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The Incentives Lab
Mental Models · Strategy

Local vs. Global Maximum

The best place nearby vs. the best place overall.

"Climbing the wrong mountain very efficiently is still climbing the wrong mountain."

Quick answer

What is Local vs. Global Maximum? The best place nearby vs. the best place overall. When optimization becomes the enemy of innovation.

In the wild

Companies that optimize a dying market structure.

Why it matters in the room

When optimization becomes the enemy of innovation.

Counter-move

Periodic 'are we on the right mountain?' reviews.

Visual · Pattern
Local vs. Global Maximum — a recurring shape in how people decide.
Live example · Apply Local vs. Global Maximum

Use the model. Pick the move.

The best place nearby vs. the best place overall. You've just seen this: Companies that optimize a dying market structure. Which lever does the model recommend?

● Live

Pick a lever. There are no neutral ones — every incentive funds a behavior somewhere.

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Human Behavior Element™ · HBE Spec

The full taxonomy entry

Every concept in the Atlas uses the same structure — so Local vs. Global Maximum can be compared, recombined, and cited like an element on a periodic table.

About the standard →
M
LV
HBT-M1156
Official name
Local vs. Global Maximum
Mental Models · Strategy
Identity
HBT ID
HBT-M1156
Symbol
LV
Official name
Local vs. Global Maximum
Synonyms
Strategy
Keywords
Mental Models, Strategy, human behavior, incentive design
Version
v1.0
Last updated
Maintained by The Incentives Lab
Classification
Kingdom
Cognition
Domain
Frameworks
Family
Mental Model
Class
Strategy
Element
Local vs. Global Maximum
Definition
Scientific
The best place nearby vs. the best place overall.
Plain-English
The best place nearby vs. the best place overall.
Feynman
Climbing the wrong mountain very efficiently is still climbing the wrong mountain.
Core principle
The best place nearby vs. the best place overall.
One-sentence summary
When optimization becomes the enemy of innovation.
Mechanisms
Psychological
The best place nearby vs. the best place overall.
Behavioral econ.
When optimization becomes the enemy of innovation.
Neurological
Reward, threat, and salience circuits bias attention toward the cue.
Evolutionary
Heuristics that paid off in ancestral environments now misfire in modern systems.
Sociological
Group norms and status incentives reinforce the pattern across a team.
Computational
Models trained on biased human signals will replicate and amplify the pattern.
Systems thinking
Feedback loops between metrics, incentives, and behavior lock the pattern in place.
Signals & signature
Inputs (activators)
Companies that optimize a dying market structure.
Outputs (observable)
When optimization becomes the enemy of innovation.
Behavioral signature
You see Local vs. Global Maximum when the explanation for a decision sounds reasonable but the outcome keeps repeating.
Behavioral molecules
Often combines with related Atlas entries — see the rail below.
Pathways · before
A goal, metric, or contract clause makes the behavior rational locally.
Pathways · after
Locally rational choices accumulate into a systemic distortion.
Domains where it shows up
  • Business
  • Leadership
  • Government
  • Healthcare
  • Education
  • Sales
  • Marketing
  • AI
  • Negotiation
  • Media
  • Public Policy
  • Relationships
Examples
Everyday
Companies that optimize a dying market structure.
Modern
When optimization becomes the enemy of innovation.
Historical
A pattern repeatedly documented since the foundational behavioral science literature on mental model.
Famous experiments
See the References block — primary papers in the Atlas link out to the original studies.
Design principles
How to leverage
When optimization becomes the enemy of innovation.
How to reduce
Periodic 'are we on the right mountain?' reviews.
How to redesign
Periodic 'are we on the right mountain?' reviews.
The Perverse Incentive Lens™
How it's exploited
Organizations weaponize local vs. global maximum — sometimes deliberately, often by accident — when metrics reward the symptom rather than the outcome.
Common perverse incentives
Volume metrics, short review windows, bonus cliffs, and contracts that pay on activity rather than impact.
Failure modes
When Local vs. Global Maximum dominates, teams optimize for the dashboard while the real outcome quietly degrades.
Incentive redesign
Periodic 'are we on the right mountain?' reviews.
Ethical considerations
Don't engineer local vs. global maximum into customers, employees, or citizens as a manipulation tactic — design for informed choice instead.
Diagnostic questions
  • Where in our org would Local vs. Global Maximum most often show up unnoticed?
  • Which metric, ritual, or contract clause quietly rewards Local vs. Global Maximum?
  • If we removed every payoff for Local vs. Global Maximum, what behavior would replace it?
  • Who benefits when Local vs. Global Maximum persists — and who pays the cost?
Organizational warning signs
Metrics
A KPI is hit while the underlying outcome stalls or worsens.
Behaviors
People route around the rule rather than challenge it.
Language
'That's just how we do it here.' / 'The system requires it.'
Culture
Naming the pattern is treated as disloyalty.
Red flags
  • People defend the status quo using the language of local vs. global maximum.
  • Decisions cluster around the easiest narrative rather than the strongest evidence.
  • New data changes the slide deck but not the decision.
  • Anyone naming the pattern is treated as the problem.
Intervention playbook
Immediate
Make the perverse payoff visible to the people creating it.
30-day
Run a small pilot that pays for the outcome, not the proxy.
Long-term
Rewrite the comp plan, contract, or ritual so the right behavior becomes the easy behavior.
AI considerations
Detect
Audit training data and reward signals for the same pattern this element describes.
Avoid amplifying
Don't optimize models on metrics that already encode the perverse incentive.
Counteract
Use the model to surface where the pattern is most active, then redesign the incentive — not the model.
Measurement
Metrics
Outcome-to-proxy ratio over time.
Assessment
The Incentives Lab III Diagnostic.
Survey
Calibrated pulse questions on rules vs. outcomes.
Behavioral signals
Where people work around the system.
Observational
Where the dashboard and the lived experience disagree.
Scientific evidence
Evidence grade
Synthesized from the behavioral science literature; see Atlas references.
Replication
Tracked in the Atlas as primary, replicated, or contested.
Intervention confidence
Moderate — patterns generalize, mechanisms vary by context.
Research consensus
Broad agreement on the pattern; ongoing debate on boundary conditions.
Known limitations
Local context, culture, and incentive structure all change the strength of the effect.
Open questions
How does Local vs. Global Maximum interact with AI-mediated decisions at scale?
References
Meta-analyses
Tracked in the Atlas registry.
Seminal authors
Kahneman, Tversky, Thaler, Ariely, Cialdini, Ostrom, Simon — and the field they built.
Cross references

Every Atlas entry is a node in a knowledge graph. See the related rail below to follow the connections.

Disciplinary layers

See Local vs. Global Maximum through 3 lenses

Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.

Test yourself · 60 seconds

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Go deeper

Worked example, counter-example & concept map

On-demand AI analysis grounded in the Lab's research. Cached on your device after first run.

How this lands in you

Your nervous system has a region for this.

Primary region
Prefrontal Cortex

When you encounter Local vs. Global Maximum, your prefrontal cortex has to do extra work to override the automatic response — and that override budget is finite.

Executive control, planning, impulse override, working memory, System 2. First thing to go offline under stress, fatigue, or low blood sugar. Why your 4pm decisions are worse than your 9am ones.

See Prefrontal in the Brain Atlas →
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