Your Brain on Uber | Hidden Brain This week we feature Keith Chen, a behavioral economist at UCLA and the head of economic research at Uber. Keith explains why surge pricing makes us nuts and discusses our weird economic choices.

This Is Your Brain On Uber

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This is HIDDEN BRAIN. I'm Shankar Vedantam.


VEDANTAM: My guest today is Keith Chen. He's a behavioral economist at UCLA. He's also the head of economic research at Uber, the ride-sharing company. Keith's going to talk about some of the behavioral anomalies that Uber has observed, and we're then going to talk about some of Keith's earlier work which explored the evolutionary and cultural origins of certain biases and heuristics. Keith Chen, welcome to HIDDEN BRAIN.

KEITH CHEN: Thank you so much for having me. I'm a big fan of the podcast.

VEDANTAM: I want to start by talking about surge-pricing. Uber charges more when demand is high based on the idea that this is going to draw more drivers into the pool and increase the supply of rides. Now, this makes perfect sense from the point of view for traditional economists, but you're a behavioral economist and you must know there's something about charging different prices for the same products that rubs customers the wrong way. They say, hey, five minutes ago this ride was 10 dollars. Now it's 20.

CHEN: So as you can imagine, I hear over and over again, you know, both at my dinner table and at family gatherings...


CHEN: ...That surge-pricing can feel very unfair to customers. But it's been a really integral part of Uber's success, precisely because, you know, the whole kind of goal of the company was to replace a really frustrating experience with taxi with a service that's just ultimately reliable - right? And the only way to do that, the only way to be able to get basically everyone who lives in a dense part of a city a car within five minutes was to do that through dynamic pricing, through giving drivers a very, very strong incentive to want to get to the places where they're needed the most, and also to get riders who could afford to wait a little longer - say they're at a bar - to say, well, you know, if it's more expensive to take a ride right now, go ahead and relax and sit back. If you can wait 15 minutes, the drink's on the person who's got to go now.

VEDANTAM: Right (laughter). The interesting thing, Keith, is that when I think about my own behavior, when Uber tells me that the price is now 1.8 times the regular price, I notice that and I factor that in, and there's a part of me that feels it's a little unfair. When I'm waiting for just a taxicab and now the taxicab doesn't show up, I don't actually think of someone whom I can blame or someone whom I hold responsible, even though it actually has a bigger effect on me that now I actually have to wait two hours or I can't get a cab at all.

CHEN: Yeah, that's absolutely right. And, you know, this basic question of how psychologically painful kind of the experience of paying a price is is something that I worry about every day, especially because I actually think that it's one of the reasons that we've grown so fast. It's one of the reasons that we've been able to displace taxi so quickly is because in a taxi, you sit in the car and when you're trapped in traffic, you literally watch your money ticking away like in front of you.


CHEN: You're just kind of forced to watch it. There's nothing else to see except this.

VEDANTAM: It's hypnotic, absolutely.

CHEN: It's hypnotic and it's the worst possible psychological experience - you know? Taxis and gas pumps - right? - are the two places where you just watch your money tick away. And the typical Uber experience, you know, you just hit a button, get in the car and if it's not surging, you don't even need to know what you paid until tomorrow morning if you want to open the email. And if you kind of trust that Uber's the kind of cheapest possible of option, you don't even need to look.

VEDANTAM: So you found that psychology plays a role in how surge-pricing works because there are places where it works in the way that a traditional economist would predict it would work and there are places where it breaks down.

CHEN: Definitely. So just like traditional economics would predict, as you raise the price, you know, surge-pricing starts to dampen demand. You know, when you go from kind of a surge of 1X, meaning no surge, to 1.2, you actually see a very, very large drop in demand - OK? And that initial drop in demand - actually early on when we first started surge-pricing at Uber, going from 1X to 1.2X, you would see a 27 percentage points drop in people who would request.

After sometime though, like both after a surge has been in the city for a while and after people have gotten a little used to it, that drops to 7 percent. So people start getting used to - it's not such an alien experience anymore. They may not love you - right? - at the company because of it, but they're not quite as kind of put off by it as normal. And then as you tick up the price further and further, you see further and further drops in demand - so 1.2, 1.3, 1.4. You know, people like surge less and less because they understand that they're paying more.

