While believability-weighted decision-making can sound complicated, chances are you do it all the time, pretty much whenever you ask yourself, “Who should I listen to?” But it’s almost certainly true that you’d do it a lot better if you gave more thought to it.
Convert your principles into algorithms and have the computer make decisions alongside you.
If you can do that, you will take the power of your decision-making to a whole other level. In many cases, you will be able to test how that principle would have worked in the past or in various situations that will help you refine it, and in all cases, it will allow you to compound your understanding to a degree that would otherwise be impossible. It will also take emotion out of the equation. Algorithms work just like words in describing what you would like to have done, but they are written in a language that the computer can understand. If you don’t know how to speak this language, you should either learn it or have someone close to you who can translate for you. Your children and their peers must learn to speak this language because it will soon be as important or more important than any other language.
By developing a partnership with your computer alter ego in which you teach each other and each do what you do best, you will be much more powerful than if you went about your decision-making alone. The computer will also be your link to great collective decision-making, which is far more powerful than individual decision-making, and will almost certainly advance the evolution of our species.
In the future, artificial intelligence will have a profound impact on how we make decisions in every aspect of our lives, especially when combined with the new era of radical transparency about people that’s already upon us. Right now, whether you like it or not, it is easy for anyone to access your digital data to learn a tremendous amount about what you’re like, and this data can be fed into computers that do everything from predict what you’re likely to buy to what you value in life. While this sounds scary to many people, at Bridgewater we have been combining radical transparency with algorithmic decision-making for more than thirty years and have found that it produces remarkable results. In fact, I believe that it won’t be long before this kind of computerized decision-making guides us nearly as much as our brains do now.
The concept of artificial intelligence is not new. Even back in the 1970s, when I first started experimenting with computerized decision-making, it had already been around for nearly twenty years (the term “artificial intelligence” was first introduced in 1956 at a conference at Dartmouth College). While a lot has changed since then, the basic concepts remain the same.
To give you just one ultrasimple example of how computerized decision-making works, let’s say you have two principles for heating your home: You want to turn the heat on when the temperature falls below 68 and you want to turn the heat off between midnight and 5:00 a.m. You can express the relationship between these criteria in a simple decision-making formula: If the temperature is less than 68 degrees and the time is not between 5:00 a.m. and midnight, then turn on the heat. By gathering many such formulas, it’s possible to create a decision-making system that takes in data, applies and weighs the relevant criteria, and recommends a decision.
Specifying our investment decision-making criteria in algorithms and running historical data through them, or specifying our work principles in algorithms and using them to aid in management decision-making, are just bigger and more complicated versions of that smart thermostat. They allow us to make more informed and less emotional decisions much faster than we could on our own.
I believe that people will increasingly do this and that computer coding will become as essential as writing. In time, we will use machine assistants as much for decision-making as we do for information gathering today. As these machines help us, they will learn about what we are like, what we value, what our strengths and weaknesses are, and they will be able to tailor the advice they give us by automatically seeking out the help of others who are strong where we are weak. It won’t be long before our machine assistants are speaking to others’ machine assistants and collaborating in this way. In fact, that’s beginning to happen already.