MALE SPEAKER: Hi, everyone.
Thanks for coming
out this afternoon.
So we have two special
speakers today as you can see,
one guest and one
fellow Googler.
Hal Varian immediately
to my right
is Google's chief economist
and has worked with Google
since 2002.
Hal has contributed
to Google projects
on auction design,
econometric analysis, finance,
corporate strategy,
and public policy.
He's also an Emeritus Professor
in Business and Economics
and Information Management at
the University of California
Berkeley.
That's also where
he earned his PhD.
Before coming to
Google, Hal taught
at some of the world's most
prestigious universities,
including the University
of Michigan, MIT, Oxford,
Stanford, and of
course, Berkeley.
Alvin Roth, our special guest,
is the Craig and Susan McCaw
Professor of Economics
at Stanford University
and the Gund
Professor of Economics
and Business Administration
Emeritus at Harvard.
Professor Roth is an
expert in game theory,
experimental economics,
and market design.
In 2012, he and Lloyd S. Shapley
were awarded the Nobel Memorial
Prize in Economics for
their contributions
to stable allocations
and market design.
And their work
isn't just theory.
It's been put to real
world use in areas ranging
from health care to education
as Professor Roth discusses
in his new book, which we're
here to talk about today, "Who
Gets What-- and Why."
So without further ado,
please give a warm welcome
to Dr. Hal Varian
and Dr. Alvin Roth.
HAL VARIAN: Welcome to
the Hal and Al show.
By the way, I didn't tell you,
but since Jon Stewart's retired
there is an opening.
And I thought maybe we could
do a little rehearsal--
ALVIN ROTH: Good idea.
HAL VARIAN: --for that position.
And one of us will
be the interviewer.
And one of us will be the guest.
ALVIN ROTH: We can
figure out afterwards.
HAL VARIAN: We'll let the
audience vote afterwards
on which role is appropriate.
But as you just heard,
Al is here today
because he wants to talk about
his new book, "Who Gets What--
and Why."
And I have to say, it's
a pretty broad title.
Right?
You could say, that's all
of economics should exist
[INAUDIBLE].
So tell me what's
unique about your book.
ALVIN ROTH: Well,
it has a subtitle,
which is "The New Economics of
Matchmaking and Market Design."
And so I try to talk about
matching markets and markets
more broadly.
A little bit, I think of it as a
field guide the markets the way
you'd use a field
guide to birds to let
you know that there are more
than red breasted robins
in the world.
I think that outside of
Silicon Valley people
have a very different
view of markets
than we have in the Valley.
Because of course, markets
are ancient human artifacts
probably older than agriculture.
But often, we tend to
think of markets the way
we think of languages, which
are also human artifacts,
but which we can't
intervene in very much.
You know, we speak English.
But we speak it the way we
received it pretty much.
But of course, companies
like Google make markets.
There are a lot of
market making companies.
And there are a lot of
markets in the world.
So markets, unlike
languages, have proprietors,
have interested active
groups of users,
and are susceptible to design.
We can change markets
when they're not
working the way we want.
And we can invent
new ones when we
see markets that are missing.
HAL VARIAN: And just to fill
in the little for the audience
is this idea that you could
design economic mechanisms,
like markets, and like
matching and other processes.
I guess Lyle Hurwitz
was really the guy who
first promulgated that,
at least in current times.
ALVIN ROTH: I
think that's right.
Although, my guess is the
guy who first promulgated
it was trading in stone tools
back before the beginning.
HAL VARIAN: Yes.
And a very interesting
development
in the last 10 or 15 years
has been the development
of algorithmic
mechanism design, which
is a way to combine
computer science
considerations like
computability and so
on with the economic
considerations which
are incentives.
And this has actually, I think,
been very, very stimulating
to both fields.
And as an economist, you focused
a lot on the incentive issues.
But have you also dealt with the
computational side of things?
Or is that--
ALVIN ROTH: Oh, no, no.
I have.
One of the chapters in the book
is devoted to kidney exchange.