The surprising thing is there is a very, very strong round number effect which we detect. So when you go from 1.9 to 2.0, you see six times larger of a drop in demand than you saw from going from 1.8 to 1.9. So the amount more that you're paying for the trip is the same between those two steps, but 2.0 just feels viscerally larger to people - right? It just seems a lot like - it's very easy to understand. Everyone understands I'm paying twice as much for this trip as I would have.

VEDANTAM: I saw a paper that came out on National Bureau of Economic Research a couple of months ago. This is by Matthew Backus, Tom Blake and Steven Tadelis where they looked at pricing on eBay. And they found something that sounds similar, which is they found that when you price things with round numbers, those things tend to sell faster, partly because people believe that sellers who price things at round numbers aren't really wedded to those prices...

CHEN: That's right.

VEDANTAM: ...That they basically put those prices on because they want to move something quickly.

CHEN: Yeah.

VEDANTAM: And therefore they're actually willing to negotiate with you because they actually don't care so much about what the actual prices is, whereas, when you pick a very specific price point...

CHEN: Yeah.

VEDANTAM: ...People say, this couch costs 74 dollars and 26 cents - some thought must have gone into this and I don't have much room to negotiate.

CHEN: (Laughter) And there is no back and forth. Where if somebody prices their couch at a hundred...


CHEN: ...You think, well, why don't I just counter with 50?


CHEN: And then the conversation gets starting from there - right?


CHEN: Actually, you know, we see exactly the same thing at Uber. And I think that's the main explanation for something really, really puzzling, and that is more people will take a ride at a surge multiplier of 2.1 than would take a ride and 2. So I described to you how between 1.9 and 2, a lot of people stopped taking rides. If anything, people take more rides at 2.1 than they did at 2.

VEDANTAM: When it's more expensive?

CHEN: When it's more expensive. It's as if they're telling you, I would rather pay you 2.1 times the normal price than I would 2. And I think just like your intuition on negotiations on eBay, that's exactly the same intuition I think that drives the behavior here. You know, people, when you tell someone, your trip is going to be two times more than it normally costs, they think, wow, that's capricious and unfair.

VEDANTAM: Someone just made that up.

CHEN: Somebody just made that up. Like, you know, they must have seen it was raining and just decided to mess with me - right? Whereas if you say, oh, you know, your trip is going to be 2.1 times more than it normally does - wow, you know, there must be some smart algorithm in the background here that's at work. It doesn't seem quite as unfair.

VEDANTAM: We had the behavioral economist Richard Thaler on the podcast some time ago. And one of the thinks he was talking about is why it's often hard to find a cab on a rainy evening. And his theory is that this had something to do with what he calls mental accounting, which is the cab driver has a number in his or her head about how much money he wants to make on a given day. So the cab driver says, you know, my expenses are going to be a hundred bucks a day. I want to make a hundred dollars over that. And so when I hit 200, I go home. And on a rainy day, demand is higher, so you hit 200 faster.

CHEN: Yeah.

VEDANTAM: And so the cab driver goes home. And so you end up with fewer cabs with more demand.

CHEN: Exactly.

VEDANTAM: Now, you have done some work looking at dynamic pricing and surge pricing. And you're finding that actually that isn't the case with Uber driver. Uber drivers do not necessarily go home when it's actually a smart time to be driving.

CHEN: Yeah, exactly right. I find very different results than Dick does with New York City cab drivers. And I think I understand a little bit of the psychology as to why we're finding different things. All right, so exactly as you said. If you're a New York City cab driver, you know, you're going to get the same amount for every trip no matter what. It's a regulated fare. So when it's raining, the only thing that's really changing is you're just picking up more people - right? So in that world, it does feel very salient. You know, many of them are collecting cash. You can just see, you know, once there's a pile of 200 dollars in the front seat, I'm just going to call it a day - right?

