Here is the full transcript of German psychologist Gerd Gigerenzer’s TEDx Talk: How Do Smart People Make Smart Decisions? at TEDxNorrköping conference. This event occurred on June 13, 2012.
TRANSCRIPT:
Gerd Gigerenzer – German psychologist
How to make good decisions? If you open a book on rational choice, you will likely read the following message: look before you leap, analyze before you act. List all alternatives, all the consequences, and estimate the utilities and do the calculation.
This is a beautiful mathematical scheme, but it doesn’t describe how most people actually make decisions. And not even how those who write these books make decisions, as the following story illustrates.
A professor from Columbia University had an offer from a rival university, and he could not make up his mind whether to accept, reject, go or stay. A colleague took him aside and said, “What’s the problem? Just maximize your expected utility! You always write about doing this!”
Exasperated, the professor responded, “Come on, this is serious.”
The method of listing pros and cons and doing the calculation is an old one. Benjamin Franklin once in a letter to his nephew recommended exactly that. Listing all pros and cons, and then weighting and adding. And at the end of his letter, he wrote the following: “If you do not learn it, I apprehend you will never get married.”
Did you choose your partner by a calculation? I asked my friends who teach this method as the only method of rational choice, how they chose their partner if they had any choice at all. All of them said, “No, no, no.”
There was one exception. And he told me that he applied his own theory. He explained to me that he listed all the alternatives, all the consequences. For instance, will she still talk to me after being married? Will she take care of the kids and let me work in peace? And then he estimated for each of these women the probability that it will actually occur. And multiplied it by the utilities and made the calculation.
Then he proposed to the woman with the highest expected utility. She accepted. He never told her how he had chosen her. I met him recently. Now they are divorced.
I will talk today about two ways of making decisions. One is the one that is taught at the academia; it has many names: Benjamin Franklin’s bookkeeping method or expected utility theory. And this is a method that works in a world of risk, that is, when the probabilities, the consequences and alternatives are known. Here, statistical thinking is enough. A key example is lotteries or if you play the casino.
Then you can calculate how much you will lose. In a different world, the world of uncertainty, calculation is not enough. You need smart rules of thumb, that are technically called heuristics, and good intuitions, which are also based on smart rules of thumb.
I will talk today about decision making under uncertainty and also about the dangers of using systems that work for known risk and applying them to the world of uncertainty. Choices between two jobs or between partners are all in the world of uncertainty.
You can’t calculate everything, you don’t know the consequences, and there will be surprises. I will make four points and then illustrate them with two examples.
First, the best decision under risk is not the best decision under uncertainty. Second, heuristics that you need in order to make good decisions in the uncertainty are indispensable for good decision making. They are not, as it’s often claimed, a sign of a kind of mental retardation or of just mental laziness.
Third, complex problems do not always require complex solutions, and that’s again in the world of uncertainty. And finally, more information, more calculation, more time is not always better. Less can be more. Let’s go to the first example. This is sports.
How does an outfielder catch a flying ball? In baseball, in cricket, or maybe in soccer, where the goalie has to get it. How does he or she know where to run? There are two theories about that. One is it’s a complex problem, you need complex mental processes, and the other one is — it’s a complex problem under uncertainty, and you need to find a simple method for that.
Let’s look for the first one. Richard Dawkins, in his famous book “The Selfish Gene,” proposed the complex method. So what does the outfielder do? He or she calculates the trajectory. Have you ever calculated a trajectory? Okay, that’s what you do?
And this formula doesn’t even have wind in it or spin, so it’s not enough. But what else could it be? What you see here is the idea to apply a theory that works if you know everything, like under known risk, to the world of uncertainty. The key idea is that you say, “Oh, he behaves ‘as if’ -” and Dawkins puts in the “as if” – the player would calculate that. What’s the alternative? How do real players catch a ball? That’s my question.
And a number of experiments show that real players use a number of simple heuristics. I’ll show you one. This one works when the ball is already high up in the air. It’s called the gaze heuristic. It has three steps.
First, fixate your eye on the ball, start running, and finally, adjust the running speed so that the angle of gaze remains constant. This player here does exactly that. He runs so that the angle of gaze remains constant, and that brings him there where the ball will land. Do you want to see it again? Here it is.
Importantly: the player can ignore to estimate or calculate every variable that’s necessary to estimate the trajectory. Every one. It’s a heuristic that belongs to a family of heuristics that just looks at one good reason. And then you get there. You’ll find the same heuristics in evolutionary history, so birds and fish, when they hunt a prey or a mate – which is sometimes not so different – they just keep the optical angle constant in three-dimensional space, and that’s enough. No trajectory correlations.
This heuristic is used by players unconsciously. If you have ever interviewed a player and asked him how is he doing this so well, then you get “intuition”. And it’s intuition, meaning the person knows what to do, but doesn’t know why.
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