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.
Bias busting tips from Nobel laureate Richard Thaler
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.”
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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.
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.
Nudging the corporation
The organizing principle of nudge is something we call choice architecture. Choice architecture is something that can apply in any company. How are we framing the options for people? How is that influencing the choices that they make? It can go anywhere from the mainstream ideas of nudge, so, say, it might involve making employees healthier.
One of the nice things about our (I call it) new building at Chicago Booth—I think it must be getting close to 15 years old, but to us it’s still a new building—one of the things the architect did was the faculty is divided across three floors: third, fourth, and fifth floors.
There are open stairwells that connect those floors. It does two things. One is it gives people a little more exercise. Because those stairs are very inviting, in a way that the stairwells that serve as fire exits are just the opposite.
Also it makes us feel more connected. You can hear people. I’m on the fourth floor, so in the middle. If I walk down the hall, I may have a chance encounter not just with the people on my floor but even with people on the adjacent floors. Because I’ll hear somebody’s voice, and I wanted to go talk to that guy.
There are lots of ways you can design buildings that will make people healthier and make them walk more. I wrote a little column about this in the
New York Times, about nudging people by making stuff fun. There was a guy in LA [Los Angeles] who wrote to me and said that they took this seriously.
They didn’t have an open stairwell in their building, but they made the stairwell that they did have more inviting. They put in music and gave everybody two songs they could nominate. They put in blackboards where people could put decorations and funny notes. I was reading something recently about another building that’s taken this idea.
Since you have to use a card to get in and out of the doors, they can keep track of who’s going in and out. So they can give you feedback on your phone or your Fitbit, of how many steps you’ve done in the stairwells. But the same is true for every decision that the firm is making.
There’s lots of talk about diversity these days. We tend to think about that in terms of things like racial diversity and gender diversity and ethnic diversity. Those things are all important. But it’s also important to have diversity in how people think.
When I came to Chicago in 1995, they asked me to help build up a behavioral-science group. At the time, I was one of two senior faculty members. The group was teetering on the edge of extinction. We’re up close to 20 now. As we’ve been growing, I’ve been nudging my colleagues.
Sometimes we’ll see a candidate and we’ll say, “That guy doesn’t seem like us.” They don’t mean that personally. They mean that the research is different from the research we do. Of course, there is a limit. We don’t want to hire somebody studying astrophysics in a behavioral-science department. Though we could use the IQ boost. But I keep saying, “No, we want to hire people that think differently from how we do, especially junior hires. Because we want to take risks.” That’s the place to take risks. That person does things that are a little different from us.
Either that candidate will convince us that that research is worthwhile to us, or will maybe come closer to what we do, or none of the above, and he or she will leave and go somewhere else. None of those are terrible outcomes. But you go into a lot of companies where everybody looks the same and they all went to the same schools. They all think the same way. And you don’t learn.
Behavioral science in business: Nudging, debiasing, and managing the irrational mind
There’s a quote—I may garble it—from Alfred P. Sloan, the founder of GM, ending some meeting, saying something like, “We seem to be all in agreement here, so I suggest we adjourn and reconvene in a week, when people have had time to think about other ideas and what might be wrong with this.”
I think strong leaders, who are self-confident and secure, who are comfortable in their skin and their place, will welcome alternative points of view. The insecure ones won’t, and it’s a recipe for disaster. You want to be in an organization where somebody will tell the boss before the boss is about to do something stupid.
Figure out ways to give people feedback, write it down, and don’t let the boss think that he or she knows it all. Figure out a way of debiasing the boss. That’s everybody’s job. You’d like it to be the boss’s job, but some bosses are not very good at it.
Making better decisions through technology
We’re just scratching the surface on what technology can do. Some applications in the healthcare sector, I think, are going to be completely game changing. Take diabetes, for example, a major cause of illness and expense. [For type 2 diabetes], most of the problem is people don’t take their medicine.
If they improved their diet and took their medicine, most of their problems would go away. We basically now have the technology to insert something in your body that will constantly measure your blood sugar and administer the appropriate drugs. Boom, we don’t have a compliance problem anymore, at least on the drug side.
There’s lots of fear about artificial intelligence. I tend to be optimistic. We don’t have to look into the future to see the way in which technology can help us make better decisions. If you think about how banks decide whom to give a credit card and how much credit to give them, that’s been done using a simple model for, I think, 30 years at least.
What I can see is the so-called
Moneyball revolution in sports—which is gradually creeping into every sport—is making less progress in the human-resources side than it should. I think that’s the place where we could see the biggest changes over the next decade.
Because job interviews are, to a first approximation, useless—at least the traditional ones, where they ask you things like, “What do you see yourself doing in ten years, or what’s your biggest weakness?” “Oh, I’m too honest. I work too hard. Those are my two biggest weaknesses.”
So-called structured interviews can be better, but we’re trying to change the chitchat into a test, to whatever extent you can do that. We wouldn’t hire a race-car driver by giving them an interview. We’d put them in a car, or better yet, because it would be cheaper, behind a video game and see how they drive.
It’s harder to see how people make decisions. But there’s one trading company I used to know pretty well. They would recruit the smartest people they could find right out of school. They didn’t care if they knew anything about options. But they would get them to bet on everything, and amounts of money that, for the kids, would be enough that they would think about it. So there’s a sporting event tonight, and they’d all have bets on it. What were they trying to do? They were trying to teach them what it feels like to size up a bet, what it feels like to lose and win. This was part of the training and part of the evaluation.
That was the job they were learning how to do, how to be traders. Now that job probably doesn’t exist anymore, but there’s some other job that exists. Figure out a way of mimicking some aspects of that, and test it, and get rid of the chitchat. Because all that tells you is whether you’re going to like the person, which may be important if it’s somebody you’re going to be working with day and night. If a doctor is hiring a nurse that’s going to work in a small office, it’s important that you get along. But if you’re hiring somebody that’s going to come to work in a big, global company, the chance that the person interviewing that candidate will work with that candidate is infinitesimal. So we don’t really care what the interviewer thinks of the interviewee. We care whether the interviewee will add something to the organization.