Whereas, on Uber, you know, the main way that we incentivize drivers to move to the places that riders need them and to stay out a little longer if they can afford to is through surge pricing. And so I think that's - I think that's very, very salient. So if you're an Uber driver, and say you were planning to do about a two-hour shift this afternoon - you know, you're driving around but all of a sudden, unexpectedly, it starts to surge 2.1 times - all right? Like, every trip you're going to make, you know, (laughter) - I can't believe my mind just went to 2.1


CHEN: But, you know, you're an Uber driver, and it's surging, you're going to get twice as much...


CHEN: ...For every additional trip you do. What I actually see in our Uber data is that even compared to yourself a week ago in exactly this situation, Uber drivers will double, triple the length of the shift that they were planning to do if it's surging 2X, if it's surging 3X. They're just going to stay out because, well, they can make a lot of money right now. You know, they can take the whole weekend off if they put in another few hours today.


VEDANTAM: When you yourself use Uber as a rider, what is it you pay attention to? I mean, do you use your own trips as sort of research into how the company is working? You must at some level.

CHEN: Oh, my god, I do all of the time.


CHEN: And it frustrates me because I kind of see so many kind of psychological biases in my own life.

VEDANTAM: Like what?

CHEN: So, for example, partially because I think it's just one of our most exciting products, I take UberPool a lot. So UberPool is this service by which now up to three passengers can share the same Uber car. And, you know, when you're in the car, you know, you can just - somebody just kind of two blocks ahead just happens to be going, you know, along the exact same route that you are, you know, will just suddenly tell your Uber driver, pull over and, you know, take in this new person. And then because that we can get people to share rides and get more people into seats, we can like reduce the price. And on average, like, UberPool can save up to 50 percent for most people's rides.

VEDANTAM: Not to mention saving on gas and emissions and all the environmental issues.

CHEN: Not to mention saving on gas, emissions and increases driver pay because, you know, literally now drivers can be constantly utilized. They can be constantly making money and they don't have any of that downtime of driving to someone or sitting idle somewhere. But a big part of making UberPool work is minimizing the amount of psychological frustration that people have with that experience.

And so, for example, I find for myself that initially when we were writing those kind of UberPool algorithms, we took a very rational view. You know, we want to pass people the most amount of savings that we can and inconvenience them as little as possible, with inconvenience usually being kind of just really, really focused on how much time is it going to get you from your point A to your point B?

One of the things that we have started to discovered is that - and that I feel very viscerally when I'm in an UberPool - is that there's something really, really psychologically painful about going backwards - right? So even if this is an amazing match for you - right? - forcing your car to kind of, you know, make three right turns and circle around the block and for a short period drive backwards - right? - drive away from your destination...


CHEN: ...Is just like three times more painful than you would have expected just from the added time.


CHEN: So, you know, so we try and avoid that as much as possible. And that's something (unintelligible).

VEDANTAM: You know, I feel there have been times that I miss an exit on the freeway, and you know that you have to wait, go to the next exit and then turn around and come back. And every inch of the way, as you're going to the next exit, you're reminding yourself, I'm going to have to retrace this inch. I'm going to have to retrace this inch. I made a mistake. And you really - it's true, you really beat up on yourself when you feel like you're doing something that's taking you in the wrong direction.

CHEN: Yeah, I'll do crazy things, like I realized the other day that - you know, there's two airports that I can fly out of. I have to fly south and there's two airports I can fly out of. One of them is north of me and one of them is south of me. I can't bring myself to book a flight out at the airport that's north of me because I feel like I'll spend the whole drive, like, going in the wrong direction.

VEDANTAM: (Laughter). One of the things I wanted to talk to you about this Uber is collecting massive amounts of information on what people are doing. And I think a lot of people don't quite realize how powerful this information is. I remember doing a story some months ago looking at some social science research, analyzing how people were using cell phones in a poor country and how the way people use their cell phones and whether they kept their cell phones topped off - so this is a country where you sort of prepay your cell phone - whether you keep your cell phone prepaid. And whether you have a lot of incoming calls or you have a lot of outgoing calls, how wide your network is - all these things can predict, with a remarkable degree of accuracy, your credit worthiness.

