It's one thing falling in love with a house,
picturing yourself moving in and calling it home,
and quite another navigating the world of price negotiating,
mortgage lenders, and finding the budget that works best for you.
An agent who's a Realtor can make understanding that world easier.
Realtors have the expertise, access to proprietary data,
and tools to help you get from imagining living somewhere to actually doing it.
That's the kind of help we can provide.
Because that's who we are.
Realtors are members of the National Association of Realtors.
Reese's Peanut Butter Cups are the greatest.
But let me play devil's advocate here.
Let's see. So, no, that's a good thing.
That's definitely not a problem.
Reese's, you did it. You stumped this charming devil.
We humans seem to like each other, except when we're hating each other.
I'm Angela Duckworth.
I'm Steven Dubner.
And you're listening to No Stupid Questions.
Today on the show, should we replace human umpires with robots?
If the purpose here is to get it right,
then why on earth would we even want to have the humans around for that?
Steven, I have a question for you that comes from watching my husband.
Are you ready for this one?
I'm not sure. No, you put it that way.
First of all, he's fully clothed in these observations,
and he's seated in our living room and screaming.
So now he's actually probably standing up, yelling at the television during the World Series,
which, you know, is now in the rearview mirror, unfortunately.
Is this because he's a Philadelphia sports fan?
Because they are the worst, the yellingest, the loudest.
He doesn't strike me as that.
I mean, he's a sports fan and he's in Philly.
And he's rooting for Philadelphia teams. We do have a reputation.
Is that why he's yelling, though? The Phillies did lose the World Series this year.
He yells in other games, too.
Basketball games, for example.
And what he's yelling at is the umpire in the baseball game or the ref in the basketball game.
And what he asked me the other day was, like, why do we have these fallible human beings
in charge of such consequential decisions when we live in the era of artificial intelligence?
And I mean, why do we have human umpires at all? I guess that is the question.
So that is an interesting topic for sure.
There are a lot of directions we go here, but, you know, let's start with the baseball umpire
question. And I think before we even answer why we still have human umpires, we should probably
just explain what human umpires do. So in baseball, there are four umpires in a regular game,
although for the playoffs, they use six. They put two more in the outfield.
As if to say, well, four isn't really like when it's important we need more, which is kind of
a whole other crazy thing. But typically, there are four, one behind home plate, then a first
base, second base and third base umpire. But it's really when people talk about the ump,
they're talking about the home plate umpire.
Yeah, it's the only one I see. I didn't even know there were three other ones.
They're kind of blending in out there. But the home plate umpire is very visible because he is
right there in the action. He's crouching behind the catcher. So for someone who doesn't know
baseball, here's the way it works.
Which would be me. So this is good.
So there's a pitcher. You know what the pitcher does?
He's pitching the ball.
Stands on a mound. He's 60 feet, six inches away, which sounds like a lot. But when they're
throwing 95, 100 miles an hour, it's really not a lot. Then there's the batter. And then there's
a catcher behind the batter. Then right behind the catcher is the umpire. Then there's this
rectangle that is supposed to represent the strike zone. It's an imaginary rectangle and
it extends from, I believe it's supposed to be from the armpits of the batter down to the knees
of the batter. That's the vertical. And then the horizontal is supposed to cover the width of home
plate, which is this five sided slab of plastic that's in the ground. So you can imagine that
the umpire is crouching behind the catcher, imagining this rectangle, armpit to knee,
left sided plate, right sided plate. And if a pitch crosses the plate within that frame,
it's supposed to be a strike. And if it crosses a plate outside of that, it's supposed to be a ball.
