The most calamitous failures of prediction

I’ve been talking recently about how the biggest problem with decisions isn’t information, but seeing what you want to see. It is a matter of how you frame issues, and how you resist or learn from events.

Nate Silver has become famous for his quantitative models of the US election, which outperformed most pundits. But, he says in his excellent book The Signal and the Noise: Why So Many Predictions Fail — but Some Don’t, what matters most is not the data or regressions. You have to get the frame right..

The most calamitous failures of prediction usually have a lot in common. We focus on those signals that tell a story about the world as we would like it to be, not how it really is. We ignore the risks that are hardest to measure, even when they pose the greatest threats to our well-being. We make approximations and assumptions about the world that are much cruder than we realize. We abhor uncertainty, even when it is an irreducible part of the problem we are trying to solve.

Markets focus so much on what the latest economic data mean for growth, the economic cycle and inflation. It’s the bread and butter of market commentary, even if it has become commoditized and everyone knows forecasts are generally inaccurate and overconfident.

But instead, what really matters is what incoming evidence says about the assumptions and frames and expectations people hold – and whether the evidence leads them to change their view. There is very little evidence you can make money out of predicting the economic cycle. But misperception is pervasive, and is the most critical factor in your most critical decisions. And you can’t easily see it yourself.