Risk, uncertainty, and nonprofit entrepreneurship

“As we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns – the ones we don’t know we don’t know.” –Donald Rumsfield

By Fredrick O. Andersson, Nonprofit Quarterly

I’ll admit, I don’t normally look to former defense secretary Donald Rumsfeld for keen scholarly insight. However, Rumsfeld’s now (in)famous passage eloquently illuminates what this article is all about: the importance of separating the risky from the uncertain.

As Nicolai Foss and Peter Klein noted in Organizing Entrepreneurial Judgment: A New Approach to the Firm, the above quote also captures and helps explicate a principal function of entrepreneurship. Drawing from the seminal work of Chicago economist Frank Hyneman Knight, the goal of this article is twofold: first, to discuss the difference between risk and uncertainty; and second, to deliberate on why the latter is essential for comprehending nonprofit sector entrepreneurship.

The Difference Between Risk and Uncertainty

Frank Hyneman Knight (1885–1972) is perhaps not the most recognized economist of the twentieth century. Yet, as a scholar he provided early and important contributions to the study of financial markets and entrepreneurship. He also mentored several noteworthy students at the University of Chicago, including Nobel Prize recipients James M. Buchanan, George Stigler, and Milton Friedman.

One key area of interest for Knight was economic dynamism, and in particular the link between economic change and knowledge. Rooted in his doctoral thesis, Knight’s book, Risk, Uncertainty, and Profit (1921), argued for – and introduced – his now illustrious distinction between risk and uncertainty.

To elucidate the difference between the concepts, Knight focuses on three types of probability, in which circumstances involving two of the types can be said to capture risky situations, and circumstances involving the third type can be said to capture situations entailing uncertainty. A priori probability reflects situations where one can assess the probability of an event in a deductive manner.

Imagine visiting a casino: When playing blackjack or standing at the roulette table, you not only know where these events will take place (i.e., you can define the state space – set of all possible configurations – of the game) but you can also come up with the probability of where the ball will land or the probability of pulling a certain card from the deck (which is the basis for the codified basic strategy in blackjack). Hence, roulette (for example) involves taking a risk knowing what you know with regard to the probability that the ball will land on red or black (and this number or that number).

Read the full story at Nonprofit Quarterly.
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