Elimination-by-Aspects
Cut options by knocking out one attribute at a time, hardest threshold first.
"Speed-dating, with criteria."
What is Elimination-by-Aspects? Cut options by knocking out one attribute at a time, hardest threshold first. How executives narrow shortlists in practice.
Apartment hunting: under $X → near transit → no roommates → only three left.
How executives narrow shortlists in practice.
Be explicit about the order of cuts; the order quietly decides the outcome.
Use the model. Pick the move.
Cut options by knocking out one attribute at a time, hardest threshold first. You've just seen this: Apartment hunting: under $X → near transit → no roommates → only three left. Which lever does the model recommend?
Pick a lever. There are no neutral ones — every incentive funds a behavior somewhere.
Pick a reaction to Elimination-by-Aspects
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The full taxonomy entry
Every concept in the Atlas uses the same structure — so Elimination-by-Aspects 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 Elimination-by-Aspects most often show up unnoticed?
- Which metric, ritual, or contract clause quietly rewards Elimination-by-Aspects?
- If we removed every payoff for Elimination-by-Aspects, what behavior would replace it?
- Who benefits when Elimination-by-Aspects persists — and who pays the cost?
- People defend the status quo using the language of elimination-by-aspects.
- 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 Elimination-by-Aspects through 2 lenses
Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.
Do you actually know Elimination-by-Aspects?
Three quick questions. Result is saved into your review streak — come back when the term is due to lock it in.
Which best describes Elimination-by-Aspects?
Worked example, counter-example & concept map
On-demand AI analysis grounded in the Lab's research. Cached on your device after first run.
When you encounter Elimination-by-Aspects, 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 →Picked for you, from the Atlas
Ranked by shared learning paths, overlapping chips, and what you've saved.
How will I feel about this in 10 minutes / 10 months / 10 years?
Inattention or forgetfulness caused by low attention, hyperfocus, or distraction.
Predicting how we will feel in the future, usually inaccurately.
Forgetting to compare an offer with the next-best alternative.
Outcomes that could have happened but did not.
Overthinking a situation so that decision-making stalls.
Send the card, not just the link
A pre-rendered social card with the title, eyebrow, and URL. Copy the link, post it anywhere, or download the SVG for slides.
More definitions to follow
Every term in the Atlas connects to a dozen others. Pick any of these and see where it takes you.
People adopt behaviors and opinions because others do.
We climb from raw data to action through selection, meaning-making, and assumption — usually invisibly.
Value grows with the number of users.
Insurer approval workflows delay or deny care to lower medical-loss ratios.
Ask 'and then what?' — twice.
Generated training data that mimics real data.
'Free' is a different psychological category — disproportionately attractive.
Using an authority's opinion as evidence, regardless of its merits.
The more a quantitative indicator drives decisions, the more it distorts the process it measures.
Preferences aren't retrieved from a stable internal list — they're built on the spot from cues, context, and the elicitation method.
Reasoning in distributions — ranges and probabilities — rather than points.
Assuming everyone secretly agrees with us.