Now, even crossing the plate is tricky because the ball is moving. It's usually dropping
and it can be going to the left or the right. And so to be really precise, as you can imagine,
can be really hard. I should also say if a batter swings at the pitch and misses,
then it's a strike. Regardless if it's in the strike zone. Exactly right. And if they foul
off a pitch that counts as a strike and so on. I do know you have three strikes before you're out
and four balls before you get walked. Excellent. So you can imagine that the batter cares a lot
about whether a pitch that he doesn't swing at is called a ball or a strike. And the pitcher
also cares, the catcher cares, the team cares, the fans care. And you are right. There's a ton
of research showing umpires are quite fallible. So we did a piece on Freakonomics Radio a few
years ago about what's called the gambler's fallacy. You familiar with that phenomenon?
You know, I've heard that defined in different ways. So what is the Steven Dubner definition?
Okay, so let me take my shot at defining it. This is based on really nice research done
by Toby Moskowitz, who's an economist now at Yale. He coauthored a paper with Daniel Chen
and Kelly Shue. It was called decision making under the gambler's fallacy evidence from asylum
judges, loan officers, and baseball umpires. So if you think about those three categories of people,
judges, loan officers, and umpires, they all have the authority to basically say yes or no.
And they're like all ultimate authorities. You know, I think easily appeal, I guess you could
try, but generally their word is their final decision. Yeah. And so the gambler's fallacy
has to do with the way that we mistake how probability really works. Many of us,
even really smart people, we find patterns that don't exist or we look for patterns where they
shouldn't exist. Let's say you're at the roulette table and you're playing red and there are three
spins in a row that come up black. The fourth spin, is it any more likely to come up red than
the previous ones? No, it's a totally independent variable, but we like to tell ourselves that,
well, there were three blacks, so the next one is more likely to be red. Right. And so we are
constantly miscalculating probabilities in that way. And the way that the gambler's fallacy would
apply in the case of like a baseball umpire or an asylum judge or a loan officer would be
that our minds seem to want to toggle a little bit. We don't want to have unnatural patterns.
And so what they found in their research is that a judge who had granted asylum to let's say
two asylum seekers in a row would be more likely to reject the next one, even though the evidence
might have been in favor. And the same for loan officers and the same for baseball umpires. In
other words, if there are two strikes called in a row and the third pitch is pretty close and maybe
even in the strike zone, there's something in the human mind that makes us a little bit reluctant
to create these patterns that don't make sense to us. And so there are two problems then with the
human umpire. Number one is they are susceptible to the gambler's fallacy, but they're also just
not that good. And when I say not that good, they're way better than you or I would be.
Right. They're experts.
But they're much worse than a computer would be. So what the researchers did is they looked at
thousands and thousands, maybe hundreds of thousands of pitches over time. They looked at
all these pitches where the batter didn't swing. And they found that on the obvious balls and the
obvious strikes, the umpires were basically 100% correct. If the pitch is right down the middle
and the batter doesn't swing and they call it a strike, they're almost always right on those.
But you don't even need an umpire hardly.
Exactly. But here we go. This is Toby Moskowitz. He's saying that on pitches that are just outside
the strike zone, they're definitely balls, but they're close on those pitches. He says umpires
only get those right about what percent would you say, Angie? What would you guess?
Oh, gosh. And this is comparing what the umpire says in real time with like careful review
afterwards. Is that right? Exactly. And these are pitches that are just outside the strike zone.
Um, I don't know. I'm going to give them like 90 to 95% because they're experts. It's all they do.
That's a very, very, very nice and generous assessment. But the actual number is 64%.
Wow. They get a D. I was going to give them an A.
So their error rate is 36%. That's shocking.
But I will say that in baseball, there are enough people who are as frustrated
as Jason that there's been a lot of movement toward automating it. And in fact,
there are these kind of robo-umps.
Are there really?
There are. I haven't seen any of these in real life yet, but I will tell you they have worked
their way up through the minor leagues in baseball. And it's estimated that they may
come to major league baseball as early as 2024.
It's not like an actual, you know, robot like Rosie from the Jetsons.
That's a good question. From what I know, it could go either way or a variety of ways.
In other words, you could actually have what looks like a robot out there making the call,
but it would basically be, you know, a series of cameras or a radar or whatever it is.