And so if a bank sort of just looks at your cell phone usage data, it can make remarkably accurate predictions about whether you are likely to repay a loan or not repay a loan. And, I mean, who would think that just the way that you're using your cell phone could tell a bank that, you know, they should give you a loan to start a new business? And I feel like Uber has sort of similar access to vast amounts of data. I mean, there have been reports for example of, you know, so Uber broadly knows where I live and where I work because I'm often taking cabs back and forth between those two locations. But let's say one day I leave work, and instead of going home I go to another location, which is known to be the address of a bar.

CHEN: (Laughter).

VEDANTAM: And then a few hours later I go to another location, which is not my home address.

CHEN: Sure.

VEDANTAM: And then I come to work the next morning, you know, you can draw the conclusion of what I was doing that evening. And it feels like you actually know more about people's lives than perhaps they realize they are letting you know.

CHEN: We do have access to a tremendous amount of data. And because of that we have kind of a privacy officer, you know, within the firm. Because of that, kind of even as an employee of the firm, I have to be very, very careful about what kinds of queries and what I look at in people's data. Yeah, precisely because this is people's lives. And you're right that, you know, we have to take very seriously this responsibility that we're becoming a big part of how people move around the world. And we just want to be very careful with that.

VEDANTAM: Have you ever been concerned about the way you are using these services that might reveal things about you? I mean, as someone who sees things from the point of view of the institution of the company and knows how powerful this information is, has it changed your own behavior and how you interact with any number of these sites?

CHEN: Well, now that you're talking about it, I'm getting even more worried, but...


CHEN: But, you know, I - and this might be naive, but I have often thought to myself that - you know, so my experience inside of large companies that have access to these huge kind of treasure troves of data is that, you know, you almost always just look at these like broad, broad aggregates. And I guess I've always just taken comfort in the fact that I'm boring enough of a person that no one would ever (laughter) - I mean, it's almost like - you know, so, for example, like, some people I know, like my sister, kind of shreds all of her credit card statements and kind of does everything before she tosses paper in the trash. And I always just throw everything in the trash because I figure, like, I just don't feel important enough for, like, someone to rummage through my trash.

VEDANTAM: (Laughter).

CHEN: We do though, you know, in the Uber data, see a lot of really, really interesting patterns. So, for example, a data scientist named Peter at Uber discovered somewhat accidentally this really, really kind of interesting fact. And that is one of the strongest predictors of whether or not you are going to be sensitive to surge - in other words, whether or not you are going to kind of say, oh, 2.2, 2.3, I'll give it a 10 to 15 minutes to see if surge goes away - is how much battery you have left on your cell phone.

VEDANTAM: Oh, that's fascinating. Of course, yeah.

CHEN: Yeah, like when your cell phone is like down to like below 5 percent battery and that little icon on the iPhone turns red, you know, then people start saying, well, I better get home, like, because I don't quite know how I'm going to get home otherwise. And we absolutely don't use that to kind of like push you a higher surge price, but it's an interesting kind of psychological fact of human behavior.

VEDANTAM: I'm talking with Keith Chen. He's a behavioral economist at UCLA. And heads up economic research at Uber. When we come back, I'm going to ask Keith about some of his earlier work, which explores the origin of some very interesting biases. Stay with us.


VEDANTAM: Keith, in your life before Uber, you conducted different experiments into the origins of various human biases, including some issues that often come up in behavioral economics. Some of your most fascinating early work was with monkeys. And I understand to conduct these experiments, you first trained the monkeys to become economic actors.

CHEN: (Laughter).

VEDANTAM: How did you go about doing that?

CHEN: Oh my gosh, this feels like a lifetime ago. But with a bunch of colleagues at Yale University, we set out to answer this kind of somewhat ill-defined question but one that we were kind of obsessed with, which was, if monkeys were taught to use money - well, one, could monkeys be taught to use money?

And two, if monkeys could be taught to use money, would they behave in the same way that humans do in all of the kind of psychologically rich ways that we interact with kind of prices and think about wealth and think about how we spend our own money? So basically what we did was, you know, we just - we had a small colony of Capuchin monkeys that were living with us at Yale. I also did some work with a colony of Tamarin monkeys back at Harvard. And my Yale students were thrilled to hear that the Yale monkeys were much smarter than the Harvard monkeys.

