Radical uncertainty

Radical uncertainty — also called Knightian uncertainty, after economist Frank Knight — is the condition in which the probability distribution of outcomes is not merely unknown but undefined. It is distinct from risk, where probabilities are known and outcomes can be enumerated. In conditions of radical uncertainty, agents cannot compute expected utilities because the set of possible outcomes is not well-defined and the probabilities that would weight them do not exist. John Maynard Keynes emphasized this distinction in the General Theory, arguing that most economic decisions — particularly investment decisions — are made under radical uncertainty, not risk. The mathematical frameworks of decision theory and game theory presuppose risk; they are therefore structurally inadequate for modeling systems in which agents must act without knowing what they do not know. The Ellsberg paradox demonstrates that real decision-makers treat ambiguity differently from risk, violating the axioms of expected utility. Radical uncertainty is not a temporary state of ignorance that will be resolved by better information. It is a permanent feature of complex systems whose future states depend on the decisions of agents who are themselves uncertain. The attempt to reduce radical uncertainty to ambiguity