But one way that I've read that it may be done, which would make it perhaps more acceptable
to the very tradition bound game of baseball would be that there still would be a human umpire
who would have the assistance in real time of the cameras and the detectors and basically
have an earpiece in so that he would get immediately confirmation of what the right
call is. Now you might say, well, that seems really stupid. Why do you want to have the
humans still out there if the human is using the computer information?
But this gets us into the notion of how comfortable humans are with computers or artificial
intelligence or machine learning in all these different aspects of our lives that we're used
to doing ourselves. This could go from medical diagnosis to cars and autonomous travel to even
having a robo up. So I think then we get into your territory of psychology, which is thinking
about how people feel about relying on technology for things that they're used to doing for
themselves. So what can you tell us about that? Well, I started thinking about this in a serious
way when a graduate student of mine named Benjamin Lira got very interested in artificial
intelligence approaches to analyzing data. And we were working on this data set. The data set was
huge and it was college admissions data. And it just dawned on both of us quite early that using
a sophisticated AI algorithm or even a really primitive algorithm would probably be better
in many cases than relying on one idiosyncratic, sleep-deprived human being. This is an idea that
goes back. I mean, if you look at Danny Kahneman's first job when he was in the Israeli army, this is
decades and decades ago. And his task was, you know, help us figure out who to promote in the
Israeli army. He immediately recognized that the problem was that human beings were in charge of
these promotions. Yeah. And they were doing what human beings do, which is kind of pulling out of
the air, the things that they cared about on that particular day, never writing down why they made
that decision, never justifying it, and then having no systematic approach across the whole army.
And maybe telling yourself stories ex post about why that was the right decision when they were
really just stories. He could see immediately, even if he didn't have the phrase confirmation bias,
that once you had made a decision that, yep, that soldier deserves to be promoted,
that you would then be searching for evidence to confirm that your original intuition was true.
And his answer to this, his antidote, if you will, didn't require neural networks, deep learning,
computers, or anything else. He just said, hey, are there criteria that we can write down on a
piece of paper, maybe say half a dozen things that we think somebody who deserves a promotion
ought to exemplify? And once you write that down, you know, conscientious, takes feedback well,
empathic, you know, strategic, whatever it is, you then spend just a few more moments saying,
well, what does that look like? What would that look like to have? What would that look like
to lack? And just that little exercise of having a systematic approach of what we're looking for,
what it exactly looks like, is really what we would call today an algorithm, right? It's a
formula as opposed to leaving it to the thoughts that are erasing through your mind at that
particular moment. And it turns out that that was not an easy sell to the Israeli army. It's like,
wait, what? You want to have a systematic, rigid approach to promotion? Well, what about
human judgment? What about all the intangibles? What about all the things that can't be articulated?
But, you know, Danny Kahneman's pretty persuasive, and he was able to convince the Israeli army to
adopt what you could say is like the most primitive of algorithms. So this idea that we
have to convince people to use algorithms is an idea that's been around for a while,
the algorithm aversion that people speak of today is a kind of fundamental distrust and dislike
of robots or computers taking the place of human beings when making important decisions.
I think there are a lot of dimensions that people may dislike about that. I mean,
part of it is we humans seem to like each other, except when we're hating each other.
I was going to say, I don't know. We also seem capable of hate, but we have a fondness for each
other that I think we don't have for, you know, our laptop. There is a kind of emotion that we
reserve for living things and especially other people. I think it's already well established
that computers are much better at reading mammograms than the humans who interpret them.
Just think about the sheer volume. You can program a computer with millions
of mammograms that show a positive result and millions that show a negative result,
and no human could do anything close to that. Right. A radiologist is only going to see how
many MRIs in their career, but if you're Google, you can see all of them.
It's also really hard to update the priors and to keep current the learning on a human radiologist,
as much as we have, you know, adult licensing and re-licensing and so on. Computers just learn
that kind of thing. Continuing medical education credits.