VEDANTAM: (Laughter).

CHEN: I didn't tell them it was because it was a different species.


CHEN: They attributed it to the school.

VEDANTAM: Right (laughter).

CHEN: But basically what you do to teach a monkey to use money is you hire a bunch of Yale undergrads to basically just live with the monkeys for a long time. So, you know, these Yale undergraduates were typically psychology majors. They were studying the monkeys for various other things. You know, the monkeys live in a big, spacious, comfortable habitat. You know, they typically ignore the kind of humans that are moving around. But you'd have a Yale undergraduate every now and then just drop a coin on the floor - OK? And we had chose these kind of large, metal washers to kind of stand in for a coin.

Now, a monkey thinks that's fascinating, runs over, grabs the coin, kind of chews on it, like, you know, bangs it on the floor, every now and then would throw it around, kind of almost a little dangerously, but would eventually kind of lose interest in this metal disc. Then you'd have the Yale undergraduates stand there and stare at the monkey with an open hand outstretched - OK? - and just stare at that monkey uncomfortably, just kind of setting this uncomfortable situation. And every now and then, the monkey would actually pick up the coin and just put it in the undergraduates' hand, like give it back to the student. And what we did was we trained the students to say, why thank you in a really exaggerated tones and then like hand the monkey a piece of food - OK? - so like a little apple slice. One undergraduate came to be known to the monkeys as like the apple undergraduate. If you gave that person a coin, they would always, like, hand you an apple slice. Another person was the pineapple undergraduate. And another person was the orange slice undergraduate. Now, what's amazing is you do this for about six months - all right? - nonstop for about six months.

And eventually you start to realize that the monkeys understand that this is Fiat money - right? Now, what does that mean? Well, what that means is, you know, the monkeys had been very familiar with, like, basic ideas, like there's a lever on the wall. If I pull this lever, an apple slice falls from the ceiling. But money is something fundamentally different. When I find this coin - all right? - it's not just an apple lever. It's actually a choice between an apple lever, an orange lever, and a pineapple lever because I can take this coin, I can carry it around with me, and I can wait until the - if I feel like having an apple, I've got to run over to the apple person and I can spend it there - right?

Money is kind of fungible across different kinds of food that I can purchase. So we started to see that. We saw that monkeys started to use these with each other, and to save them, to kind of hide them from other monkeys and then to make very, very rational decisions, rational-looking decisions. When the price of apple doubled, you know, when that apple undergraduate started only giving one piece of apple, not two, when you handed him a coin, you know, demand for apples went down and demand for oranges and for pineapples went up.

VEDANTAM: How did the monkeys protect their money?

CHEN: A fascinating component of the monkeys starting to understand money was that they displayed signs that they realized not only that they understood the value of this disc but that they understood other monkeys recognized the value of this disc. So, for example, you know, early on when the money that we were using was these physical disks, it's a little bit hard to shield from other monkeys - right? And you don't want to be kind of carrying all of these things around.

So you would see monkeys, like, hide the discs, like, you know, over in the corner, under a pile of, like, wood shavings - right? They'd kind of hide the discs. Later, actually, we taught these monkeys to use touchpads. And so, like, then kind of in some sense, like, monkeys learned, you know, to use currency as if it's just kind of an ATM, as if it just gets, like, Venmoed or transferred to other players, to the apple guy...

VEDANTAM: So this is like an Apple wallet basically?

CHEN: Yeah, basically. They are ahead of us on this dimension. You know, they are a completely cashless economy at this point.

VEDANTAM: You eventually got to the point where you would also introduce to the monkeys the idea that sometimes when they would give a certain amount of money to an experimenter, the experimenter might give the monkey three things. And sometimes the experimenter might give the monkey one thing. So in other words, it was unpredictable what the reward was going to be. You taught the monkeys to gamble.

