It is one thing to act when the future can easily be anticipated, but it is something else to do so when the future is radically unknown. The difference between these two situations is captured by the concept of Knightian uncertainty. The idea dates back to 1921, when the famed American economist Frank Knight drew a distinction that to this day has proved fundamental for managers, policymakers and investors. When the range of possible contingencies is open and the odds are unclear, Knight argued, decision making is more dangerous and complex — but also more lucrative. Knight referred to this scenario as “uncertainty” to distinguish it from the more predictable setting of “risk.”
Why study uncertainty? The topic is, I believe, directly relevant to today’s ever-changing world. In the past 50 years, business researchers have made impressive advances by modeling situations in which the future could be more or less known in advance. From Porter’s five forces to the capital asset pricing model, most business models assume risk. But in the current business environment — of innovation, arbitrage, financial bubbles, climate change and terrorism — Knightian uncertainty, not risk, tops decision makers’ agendas. It should also be at the top of researchers’ agendas.
A workshop that I recently organized at Columbia Business School examined these crucial topics. The workshop explored several key dimensions of the Knightian approach to uncertainty and laid out a radical research agenda that is particularly relevant outside the ivory tower. To do so, it brought together a cross-disciplinary group of professors, including Bruce Kogut and me from the Business School’s Management Division, David Stark from Columbia’s Department of Sociology and Emanuel Derman from Columbia’s School of Engineering, as well as economists, sociologists and management theorists from the United States, England and France.
One of uncertainty’s most counterintuitive aspects is that the recipes that work best in a predictable world of risk can create major disasters under a scenario of uncertainty — and vice versa. This distinction is most clearly articulated in author and trader Nassim Taleb’s best-seller The Black Swan. At the workshop, Taleb distinguished between “Mediocristan” and “Extremistan.” Mediocristan denotes phenomena that fit a conventional normal distribution: the size of mountains, the weight of people or the profits of companies in established industries. Extremistan, by contrast, denotes rare phenomena that do not fit normal distributions: the wealth of Bill Gates, the returns of some hedge funds or stock-price movements during market crises. In Mediocristan, routine rules; in Extremistan, managers have to prepare for disruptive events like blockbuster hits, bankruptcies, killer apps or sudden defaults. Taleb’s implication is that it is dangerous for managers to mistake Mediocristan for Extremistan and assume that they can predict the world.
How, then, should companies confront uncertainty? A different type of organization is necessary. Uncertainty poses managerial problems that go far beyond making the numbers for the next quarter. Indeed, earnings may not even be the key measure of success. Employment, sales, production — even location — may be the reason why a company gains resources. For instances such as these, sociologist David Stark calls for a different internal logic of organization, one that allows companies to adhere to competing conceptions of worth: Let some employees think that profits are everything, and let others have faith in sales; and do not impose a single criterion for “what really matters,” he says. To accomplish this, firms need to avoid hierarchy and bureaucracy — and instead embrace what Stark calls “heterarchy.” Heterarchies pursue a dense network of horizontal interactions and distributed accountability.
Next, consider how uncertainty manifests itself on Wall Street. From portfolio insurance to options arbitrage, the last 40 years of academic research in finance have provided Wall Street practitioners with impressive investment tools. But in contexts of uncertainty, quantitative models can suddenly stop working and turn against their creators. Such was the message financial engineer Emanuel Derman gave at the workshop: mathematical formulas, he argued, provide a mind-broadening ability to value stocks by association. Modern finance operates by analogy, but profitable analogies become overexploited and may break down. Examples include the stock markets of 1987 and 1998, as well as this summer’s subprime mortgage debacle.
My own work, which I discussed at the workshop, addresses this dilemma as it pertains to quantitative finance, pointing to financial tools as a solution to the very challenge that they create. My presentation examined the ways in which merger arbitrageurs use a visualization known as the spread plot. Arbitrageurs operate in an environment of uncertainty. They exploit this uncertainty by relying on mathematical models, but their choice of formulas could be misguided. Arbitrageurs use the spread plot to gauge the choices made by their rivals and gain reassurance of their own use of models and formulas. In this way, quantitative finance provides a remedy for some of the problems that it originates.
Uncertainty also comes into play when we consider strategic rivalry. What does “acting strategically” mean when the opponent is unknown? Traditionally, strategic interaction has been studied in game theory, but classic game theory does not account for uncertainty. Specifically, orthodox game theorists typically assume that both players in a game know how the other thinks and what their interests and payoffs are — the so-called assumption of common knowledge. But assuming that you know your opponent is a dangerous thing when this is in fact not the case; by doing so, businesses may overexpose themselves or destroy the beginning of an emergent partnership.
Bringing uncertainty into a game radically shifts the player’s problem. Instead of an abstract exercise of calculative anticipation, strategizing becomes something more social and personal — in short, more interesting. The theory of so-called epistemic game theory views strategic interaction as a way for players to find out about each other. In this alternative, uncertainty-based conception of games, the moves by other players provide a partial answer to the central questions that players confront: How do my rivals think? And what do they think that I think of them?
Ultimately, awareness of uncertainty in organizations, markets and strategic games is crucial. The world constantly reminds us that we know less about our environment, our formulas and our rivals than we think we do. Gaining a better understanding of what uncertainty is and how it changes our conventional ways of managing, investing or reacting to others can help decision makers fare better in times of change.
Daniel Beunza is assistant professor of management at Columbia Business School. With David Stark, he recently co-organized the Uncertainty Workshop at Columbia University, a cross-disciplinary debate about how economic actors make decisions with limited knowledge of the world. For more information, see http://uncertaintycolumbia.googlepages.com/home.
