Local-Optimum Optimization
Greedy improvement loops climb hills that aren't the highest hill.
"You can be the best at the wrong thing."
What is Local-Optimum Optimization? Greedy improvement loops climb hills that aren't the highest hill. Local-greedy beats global-aware in most orgs.
Org units optimizing functional KPIs against system goals.
Local-greedy beats global-aware in most orgs.
System-level metrics. Cross-unit accountability.
Flip the incentive. Watch the side-effect move.
Greedy improvement loops climb hills that aren't the highest hill. Caught in the wild: Org units optimizing functional KPIs against system goals.
In the room: Local-greedy beats global-aware in most orgs.
Counter-move from the Atlas: System-level metrics.
Pick a reaction to Local-Optimum Optimization
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The full taxonomy entry
Every concept in the Atlas uses the same structure — so Local-Optimum Optimization can be compared, recombined, and cited like an element on a periodic table.
- Business
- Leadership
- Government
- Healthcare
- Education
- Sales
- Marketing
- AI
- Negotiation
- Media
- Public Policy
- Relationships
- Where in our org would Local-Optimum Optimization most often show up unnoticed?
- Which metric, ritual, or contract clause quietly rewards Local-Optimum Optimization?
- If we removed every payoff for Local-Optimum Optimization, what behavior would replace it?
- Who benefits when Local-Optimum Optimization persists — and who pays the cost?
- People defend the status quo using the language of local-optimum optimization.
- 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.
Every Atlas entry is a node in a knowledge graph. See the related rail below to follow the connections.
See Local-Optimum Optimization through 2 lenses
Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.
Do you actually know Local-Optimum Optimization?
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Which best describes Local-Optimum Optimization?
Worked example, counter-example & concept map
On-demand AI analysis grounded in the Lab's research. Cached on your device after first run.
Your nervous system has a region for this.
When you encounter Local-Optimum Optimization, your striatum has built a reward association — and the next time the cue appears, it will push you toward the behavior whether you decide to or not.
Reward learning, habit formation, anticipation, craving, action selection. Habits live here. So do addictions. Variable rewards train this circuit faster than fixed ones.
See Striatum in the Brain Atlas →Picked for you, from the Atlas
Ranked by shared learning paths, overlapping chips, and what you've saved.
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.
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More definitions to follow
Every term in the Atlas connects to a dozen others. Pick any of these and see where it takes you.
AI pilots that succeed and never scale.
Pre-deployment analysis of who loses what.
Manager calibration produces predictable distortions over years.
Annual appropriations force agencies to spend before fiscal year-end or lose future budget baseline.
Every model simplifies reality; some are still useful.
A color appears different depending on adjacent colors.
Individually rational choices that produce a collectively bad outcome.
Adding a clearly worse option steers people toward the option you wanted.
Average outcomes across the population differ from outcomes across time for one person.
How vividly we connect to our future self predicts long-term decision quality.
Distaste for unequal payoffs — including when we'd benefit.
Always have a working model of what your counterpart believes.