#uncertainty
9 nodes in the Atlas
- 002When Uncertainty Pays YouConvexity
Survivable exposure, payoff curvature and a worthwhile price require separate judgments.
- 003The Most Valuable Thing an AI Can Say Is "I Don't Know"The Invisible Frontier
A refusal becomes useful when its error rate, coverage and next owner are defined.
- 004The AI Wins You Can Measure Are Table StakesSelection
A productivity gain can be useful even when rivals can match it. Test costs and who keeps the benefit before scaling.
- 106Knightian Uncertainty: When No Defensible Reference Class Supports the Odds
Knightian uncertainty names a limit to the quantitative basis for a decision.
- 112Convexity: When Volatility Starts Paying You
Convexity is a property of payoff curvature over a stated range. A call option is one example; a loss cap alone does not establish it.
- 113The Jagged Frontier: The Edge You Can't See Until You're Past It
A jagged frontier is an uneven boundary of model capability. Similar-looking tasks can produce opposite effects from AI assistance.
- 114Out of Distribution: A Map of Where the Data Lived
Out of distribution describes a case relative to a named reference. Model reliability still needs evidence for the task.
- 117Prediction vs Decision: The Machine Forecasts, You Judge
A forecast estimates what may happen. Acting on it also requires an objective, an error tradeoff and authority. Systems can execute a configured rule; the forecast alone does not supply those conditions.
- 119Rare for You, Common for the Corpus
A case can be unfamiliar to its owner and documented elsewhere. The comparison still has to hold.