CHEN: Yeah, that's basically exactly what we did. So we introduced them to two new undergraduates. So one undergraduate would always approach a monkey with three pieces of apple in their outstretched hand. Let's call this undergraduate Adam - OK? - would show three, and either give over all three or would it take two back and would only give one. Then we introduced him to another undergraduate, Ben, - all right? - who always showed one - OK? But if you gave Ben a coin, Ben would half the time hand over just that one, half the time would add two apple pieces to his hand and hand over three - all right? So now the fascinating thing is both Adam and Ben are presenting you exactly the same deal. They are a 50-50 gamble between three apple pieces and one apple piece - right? And you can give the monkeys a lot of experience trading only with Adam and a lot of experience training only with Ben. So they kind of get that this is a 50-50 gamble.

The interesting thing is what the monkeys showed us through their preferences between Adam and Ben is that they experience something just like humans do. And that's this very powerful psychological force called loss aversion - all right? And that's the idea that it's more than twice as painful to experience a loss than it is to experience a similar sized gain - right? Now, what does that have to do with Adam and Ben? Well, Adam half the time gives you, in some sense, what he's shown - right? - the three apple pieces. But half the time he delivers you a loss. He takes away two and then hands over only one - all right? So half the time, he delivers you a loss of two. Ben half the time just hands you the one that he is showing you and half the time delivers you a gain. He gives - he puts an extra two pieces of apple there and hands you an extra two apple pieces.

What we find is that when given the choice between Adam and Ben, monkeys vastly prefer Ben, - all right? - the guy who initially only shows one apple piece but half the time gives you a gain, than they preferred Adam, who initially shows you three apple pieces and then half the time delivers you a loss. They - you know, 6 to 1 preferred trading with Ben to Adam. And that's fascinating because that cuts against their very, very core instinct, which is, well, if I have got a coin, why don't I go trade it with the guy who is showing me three instead of the guy who is showing me one - right? Like every kind of fiber in their body tells them that they should be going for more food, not less. And yet because of loss aversion and because they actually feel this very viscerally in the same way that people do, they actually prefer the guy who promises less but delivers more.

VEDANTAM: I have to say that I am thinking about what I would do in that situation. And I have to say I'm clearly no smarter than a monkey because I would definitely prefer to trade with Ben because you are getting something that seems like it's a surprise. It's a gift. It's like - it's wonderful. It's unexpected. It's happy. And at the worst it's only going to be what he's promised you in the first place. But with Adam, he's taking away something that you thought was yours.

CHEN: Yeah, yeah, absolutely. And interestingly, we see in our Uber data this loss aversion at work as well. And you can even just feel it psychologically. Like when you are a rider and, you know, surge pricing hits, like that's a loss - right? And that feels very, very bad. We have often been asked, well, why didn't you frame surge pricing as discounts instead of as - instead of surcharges? Why isn't Uber's standard price twice as high as it is and then most of the time you're getting a discount - right? Why not kind of frame it that way?

VEDANTAM: Because behavioral economics would predict that you would actually make people much happier if you did it that way.

CHEN: Well, behavioral economics would predict that you would make riders happier if you did that. I mean, the critical thing to notice is that we are a two-sided market. That move, which would make riders happier, would also make drivers feel less well. So, I mean, so we had thought for a while, why not just frame Uber's pricing as how much cheaper we are than a taxi? Because in many major cities in the United States, we're up to 60 percent cheaper than taxi fares when we're not surging.

And, you know, what that means is that, you know, you can surge like 2.1 and still be coming basically out even if you had instead just taken a taxi. But even though because Uber drivers are in such an efficient system, they constantly have a paying rider in the back of the car, they are actually making more than they would have if they had been working as a taxi driver, framing this to them - oh, you're making 60 percent less per mile than you would if you were driving a taxi, that's - that would be a cost of this pricing system.


VEDANTAM: Keith Chen is a behavioral economist at UCLA. He heads up economic research at Uber. Keith Chen, I want to thank you for joining me today on HIDDEN BRAIN.

CHEN: Shankar, it's been incredibly fun. Thank you for having me on.

VEDANTAM: The HIDDEN BRAIN podcast is produced by Kara McGuirk-Alison, Maggie Penman and Max Nesterak. You can follow us on Facebook, Twitter and Instagram and follow my stories on your local public radio station. If you like this episode, consider giving us a review on iTunes. It will help other people find the podcast. I'm Shankar Vedantam, and this is NPR.

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