Daniel Kahneman, 78, is an Israeli-American psychologist specializing in the psy…

Daniel Kahneman, 78, is an Israeli-American psychologist specializing in the psychology of judgment and decision-making as well as behavioral economics. He is currently a senior scholar and emer itus professor at Princeton University. In 2002, Kahneman was awarded the Nobel prize in economics for his work on prospect theory, which helps explain the role biases play in decision-making. In 2011, he published a summary of much of his research in "Thinking, Fast and Slow,". Below are extracts of an interview that took place in May 2012.

DK:
"Psychologists distinguish between a "System 1" and a "System 2," which control our actions. System 1 represents what we may call intuition. It tirelessly provides us with quick impressions, intentions and feelings. System 2, on the other hand, represents reason, self-control and intelligence.

System 2 is the one who believes that it's making the decisions. But in reality, most of the time, System 1 is acting on its own, without your being aware of it. It's System 1 that decides whether you like a person, which thoughts or associations come to mind, and wha t you feel about something. All of this happens automatically. You can't help it, and yet you often base your decisions on it.

System 1 can never be switched off. You can't stop it from doing its thing. System 2, on the other hand, is lazy and only becomes active when necessary. Slow, deliberate thinking is hard work. It consumes chemical resources in the brain, and people usually don't like that. It's accompanied by physical arousal, increasing heart rate and blood pressure, activated sweat glands and dilated pupils.

The pupil normally fluctuates in size, mostly depending on incoming light. But, when you give someone a mental task, it widens and remains surprisingly stable — a strange circumstance that proved to be very useful to us. In fact, the pupils reflect the extent of mental effort in an incredibly precise way. I have never done any work in which the measurement is so precise.

Our intuition works very well for the most pa rt. But it's interesting to examine where it fails.

It depends on the field. In the stock market, for example, the predictions of experts are practically worthless. Anyone who wants to invest money is better off choosing index funds, which simply follow a certain stock index without any intervention of gifted stock pickers. Year after year, they perform better than 80 percent of the investment funds managed by highly paid specialists. Nevertheless, intuitively, we want to invest our money with somebody who appears to understand, even though the statistical evidence is plain that they are very unlikely to do so. Of course, there are fields in which expertise exists. This depends on two things: whether the domain is inherently predictable, and whether the expert has had sufficient experience to learn the regularities. The world of stock is inherently unpredictable.

A computer will be just as unreliable at predicting stock prices as a human be ing. And the political situation in 20 years is probably completely unpredictable; the world is simply too complex. However, computer models are good where things are relatively regular. Human judgment is easily influenced by circumstances and moods: Give a radiologist the same X-ray twice, and he'll often interpret it differently the second time. But with an algorithm, if you give it the same information twice, it will turn out the same answer twice.

The entertainment industry wastes a lot of money on films that don't work. It shouldn't be that difficult to develop a program that at least doesn't do any worse than the intuitive judgments that govern these decisions now.

So-called "evidence-based medicine" is making progress, and it's based on clear, replicable algorithms. Or take the oil industry. There are strict procedures on deciding whether or not to drill in a specific location. They have a set of questions that they ask, and then they measure. Relying on intuition would be far too error-prone. After all, the risks are high, and there is a lot of money at stake".