Humans are terrible at making forecasts, we’re often told. Here’s one recent example at Bloomberg View:
I don’t mean to pick on either of those folks; you can randomly name any 10 strategists, forecasters, pundits and commentators and the vast majority of their predictions will be wrong. Not just a little wrong, but wildly, hilariously off.
The author is talking specifically about the economy, and I mostly agree with what I think he’s trying to say. But I’m tired of this framing:
Every now and again, it is worth reminding ourselves just how terrible humans are at predicting what will happen in markets and/or the economy.
Humans are amazing at predicting the future, and yes that includes what will happen in the economy. It’s just that when we sit down to talk about forecasting, for some reason we decide to throw out all the good predictions, and focus on the stuff that’s just hard enough to be beyond our reach.
There are two main avenues through which this happens. The first is that we idolize precision, and ignore the fact that applying a probability distribution to a range of possibilities is a type of prediction. So the piece above is right that it’s incredibly difficult for an economist to predict exactly the number of jobs that will be added in a given month. But experts can assign probabilities to different outcomes. They can say with a high confidence, for example, that the unemployment rate for August will be somewhere between say 5.5% and 6.5%.
You might think that’s not very impressive. But it’s a prediction, and a useful one. The knowledge that the unemployment rate is unlikely to spike over any given month allows businesses to feel confident in making investments, and workers to feel confident making purchases. I’m not saying we’re perfect at this probabilistic approach — recessions still surprise us. But it’s a legitimate form of prediction at which we do far better than random.
That example leads me to the second way in which we ignore good predictions. Talk of how terrible we are at forecasting ignores the “easy” cases. Will the sun rise tomorrow? Will Google still be profitable in a week? Will the price of milk triple over the next 1o days? We can answer these questions fairly easily, with high confidence. Yes, they seem easy. But they seem easy precisely because human knowledge and the scientific process have been so successfully incorporated into modern life.
And there are plenty of other predictions between these easy cases and the toughest ones that get thrown around. If you invest in the stock market for the long-term, you’re likely to make money. Somewhere around a third of venture-backed startups won’t make it to their 10th birthday. A few years down the line, today’s college graduates will have higher wages on average than their peers without a degree. None of these things are certain. But we can assign probabilities to them that would exceed that of a dart-throwing chimp. Perhaps you’re not impressed, but to me this is the foundation of modern society.
None of this is to say we shouldn’t hold pundits and experts more accountable for their bad predictions, or that we shouldn’t work to improve our predictions where possible. (And research suggests such improvement is often possible.)
But let’s not lose sight of all the ways in which we excel at prediction. Forecasting is a really broad category of thinking that is at the center of modern science. And compared to our ancestors, we’re pretty good at it.