As the discussion in the previous video suggested, uncertainty can reflect different realities. As former U. S. Defense Secretary Donald Rumsfeld once put it, “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." Though the wording may seem complicated on the surface, Rumsfeld’s approach to uncertainty is rather interesting. Think about what known knowns are. the facts, the confirmed reports and pieces of information, the data that any diligent observer has access to. Examples include the exchange rate, at this very moment, between the euro and the dollar, the identity of your immediate competitors and rivals, and the time of day in your most significant market. Think about what known unknowns represent. Those key variables and indicators that we know evolve all the time and the questions to which we’ll have an answer to at some point in time. Examples include tomorrow’s exchange rate between the euro and the dollar, the future sales of your star product and the number of future competitors that you’ll be facing in a few years. Addressing these questions is like rolling the dice. You have a certain probability of being right or wrong. In economic theory, this is what we might refer to as risk. The analyst can formulate different expectations and assign a probability to each. In other words, the doubt, in this case, is quantifiable. Let’s think about this for a second. What are the known knowns and the known unknowns in your personal or professional landscape? Pause this video and think about it for a moment. I’m sure you’ve come up with different answers. As we previously argued, the trick may lie in trying to address this question regularly, at key points of your personal and professional life. Finally, think about what unknown unknowns represent. These are the disruptive moments, the strategic surprises and the black swans. By definition, it is impossible to say what future unknown unknowns will look like, they wouldn’t be unknown in that case. But past strategic surprises include Ulysses’ use of the Trojan Horse in Ancient Greece, the Pearl Harbor attacks and the 9-11 attacks. Let’s consider this too. Can you think of other past surprises like these? Pause this video and think about it for a moment. In economic theory, this is what we refer to as uncertainty. It refers to the form of doubt that can’t be measured in terms of likelihood (contrary to risk) and to what is truly unpredictable. Quite the opposite, in fact, of rolling the dice. Ultimately, it seems that the challenge lies in understanding what in your landscape relates to known unknowns and to risk on the one hand, and what relates to unknown unknowns and to uncertainty on the other hand. In fact, we do live, to some extent, in a linear world. A linear world in which some events can be more or less predictable though a margin of error can exist. This is true when the causal link is clearly identifiable, when there is such a thing as cause and effect. There are numerous examples of this. If I get on a bus and knock over a couple of elderly people, I can expect at a minimum people to look at me in a disapproving way. Though the extent of the response from others is not fully certain, I can formulate some reasonable expectations about what those might be. Similarly, meteorologists have developed sophisticated models that are able to predict, with some level of certainty, the weather in the short run and in the medium run given current atmospheric conditions. This does not mean that they always get it right, but weather forecasts are becoming increasingly reliable. One last example comes from demography. Demographers rely on current age pyramids and projections to measure the rate at which populations are growing and ageing. As a result, they are able to make reliable projections about future age pyramids. But the cleverest analyst needs to recognize that we also live in a world full of surprises, disruptive moments and turning points. The interactions, in this case, are more akin to what happens in a poker game than when you role the dice. Indeed, bluff, personalities and a wide range of outcomes possible render any extrapolation or prediction effort completely useless. In that chaotic world, the complexity of the relationships between different events and the significant number of stakeholders make it hard to identify a causal link. Forecasting what will happen is, as a result, a fool’s errand. Remember. It is by definition hard to give future examples of these strategic surprises since they are not known, by definition. And you can always find an ex post explanation to most of these, that is, explain them after they’ve happened like in the case of outbreaks of epidemics or revolutions like the Arab Spring. Also remember the story of the turkey in the previous module. It is worth remembering though that the true causal links are not necessarily those that we can identify on the surface. They might, in practice, be far more complex than what we think. Author Nicholas Taleb provides a nice summary of this discussion, arguing that we live in two worlds at once. He states: “Humans simultaneously inhabit two systems: the linear and the complex (…). The linear domain is characterized by its predictability and the low degree of interaction among its components, which allows the use of mathematical methods that make forecasts reliable." He adds: “In complex systems, there is an absence of visible causal links between the elements, masking a high degree of interdependence and extremely low predictability. Nonlinear elements are also present, such as those commonly known, and generally misunderstood, as ‘tipping points’." Let’s see if you're still with me. What characterizes risk? A – The causal link; B – The fact that it is not measurable; C – The fact that it is quantifiable; The answers are A and C, of course. Remember. Risk refers to what is unknown but measurable and quantifiable, when the causal link is clear. It is the hallmark of the linear world. How about uncertainty? What characterizes it? A – The complexity of the causal link; B – The fact that it is not measurable; C – The fact that it is quantifiable. The answers are A and B, of course. Remember, uncertainty refers to what is not measurable, when probability distributions are unknown. In the uncertain world, the causal link is not straightforward. The bottom line? There is a difference between risk, what we see in the linear world, and uncertainty, what we observe in the complex world.
