I just finished reading Sapiens: A Brief History of Humankind by Yuval Noah Harari and I wanted to share an excerpt from the book that I thought was really interesting:
“History… cannot be predicted because it is chaotic. So many forces are at work and their interactions are so complex that extremely small variations in the strength of the forces and the way they interact produce huge differences in outcomes. Not only that, but history is what is called a “level two” chaotic system. Chaotic systems come in two shapes. Level one chaos is chaos that does not react to predictions about it. The weather, for example is a level one chaotic system. Though it is influenced by myriad factors, we can build computer models that take more and more of them into consideration, and produce better and better weather forecasts.
Level two chaos is chaos that reacts to predictions about it, and therefore can never be predicted accurately. Markets, for example, are a level two chaotic system. What will happen if we develop a computer program that forecasts with 100 per cent accuracy the price of oil tomorrow? The price of oil will immediately react to the forecast, which would subsequently fail to materialize. If the current price of oil is $90 a barrel, and the infallible computer program predicts that tomorrow it will be $100, traders will rush to buy oil so that they can profit from the predicted price rise. As a result, the price will shoot up to $100 a barrel today rather than tomorrow. Then what will happen tomorrow? Nobody knows.”
The Future is Mostly Unpredictable
It’s possible to accurately predict level one chaotic systems. As Harari writes, we can come up with more and more sophisticated computer models to produce better and better weather forecasts, because the weather doesn’t react to our predictions of it.
But it’s basically impossible to accurately predict level two chaotic systems because these systems will re-write themselves based on what is known about them. This is especially applicable to most complex systems involving humans. We’ve already seen the oil market example. Harari says that politics is also a second-order chaotic system. For example:
“Many people criticize Sovietologists for failing to predict the 1989 revolutions and castigate Middle East experts for not anticipating the Arab Spring revolutions of 2011. This is unfair. Revolutions are, by definition, unpredictable. A predictable revolution never erupts.
Why not? Imagine that it’s 2010 and some genius political scientists in cahoots with a computer wizard have developed an infallible algorithm that, incorporated into an attractive interface, can be marketed as a revolution predictor. They offer their services to President Hosni Mubarak of Egypt and, in return for a generous down payment, tell Mubarak that according to their forecasts a revolution would certainly break out in Egypt during the course of the following year.
How would Mubarak react? Most likely, he would immediately lower taxes, distribute billions of dollars in handouts to the citizenry – and also beef up his secret police force, just in case. The preemptive measures work. the year comes and goes and, surprise, there is no revolution. Mubarak demands his money back.
“Your algorithm is worthless?” he shouts at the scientists. “In the end I could have built another palace instead of giving all that money away!”
“But the reason the revolution didn’t happen is because we predicted it,” the scientists say in their defense.
“Prophets who predict things that don’t happen?” Mubarak remarks as he motions his guards to grab them. “I could have picked up a dozen of those for next to nothing in the Cairo marketplace.”
Harari’s example is like the opposite of a self-fulfilling prophecy – it’s a self-defeating prophecy. Whereas a self-fulfilling prophecy comes true just because it was predicted, a self-defeating prophecy doesn’t come true because it was predicted.
Conclusion
Warren Buffett famously does not give a lot of stock to models or predictions. As he says, “Forecasts may tell you a great deal about the forecaster; they tell you nothing about the future.”
Click To Tweet
We were given a big reminder of the limitations of predictive models during the 2016 Presidential Election, during which essentially no one predicted that Trump would win.
You should keep this recent history in mind when you read news articles and hear forecasts of what GDP growth and inflation will be once Trump officially takes office. The truth is that no one really knows for sure, and Trump’s already shown numerous times that he’s full of surprises.
When it comes to making predictions about level two chaotic systems in general, models can only serve as rough guides that allow us to make somewhat educated guesses. But these guesses might not always be right.
So while predictions of level one chaotic systems (weather forecasts, engineering simulations, etc.) can be very useful when making decisions, if you’re going to use a model when you’re investing then it’s wise to be healthily skeptical of the output and to discount the results when making your analysis.
In The Intelligent Investor, Ben Graham wrote:
“The more dependent the valuation becomes on anticipations of the future – and the less it is tied to a figure demonstrated by past performance – the more vulnerable it becomes to possible miscalculation and serious error. And the less it is tied to a figure demonstrated by past performance – the more vulnerable it becomes to possible miscalculation and serious error.”
In fact, value investors look for investments with a margin of safety (which is one of the core tenets of value investing) so that an accurate prediction of the future becomes unnecessary.
