Courtesy Bias
Giving polite rather than honest answers to avoid offense.
"Politeness corrupts more research than fraud does."
What is Courtesy Bias? Giving polite rather than honest answers to avoid offense. Customer-feedback systems systematically over-report satisfaction.
Customer interviews where everyone says they 'love' the feature they won't use.
Customer-feedback systems systematically over-report satisfaction.
Use behavioral data over self-reported data. Make dissent comfortable.
Use the model. Pick the move.
Giving polite rather than honest answers to avoid offense. You've just seen this: Customer interviews where everyone says they 'love' the feature they won't use. Which lever does the model recommend?
Pick a lever. There are no neutral ones — every incentive funds a behavior somewhere.
Pick a reaction to Courtesy Bias
One tap. We'll point you at the most useful next surface based on how this hits.
The full taxonomy entry
Every concept in the Atlas uses the same structure — so Courtesy Bias 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 Courtesy Bias most often show up unnoticed?
- Which metric, ritual, or contract clause quietly rewards Courtesy Bias?
- If we removed every payoff for Courtesy Bias, what behavior would replace it?
- Who benefits when Courtesy Bias persists — and who pays the cost?
- People defend the status quo using the language of courtesy bias.
- 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 Courtesy Bias through 2 lenses
Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.
Do you actually know Courtesy Bias?
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Which best describes Courtesy Bias?
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 Courtesy Bias, your default mode network folds the experience into your ongoing story-of-self — which is why the same fact lands differently depending on who you think you are.
Self-referential thought, mind-wandering, narrative-of-self, mental time travel. Most of your waking thought is this network running scenarios about you, your status, your past, and your future.
See Default Mode in the Brain Atlas →Picked for you, from the Atlas
Ranked by shared learning paths, overlapping chips, and what you've saved.
A group decides on a course of action that nobody actually wants, because everyone assumes others prefer it.
Power tends to corrupt, and absolute power corrupts absolutely.
We attribute our own actions to situations but others' actions to their character.
We favor people who are similar to us or who like us.
Sacrificing for others without expecting a personal reward.
Masking the sponsors of a message to make it appear grassroots.
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.
Information that everyone knows, and everyone knows that everyone knows.
Quietly redefining a term mid-argument.
Systematic testing of model quality, safety, and capability.
The deepest level of listening — where new possibility emerges that neither party brought into the room.
One party has more or better information than another.
Treating the measure as the goal it was meant to approximate.
Worst-cases assumed as base-cases because they 'feel responsible.'
Output amplifies input — virtuous or vicious cycle.
A developmental model of human value systems (vMemes) emerging in response to life conditions.
Binding yourself now to avoid future temptation.
AI generates content; AI scrapes content; AI trains on its own output.
Passion is inversely proportional to the amount of real information available.