Events Are Outlandish
Prediction fails in Extremistan because it misses rare, large-impact events; cumulative errors matter more than frequency of correctness, and forecasters often cluster near each other rather than near the actual outcome.
THE IDEA
Events Are Outlandish
The problem with prediction is a little more subtle. It comes mainly from the fact that we are living in Extremistan, not Mediocristan. Our predictors may be good at predicting the ordinary, but not the irregular, and this is where they ultimately fail. All you need to do is miss one interest-rates move, from 6 percent to 1 percent in a longer-term projection (what happened between 2000 and 2001) to have all your subsequent forecasts rendered completely ineffectual in correcting your cumulative track record. What matters is not how often you are right, but how large your cumulative errors are.
And these cumulative errors depend largely on the big surprises, the big opportunities. Not only do economic, financial, and political predictors miss them, but they are quite ashamed to say anything outlandish to their clients—and yet events, it turns out, are almost always outlandish. Furthermore, economic forecasters tend to fall closer to one another than to the resulting outcome. Nobody wants to be off the wall.
Since my testing has been informal, for commercial and entertainment purposes, for my own consumption and not formatted for publishing, I will use the more formal results of other researchers who did the dog work of dealing with the tedium of the publishing process. I am surprised that so little introspection has been done to check on the usefulness of these professions. There are a few—but not many—formal tests in three domains: security analysis, political science, and economics. We will no doubt have more in a few years. Or perhaps not—the authors of such papers might become stigmatized by their colleagues. Out of close to a million papers published in politics, finance, and economics, there have been only a small number of checks on the predictive quality of such knowledge.
When to use it
Risk management: Evaluating financial portfolios for tail risks beyond normal distributions.
Strategic planning: Considering black swan events that could disrupt long-term business projections.
Personal decision-making: Not relying solely on average outcomes but preparing for extreme possibilities.
Policy analysis: Recognizing that rare geopolitical events can dramatically alter economic forecasts.
FROM THEORY TO EVERYDAY LIFE
In practice
Interest Rate Miss
Taleb uses an interest-rate move from 6% to 1%, dated in this passage between 2000 and 2001, to illustrate how a large missed change can dominate cumulative forecast error. The example does not describe a particular forecaster’s complete record.
Security Analysis Failure
An analyst correctly predicts minor price fluctuations for a stock over several months, but misses a sudden market crash. The crash's impact on the portfolio's overall return far outweighs the small correct predictions, illustrating that cumulative errors depend on outsized events.
A MOMENT TO REFLECT
Put it to work.
Think of a time when a small number of events had a large impact on your decisions. How did your predictions account for extreme events?
How can you adjust your forecasting to account for rare but consequential outliers?
Why do forecasters tend to cluster around similar predictions, and how does that affect their collective accuracy?
Often confused with
CONTINUE THE CONVERSATION
Source texts
- The Black Swan The Impact of the Highly Improbable With a new section On Robustness and Fragility (Nassim Nicholas Taleb)