Pre-2008 Mortgage Origination
Originators paid on volume, not on default rates, fueled the subprime collapse.
"If the loss is downstream, the loan looks great."
What is Pre-2008 Mortgage Origination? Originators paid on volume, not on default rates, fueled the subprime collapse. Risk passed downstream creates markets for bad loans.
Liar loans during the subprime boom.
Risk passed downstream creates markets for bad loans.
Originator skin-in-the-game. Risk retention rules.
Flip the incentive. Watch the side-effect move.
Originators paid on volume, not on default rates, fueled the subprime collapse. Caught in the wild: Liar loans during the subprime boom.
In the room: Risk passed downstream creates markets for bad loans.
Counter-move from the Atlas: Originator skin-in-the-game.
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The full taxonomy entry
Every concept in the Atlas uses the same structure — so Pre-2008 Mortgage Origination can be compared, recombined, and cited like an element on a periodic table.
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- Government
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- Education
- Sales
- Marketing
- AI
- Negotiation
- Media
- Public Policy
- Relationships
- Where in our org would Pre-2008 Mortgage Origination most often show up unnoticed?
- Which metric, ritual, or contract clause quietly rewards Pre-2008 Mortgage Origination?
- If we removed every payoff for Pre-2008 Mortgage Origination, what behavior would replace it?
- Who benefits when Pre-2008 Mortgage Origination persists — and who pays the cost?
- People defend the status quo using the language of pre-2008 mortgage origination.
- 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 Pre-2008 Mortgage Origination through 4 lenses
Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.
- Layer 2Behavioral Economics
Which biases are most likely operating right now?
- Layer 9Persuasion & Behavior Design
What is making this behavior easier than the alternative?
- Layer 11Economics & Mechanism Design
Who pays, who is paid, and what does the price signal hide?
- Layer 15AI & Alignment
What proxy reward is the AI optimizing — and what is it ignoring?
Do you actually know Pre-2008 Mortgage Origination?
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Which best describes Pre-2008 Mortgage Origination?
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 Pre-2008 Mortgage Origination, 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.
Annual bonuses reward annual results — risk that blows up in year four with damage in year seven is rational.
Auditors paid by the firms they audit have predictable blind spots.
Quarterly bonuses create end-of-quarter behavior changes.
Government rescues of failing institutions privatize gains and socialize losses.
Issuers pay raters who compete for the highest ratings.
Quarterly earnings drive quarterly behavior.
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.
Training is a one-time cost. Inference is forever.
The trained model develops its own internal optimizer.
Standards untethered from shipping cause paralysis and avoidance.
Agencies meant to regulate an industry get captured by it.
Police evaluated on ticket counts produce more tickets, not safer roads.
False positives vs. false negatives.
AI strategy = decisions about which capabilities to build and where.
Evaluating an argument's logic based on whether you agree with the conclusion.
Working memory has limits.
Reducing cognitive biases through training, structure, and process.
We measure ourselves against peers, not against our past selves.
Temporal landmarks (Monday, January, birthday) trigger new behavior attempts.