Debiasing the corporation: An interview with Nobel laureate Richard Thaler

Here is a brief excerpt from an interview of Richard Thaler conducted by Bill Javetski and Tim Koller for the McKinsey Quarterly, published by McKinsey & Company.The University of Chicago professor explains how executives can battle back against biases that can affect their decision making. To read the complete interview, check out other articles, learn more about the firm, obtain subscription information, and register to receive email alerts,please click here.

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Whether standing at the front of a lecture hall at the University of Chicago or sharing a Hollywood soundstage with Selena Gomez, Professor Richard H. Thaler has made it his life’s work to understand and explain the biases that get in the way of good decision making.

In 2017, he was awarded the Nobel Prize for four decades of research that incorporates human psychology and social science into economic analysis. Through his lectures, writings, and even a cameo in the feature film The Big Short, Thaler introduced economists, policy makers, business leaders, and consumers to phrases like “mental accounting” and “nudging”—concepts that explain why individuals and organizations sometimes act against their own best interests and how they can challenge assumptions and change behaviors.

In this edited interview with McKinsey’s Bill Javetski and Tim Koller, Thaler considers how business leaders can apply principles of behavioral economics and behavioral finance when allocating resources, generating forecasts, or otherwise making hard choices in uncertain business situations.

Write stuff down

One of the big problems that companies have, in getting people to take risk, is something called hindsight bias—that after the fact, people all think they knew it all along. So if you ask people now, did they think it was plausible that we would have an African-American president before a woman president, they say, “Yeah, that could happen.”

All you needed was the right candidate to come along. Obviously, one happened to come along. But, of course, a decade ago no one thought that that was more likely. So, we’re all geniuses after the fact. Here in America we call it Monday-morning quarterbacking.

One of the problems is CEOs exacerbate this problem. Because they have hindsight bias. When a good decision happens—good meaning ex ante, or before it gets played out—the CEO will say, “Yeah, great. Let’s go for that gamble. That looks good.”

Two years later, or five years later, when things have played out, and it turns out that a competitor came up with a better version of the same product that we all thought was a great idea, then the CEO is going to remember, “I never really liked this idea.”

One suggestion I make to my students, and I make this suggestion about a lot of things, so this may come up more than once in this conversation, is “write stuff down.” I have a colleague who says, “If you don’t write it down, it never happened.”

What does writing stuff down do? I encourage my students, when they’re dealing with their boss—be it the CEO or whatever—on a big decision, not whether to buy this kind of computer or that one but career-building or -ending decisions, to first, get some agreement on the goals, what are we trying to achieve here, the assumptions of why we are going to try this risky gamble, risky investment. We wouldn’t want to call it a gamble.

Essentially memorialize the fact that the CEO and the other people that have approved this decision all have the same assumptions, that no competitor has a similar product in the pipeline, that we don’t expect a major financial crisis.

You can imagine all kinds of good decisions taken in 2005 were evaluated five years later as stupid. They weren’t stupid. They were unlucky. So any company that can learn to distinguish between bad decisions and bad outcomes has a leg up.

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We’re doing this interview in midtown New York, and it’s reminding me of an old story. Amos Tversky, Danny Kahneman, and I were here visiting the head of a large investment company that both managed money and made earnings forecasts.

We had a suggestion for them. Their earnings forecasts are always a single number: “This company will make $2.76 next year.” We said, “Why don’t you give confidence limits: it’ll be between $2.50 and $3.00—80 percent of the time.”

They just dropped that idea very quickly. We said, “Look, we understand why you wouldn’t want to do this publicly. Why don’t you do it internally?”

Duke does a survey of CFOs, I think, every quarter. One of the questions they ask them is a forecast of the return on the S&P 500 for the next 12 months. They ask for 80 percent confidence limits. The outcome should lie between their high and low estimate 80 percent of the time. Over the decade that they’ve been doing this, the outcome occurred within their limits a third of the time, not 80 percent of the time.

The reason is their confidence limits are way too narrow. There was an entire period leading up to the financial crisis where the median low estimate, the worst-case scenario, was zero. That’s hopelessly optimistic. We asked the authors, “If you know nothing, what would a rational forecast look like, based on historical numbers?”

It would be plus 30 percent on the upside, minus 10 percent on the downside. If you did that, you’d be right 80 percent of the time—80 percent of the outcomes would occur in your range. But, think about what an idiot you would look like. Really? That’s your forecast? Somewhere between plus 30 and minus ten? It makes you look like an idiot.

It turns out it just makes you look like you have no ability to forecast the stock market, which they don’t; nor does anyone else. So providing numbers that make you look like an idiot is accurate. Write stuff down. Anybody that’s making repeated forecasts, there should be a record. If you have a record, then you can go back. This takes some patience. But keeping track will bring people down to earth.

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Here is a direct link to the complete interview.

Bill Javetski
is an executive editor with McKinsey Publishing and is based in McKinsey’s New Jersey office, and Tim Koller is a partner in the New York office.


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