Yeah. So Angela, I would love to hear from our listeners about an area
in their lives where they are eager to accept more automation.
Or which area you hate the idea.
Sure. Make a voice memo. Don't make it too long. Tell us your name,
where you live, stuff like that. Do it in a nice quiet place. A lot of listeners
get so inspired by this callout for voice memos while, for instance, running or what sounds like
using a hacksaw to cut up an old water boiler. There's a lot of noise going on.
So honestly, a little bit of quiet goes a long way in the voice memo department,
and I would love to hear what you all have to say.
Still to come on No Stupid Questions, Steven and Angela discuss how much control
they're actually willing to hand over to machines.
I, Steven, am in fact AI 1743 XT squared Delta Steven,
which is the avatar that Steven Dubner has programmed to have this conversation with you.
As school messes return, rest easy. Clorox has got your back to school.
Oh no.
I'm back. That messy science experiment. And I brought baking soda, cornstarch.
And you're due tomorrow, right? Well, at least I stocked up on Clorox disinfecting wipes.
Because you're not messing up my kitchen counters like last time.
Ooh, smart. Got any food coloring?
And off to the store we go.
Stock up on Clorox disinfecting wipes that clean three times better.
The wet paper towels on kitchen grease and soap scum use is directed.
Is this house a good price compared to others in the area? Are prices going up or down? If I don't
make an offer right this very moment, will I miss my chance? These are just some of the questions a
home buyer might ask. And these are the sorts of questions an agent who is a Realtor can help
answer. Because Realtors have the expertise, data, and access to specialty training to help you
navigate the process of buying a home. They provide support, guidance, and have your back
every step of the way. That's what Realtors do. Because that's who we are. Realtors are members
of the National Association of Realtors. Reese's Peanut Butter Cups are the greatest,
but let me play devil's advocate here. Let's see. So, no, that's a good thing.
That's definitely not a problem. Reese's, you did it. You stumped this charming devil.
Freakonomics Radio is sponsored by Lululemon. Everyone has those moments where they say,
not today, when it comes to fitness. Lululemon restorative gear is made for those days. Days
where you want to max out your rest, not your reps. Lululemon's new campaign features OBJ and
DK Metcalf in their buttery soft, breathable, restorative wear designed to keep up or kick
back with you. Visit Lululemon.com for everything you need to bring it tomorrow. Rest day is the
best day. Now, back to Stephen and Angela's conversation about whether robots make better
umpires than human beings. Going back to baseball for a moment, Major League Baseball has recently
been using video replays to look at close calls. And, you know, there are video replays now in
professional soccer, in the NFL. In basketball, too, right? In basketball, yes. In tennis,
we should say the chair umpire. As far as I know, most outcalls are made by this computerized
system called the Hawkeye system. Wait, what's the person sitting in the, you know, like that
little lifeguard stand? What are they doing? They are actually a lifeguard just in case the
tennis court gets flooded. They want to make sure everybody's going to get out. OK, they are the
chair umpire and, you know, there's some umpiring to do and there are some other calls to be made.
But tennis has certainly embraced the technology in that way. So baseball fairly recently began
using video replays and they found that in the cases where the calls are challenged or looked
at again, they're overturned nearly half the time. That goes back to that statistic, that sort
of shocking D grade. Yeah. And these are for other calls. These are not just balls and strikes. These
are for you're safe or you're out, you know, within play or out of bounds and so on. And so
to me, what's shocking about that is you might naturally want to say, well, wait a minute,
if the purpose here is to get it right, then why on earth would we even want to have the humans
around for that? On the other hand, I think there's a lot which helps us understand this
algorithm aversion. Joe Torrey, who was a longtime baseball player and manager, I think he now works
for the league. He's against the robo-umps. His version is it's an imperfect game and has always
felt perfect to me. That's so beautiful. So to me, when I think this through, I get both sides
very, very much. I think you could easily remove the home plate umpire from baseball, maybe the
