Local vs. Global Maximum is the best place nearby vs. the best place overall. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0449, within the Strategy family. The core principle: the best place nearby vs. the best place overall. 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
The best place nearby vs. the best place overall.
Plain-English Definition
The best place nearby vs. the best place overall.
Feynman Explanation
Climbing the wrong mountain very efficiently is still climbing the wrong mountain.
Core Principle
The best place nearby vs. the best place overall.
Mechanisms
Pending editorial review.
The best place nearby vs. the best place overall.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
When optimization becomes the enemy of innovation.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
Climbing the wrong mountain very efficiently is still climbing the wrong mountain.
Examples
- Companies that optimize a dying market structure.
- When optimization becomes the enemy of innovation.
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: the best place nearby vs. the best place overall. You can recognize it in the field by its signature: climbing the wrong mountain very efficiently is still climbing the wrong 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, when optimization becomes the enemy of innovation. It is amplified whenever when optimization becomes the enemy of innovation. Inside organizations that shows up as when optimization becomes the enemy of innovation. 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 periodic 'are we on the right mountain?' reviews. The leverage is not in explaining the behavior to people. It is in changing what the behavior earns.
Famous Experiments
Pending editorial review.
Design Principles
- Periodic 'are we on the right mountain?' reviews.
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.
- Periodic 'are we on the right mountain?' reviews.
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.
Basics first (health/finances), then depth (mastery/impact), then altruism (widening circle).
A problem whose solution requires people to change their values, beliefs, or habits — not just apply expertise.
Holding a different — and correct — set of tools than everyone else.
Someone who incites others to take illegal or rash action, often to expose or discredit them.
Sun Tzu's ancient treatise on strategy, deception, and positioning.
Conflict between belligerents with very different resources and tactics.
Start from the desired future and work backward to the steps that make it inevitable.
Winning the disputed population, not just defeating the visible opponent.
Countering hostile narratives with truthful, well-timed alternatives.
Sort situations into Clear, Complicated, Complex, Chaotic, or Confused — each needs a different response.
Eroding the will to act, even when capacity remains.
Munger's principle: the surest way to get something is to deserve it.
Where Local vs. Global 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.
- 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 vs. Global Maximum
- What is Local vs. Global Maximum?
- Local vs. Global Maximum is the best place nearby vs. the best place overall. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0449, within the Strategy family. The core principle: the best place nearby vs. the best place overall. 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 vs. Global Maximum?
- When optimization becomes the enemy of innovation. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0449).
- How is Local vs. Global Maximum exploited?
- When optimization becomes the enemy of innovation.
- How do you design around Local vs. Global Maximum?
- Periodic 'are we on the right mountain?' reviews.
- Which behavioral dimension does Local vs. Global Maximum belong to?
- Local vs. Global Maximum is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Strategy", class "Mental Model". Its permanent identifier is HBT-COG-0449 and its evidence grade is B.