Uncertainty
Not knowing what will happen — sometimes with known odds, sometimes without any.
What it means
Uncertainty is the condition of not knowing which outcome will occur, and a foundational distinction separates two kinds with very different decision implications. Under risk, the possible outcomes and their probabilities are known or can be assigned, so choices can in principle be optimized by expected-utility calculations; under genuine, or Knightian, uncertainty, the probabilities themselves are unknown or unknowable, and standard tools break down. People are not indifferent between these: ambiguity aversion shows a strong preference for known risks over unknown ones even when this is incoherent, and behavior under deep uncertainty leans on heuristics, robustness, and the search for more information. Beyond this lies the distinction between aleatory uncertainty, irreducible randomness in the world, and epistemic uncertainty, ignorance that could in principle be reduced by learning. How uncertainty is perceived and communicated — as a probability, a range, or a frame — strongly shapes choices. It matters because most consequential decisions, from medicine to policy to investing, are made under uncertainty rather than the tidy risk of textbook gambles.
Examples
Drawing from an urn with a known 50-50 mix is risk; betting on an urn with an unknown mix is uncertainty — and most people will pay to avoid the latter even when the odds could be identical.
A surgeon can quote a complication rate drawn from thousands of past operations — that is risk. How a brand-new technique holds up over twenty years is uncertainty; nobody has the numbers yet.
A weather app's '30% chance of rain' rests on years of comparable days. Ask a founder the odds their new market exists in five years and the percentage is decoration, not data.
First described in Risk-versus-uncertainty distinction, Frank Knight (1921).