Monday Macro: Risk and uncertainty
This interview is from SDPB's daily public-affairs show, In the Moment, hosted by Lori Walsh.
Imagine you're navigating an obstacle course blindfolded. That's more or less how macroeconomic policy makers have had to face the past couple years. But what does that mean for folks like you and me? Joe Santos is a professor of economics and Dykhouse Scholar of Money, Banking and Regulation at South Dakota State University.
The audio accompanies this Monday Macro blog.
In 1921, Frank Knight (1885 – 1972), preeminent economist and a founding member of the (University of Chicago’s) Chicago School of economic thought, published, while an associate professor of economics at Iowa State University, Risk, Uncertainty and Profit, a 375-page treatise on the role and “peculiar income” of the entrepreneur in a free-enterprise economy (232). In the book, Knight now-famously and painstakingly distinguished between the concepts of risk and uncertainty; the latter, incidentally, is the source of the entrepreneur’s peculiar income—but that’s a topic for another day.
According to Knight (1921), risk is “measurable uncertainty” (233). This is to say, risk is a concept that refers to a distribution of possible outcomes about which we are reasonably well informed, because of theoretical deduction—the probability of rolling a three with a fair die is 1/6—or empirical induction—the probability of being struck by lightening is 1/500,000. To use Knight’s (1921) terms, 1/6 is an “a priori” probability, whereas 1/500,000 is a “statistical probability,” and both characterize risk (224-225).
In stark contrast, uncertainty—or, in the parlance of economics, Knightian uncertainty—is a concept that refers to a distribution of possible outcomes about which we know very little, because “there is no valid basis of any kind for classifying instances” (225). As Knight so eloquently explained,
"The practical difference between the two categories, risk and uncertainty, is that in the former the distribution of the outcome in a group of instances is known (either through calculation a priori or from statistics of past experience), while in the case of uncertainty this is not true, the reason being in general that it is impossible to form a group of instances, because the situation dealt with is in a high degree unique."Knight (1921), p. 233; a priori is italicized in original.