What do you know about the future? More than you think.

What do you know about tomorrow? How much will it look like today? 

Superforecasting: the art and science of prediction by Philip E. Tetlock, Dan Gardner. I skimmed through parts of the book. Yet I discovered a brand new of looking at the world. Do read, I borrowed it from the New York Public Library — please consider supporting them. 

The most interesting part for my purpose is toward the first half. There are some interesting stuff later for team leaders and managers in a corporate setting.

Tetlock and Gardner look at the superforecasters, people who spend a good chunk of their time making forecasts, and getting them right more often than not. Using a couple of simple rules, they are able to make very accurate predictions about very uncertain, far-off events. 

Prerequisite: you need to leave behind the notion of right or wrong and think probabilistically. If you can’t let go of that, move on to the next post.

How you look at an event. That’s what matters.

You think you know what tomorrow will be made of? You have that arrogance? Well, sorry to disappoint. You don’t. Look away from the specific case you are presented with and try to identify a generic case to which you can assign a probability.

You meet a fine woman, she wears glasses, she carries a bag with a bunch of books inside of it and likes to go and listen to literary conferences at night. Is she a librarian or a nurse? You say a librarian of course. Yet in our society, there are many more nurses than librarians and so the probability of a random stranger to be a nurse is a lot higher than her being a librarian…

Once you have the base probability, you can now work through your specific case and add additional factors to move your base probability up or down.

Are equity markets going to go down by September? 

S&P500 is highly valued right now, and in the past, markets at such valuation levels have a tendency to come back to the mean and go down. Some argue we are in the 95th percentile of valuation. You get a base probability of markets going down by September.

But the economy is strong, there are no signs of any worries on the horizon. You bump down the probability of markets going down. But the Fed is raising rates, who knows where inflation is going and how the new generation of traders is going to behave once they enter a world where interests and inflation are materially above zero? Bump the probability down.

And so on. Until you get comfortable with the probability you assign to the events you’re trying to forecast. 

Then day after day, update the forecast and probabilities based on the new information available each day in the news, in the markets etc.

One key point of this method is to be sincere about the probabilities and the updates. And to back-test your predictions once the facts are known and to understand how one could improve the probabilities. 

What facts were not properly incorporated?

The method departs from anything you see in the financial news every day: it is balanced and nuanced (financial TV is never balanced, always one side or the other). It performs a reality check (which financial pundits never do, have we ever seen the success rates of some very famous financial TV expert that I would not name?). And eventually it has the potential to lead to better decisions, in any context.

I unfortunately lack some of the basics in probability theory and I wish I could appreciate even more their discussion of base rate, Bayes theorem etc.

One caveat: Tetlock makes a very good by calling Nassim Taleb’s work on black swan. What if the probability assigned to a given event is subject to black swans? Then you can still make some forecasts, but you run the possibility that everything will come crashing down because of some extremely rare event you never thought could happen. Something that had a chance in a billion to happen, and yet it happened.

So my (very modest) contribution to this would be to add a level of variations, volatility, variance into the prediction one is making. Try to know how far what you don’t know extends.

A lot of food for thought. My first predictions start today.

What about you? What will you forecast? What do you look at to form your forecast?