What I learnt about forescasting from the US Government.

If I ask you what predictions you’ve gotten right in your life, almost certainly one or two will come to mind.
What you saw coming before it happened.
It’s also likely that the guess evokes in you a kind of regret that you didn’t do anything about it.
“I got it right, but it didn’t do me any good because I didn’t do anything with it.”

There are some very clever people out there who actually do something with forescasting. Putting money in or whatever.

They don’t just sit at the bar, like you and me.
If I had done something about it I would have been able to monetize things I’ve gotten right in my life, like:
– The rise of China
– Craft beer
– Bitcoin.
A few years ago I studied in California with some gentlemen who worked for the American government and who had some methodologies to get things right.
They structured and systematized uncertainty. Obviously they didn’t get everything right, but they got something more than average.
It is curious that there are a couple of techniques for this.

And it’s not the tarot or a crystal ball.

One is to look for strange or surprising things. If something doesn’t add up on the street or in the newspaper, watch out. There’s a tendency there.

The other fascinates me…
And that is that some things are so big that we don’t see them.

Like the particular day when more people moved to live in cities than in the countryside. It was around 2010 or so.

The day Humankind reached its peak oil usage. Also around that time.
These are things that are so big that you don’t see them,
And there are trends there.

This happens with data in health.

It’s so big that most people don’t see it.
You don’t see what you can get out of it.

And we’re not talking about complicated things like asking disruptive predictive questions.

We’re talking about everyday things, like understanding the effectiveness of an intervention.
Or pulling out epidemiology where you only had indirect and biased studies.
Or knowing which biologics are being most cost-effective by patient phenotype.

Look, I only got going with one of my accurate predictions.

And that was with AI in Medicine.
It’s still a little while for most to realize its power.
If you want to get ahead of the curve, it’s here.

Enrol in a project