Knightian uncertainty

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In economics, Knightian uncertainty is a lack of any quantifiable knowledge about some possible occurrence, as opposed to the presence of quantifiable risk (e.g., that in statistical noise or a parameter's confidence interval). The concept acknowledges some fundamental degree of ignorance, a limit to knowledge, and an essential unpredictability of future events.

Knightian uncertainty is named after University of Chicago economist Frank Knight (1885–1972), who distinguished risk and uncertainty in his 1921 work Risk, Uncertainty, and Profit:^[1]

"Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated.... The essential fact is that 'risk' means in some cases a quantity susceptible of measurement, while at other times it is something distinctly not of this character; and there are far-reaching and crucial differences in the bearings of the phenomena depending on which of the two is really present and operating.... It will appear that a measurable uncertainty, or 'risk' proper, as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all."

In this matter Knight's own views were widely shared by key economists^[2] in the 1920s and 1930s who played a key role distinguishing the effects of risk from uncertainty. They were particularly concerned with the different impact on human behavior as economic agents. Entrepreneurs invest for quantifiable risk and return; savers may mistrust potential future inflation.

Whilst Frank Knight's seminal book^[1] elaborated the problem, his focus was on how uncertainty generates imperfect market structures and explains actual profits. Work on estimating and mitigating uncertainty was continued by G. L. S. Shackle who later followed up with Potential Surprise Theory.^[3][4] However, the concept is largely informal and there is no single best formal system of probability and belief to represent Knightian uncertainty. Economists and management scientists continue to look at practical methodologies for decision under different types of uncertainty.