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518: Daniel Kahneman | When Noise Destroys Our Best of Choices
The Jordan Harbinger Show
Understanding Noise and the Wisdom of Crowds in Judgment
This chapter explores the concept of 'noise' in judicial sentencing, emphasizing its statistical nature in collective decisions. It illustrates how averaging independent judgments can reduce variability, using the example of people guessing the weight of an ox to demonstrate the wisdom of crowds and the accuracy of collective judgment.
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Speaker 1
If
Speaker 2
we see judges handing out some sentences that are much more harsh than others, it might look like noise or it might look like some other sort of bias. How can we tell if something is noise or if it's racism, for example, especially when it comes to sentencing?
Speaker 1
Well, you can never detect noise in a single decision. Noise is a statistical observation. It applies to a set of judgments that should not vary, and they do. That's when we have noise. So you can never detect noise. Sometimes, on some occasion, when it's particularly blatant, you can see bias in a single judgment, but you will never see noise in a single judgment. And that is part of the reason that noise is generally neglected, which was part of the reason for writing this book.
Speaker 2
You're listening to The Jordan Harbinger Show with our guest, Daniel Kahneman. We'll be right back. Now, back to Daniel Kahneman on The Jordan Harbinger Show. In the book, you discuss the wisdom of crowds, and the example, I think, is guessing the weight of a cow or an ox or something along those lines. I suppose this is a famous example, and I vaguely remember being forced to learn about this in the statistics class in college. Can you take us through this because this is the wisdom of crowds on the one hand, somehow that gets more accurate, which I kind of didn't really see coming as somebody who looks at groups of humans and thinks, what are you all thinking collectively? Somehow we're right when it comes to guessing simple things. Well,
Speaker 1
it turns out that suppose you have a set of judgments of the same object and you average them. Then the more judgments there are that you average, the less noise there is. And there is a statistical function. If the judgments are identical, are independent of each other, then the noise goes down with the square root of the number of observations. It's completely predictable. And if you have enough observations, noise goes down to zero. In that case, you know, in that classic experiment by Francis Galton, there were more than a thousand people, I think, and they got the weight of the ox on average was two pounds off. But even if there had been a bias, when you average a thousand judgments, noise is gone. So noise is a phenomenon of individual judgments or of judgments of small groups of people or judgments of groups of people that are not independent of each other. When you get independent judgments and average them, noise will go away.
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