- Local Maximum is optimizing in a small region while missing a better global solution. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0448, within the Systems family. The core principle: optimizing in a small region while missing a better global solution. 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 Maximum?
- Incremental optimization can trap you far from the best answer. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0448).
- How is Local Maximum exploited?
- Incremental optimization can trap you far from the best answer.
- How do you design around Local Maximum?
- Periodically explore radically different alternatives, not just refinements.
- Which behavioral dimension does Local Maximum belong to?
- Local Maximum is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Systems", class "Mental Model". Its permanent identifier is HBT-COG-0448 and its evidence grade is B.