## “Coconut uncertainty” and failing forecasts

12 August, 2013 at 18:23 | Posted in Economics, Statistics & Econometrics | 1 Comment

Many things that occur in the business world may not be predictable, but their unpredictability can at least be modeled. In other words, there are two types of uncertainty that practitioners need to be aware of. We call them subway and coconut uncertainty, respectively, and we’ll explain by way of a story.

Let’s imagine a character called Pierre. He’s a graduate of France’s famous engineering school, the École Polytechnique, and he lives and works in Paris. One of his passions is recording how long it takes him to get to work each morning via Paris’s highly efficient Métro system. The wait generally varies between almost nothing and just a few minutes. However, there are many one-day strikes, which can cause considerable delay or even force him to walk all the way to work. Some days, too, the large crowds of tourists on the platform can force him to miss a train.

The graph of Pierre’s daily commuting times fits the well-known bell-shaped curve of the normal distribution. In his statistics class, he learned that almost all the values in a normal distribution lie within three standard deviations of the mean, while 95% lie within two standard deviations. There are almost no extreme values; most of Pierre’s journey times are clustered neatly around the average of 43 minutes. The graph represents what we call “subway uncertainty.” It effectively models the time it takes Pierre to get to his office each morning, together with the uncertainty of being earlier or later than the average. Indeed, Pierre has used it to make probabilistic predictions of how long his journey will take — and was satisfied to find that his forecasts were accurate. Pierre’s model makes some important assumptions. To begin, it assumes that future days are drawn from the same distribution as was observed in the past. Provided there is no major change — a prolonged shutdown of the entire Métro system, interruptions to the city’s power supply, a strike — that is a safe assumption. As long as there’s continuity between the past and future, the model is reliable.

In addition to liking a reliable commute, Pierre also likes exotic vacations. Unfortunately, on a trip to Thailand he had a deadly accident. While seeking shade under a palm tree, a coconut fell on his head. Our unlikely hero was the victim of a highly unlikely event that we call “coconut uncertainty” — a kind of freak happening that you just can’t plan for. The truth is that most real-life situations are mixtures of subway and coconut uncertainty, which is precisely why coconut uncertainty interests us.

In technical terms, coconut uncertainty can’t be modeled statistically using, say, the normal distribution. That’s because there are more rare and unexpected events than, well, you’d expect. In addition, there’s no regularity in the occurrence of coconuts that can be modeled. And we’re not just talking about Taleb’s “black swans” — truly bizarre events that we couldn’t have imagined. There are also bubbles, recessions and financial crises, which may not occur often but do repeat at infrequent and irregular intervals. Coconuts, in our view, are less rare than you’d think. They don’t need to be big and hairy and come from space. They can also be small and prickly and occur without warning. Coconuts can even be positive: an inheritance from a long-lost relative, a lottery win or a yachting invitation from a rich client.

Pierre didn’t study psychology along with engineering and statistics. But if he had, he might have come across research showing that while people may be quite aware that rare events can occur, and may even be able to imagine several examples, they consistently underestimate the probability of at least one such event (including the ones they didn’t imagine) occurring. In other words, we tend to underestimate the size of the class of rare events. And that can lead to serious, sometimes mortal errors. Engineering disasters, for example, often arise because “fail-safe” systems crash due to the breakdown of only one previously unconsidered component …

Given the number of disastrously bad forecasts — and not just in the last few years — it’s clear that businesses need a different strategy to cope with coconut uncertainty … The key is not to develop precise plans based on predictions, but to have emergency plans for a variety of possibilities. If you live in Paris, it’s not necessary to plan for an earthquake or a piece of a satellite falling from the sky. But there are some actions you can take that can protect you from events you cannot predict. Indeed, many of us already do so by purchasing insurance or practicing fire drills in the workplace. Most insurance policies cover a wide range of potential disasters, and the evacuation techniques practiced for fire would be just as well suited for bomb scares, floods or gas leaks.

Exactly how you deal with uncertainty is for you and your team to decide … The main thing is to stop believing your own predictions about the future and to develop plans that will be sensitive to surprises, whether future credit crunches or other recessionary forces.

As coconuts go, the current economic crisis is a big hairy one from outer space. Will global, free market capitalism turn out to be another of those great political ideas that didn’t work in practice? We don’t know, but it’s interesting to speculate, and also important. In the end, the empire of capitalism will probably strike back, and it’s likely that the financial Jedi will return to preeminence (albeit, with powers diminished by regulation).

Spyros Makridakis et al.

## 1 Comment »

1. I consider subway uncertainty to be a bigger problem than this little story tells. Because when Pierre actually starts doing the maths of his travels to work, he works out the uncertainty in arrival time to work. Pierre is an engineer and wants to arrive to work in time every day. He therefore change his travel behavior based on his model. Suddenly his travel behavior occupy a completely unknown travel space for which his model knows nothing. Turns out that the earlier subway is much less used by commuters and is always on time, and he arrives to work ridiculusly early.

Pierre being an engineer says to himself, “No problem, I just “refine” my new model with all the data and add this to de model.”

Now Pierre is modelling a travel space that do not exist, but being an engineer he believes he has a better model.

Ofcourse in the case of a subway line, he is not going to go to the station at a time when there is no train, that would be stupid. But when this type of thing is done in other situations it may not be as self evident that one is modelling a space that do not exist. I have seen this done, even in real science.

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