And as that market
has developed,
we're looking more and more
at long chains and things
that turn out to
be computationally
difficult problems.
So they're incomplete problems.
They are also just hard,
even though solvable,
because you're dealing
with integer programs that
have more constraints
than you can write down.
So you have to generate
them on the fly.
And when you try calling
things like CPLEX,
you run into memory problems.
So you have to do column
generation or a row generation.
So yes, we think a lot about
the computational problems that
arise in organizing some of
the markets we deal with.
HAL VARIAN: Yeah.
It's been a very
stimulating field
for both sets of researchers.
In fact, there's a conference,
a whole fall specialization
at the Math Sciences Research
Institute at Berkeley,
which is devoted to
exactly this topic,
getting the computer
engineers, computer sciences,
together along with economist
to study a lot of these markets,
both the theory of what can be
done and what can be achieved
and the practice of actually
implementing some of the stuff.
ALVIN ROTH: You know, again
I hardly have to say here,
but as markets have moved
more and more to the internet,
the problem just to
implementing them
often falls to computer
engineers or various sorts.
And so there's going to be
natural, inevitable contact
between people who think of how
the participants in the market
will use it and
the engineers who
think how to make it
happen on the internet.
HAL VARIAN: So how did
you get into this area?
When you started, there
really weren't many people.
I guess, it was the
original Gale, Shapley paper
the lot has subsequently
been built on.
But it wasn't something that
was in every curriculum the way
it is now.
ALVIN ROTH: Well, I
started by thinking
about particular markets.
And the first one that
I studied for many years
before I got an
opportunity to redesigned
it was the market for new
doctors in the United States.
And that market had a
lot of market failures
prior to the 1950s
when they invented
a centralized clearinghouse
that essentially anticipated
the paper that Gale and
Shapley wrote 10 years later.
That market worked
very well for reasons
that we now understand
much better.
But over the years,
it developed problems.
Because in the
1950s, when it began,
it was a sort of simple market.
Simple in part, because
just about every graduate
of an American medical
school in 1950 was a man.
But by 1970s, there were
10% women graduates.
And today, there are 50%.
And so medical graduates,
they're very busy.
And one of things they can
do is marry each other.
And they started looking for
two jobs, not just for one.
It turns out looking
for two jobs is
a theoretically and
computationally more difficult
problem than
looking for one job.
So matching married
couples to pairs of jobs
became the question
I was asked to help
when I was asked to direct a
redesign of that marketplace.
HAL VARIAN: The grand challenge.
ALVIN ROTH: It was a challenge.
HAL VARIAN: So why
don't you outline,
let's say, the Gale
Shapley example just
as a very simple case,
a starting problem,
so people can get an idea of
what this matching process
looks like.
And then maybe say a
word or two about how it
can be extended to other cases.
ALVIN ROTH: OK.
Well, Gale and Shapley
wrote a prescient paper
called "College Admissions and
the Stability of Marriage."
So it had a funny title.
And what they said is they said,
think of a problem of marriage
where-- they didn't quite put it
this way, but supposing you're
trying to organize a centralized
clearinghouse to marry people.
And you're not a
central planner.
You can't simply decide
who marries whom.
You can only make suggestions
about who should marry whom.
And what will
ultimately determine
who marries whom
are the preferences
of the married people, who
each have the right to marry
each other if they so choose.
They said what you
would like to do
then is you'd like to create a
stable matching if you could,
which is a matching that has the
property that there are no man
and woman not married to each
other who would both prefer
to be married to each other.
So your aim is to suggest
a stable matching.
Because if you suggest
an unstable matching,
they'll be a blocking pair.
They'll be a man and a woman
not married to each other
who would prefer
to marry each other
and who won't have to
accept your suggestion.
Instead of doing
what you propose,
they could marry each other.
HAL VARIAN: So in other words,
anybody that I would rather
have wouldn't want me.
ALVIN ROTH: That's
a characteristic
of a stable matching.
And they constructed
an algorithm
that produces a stable
matching no matter
what the preferences of
the men and the women are.
And there are two
versions of the algorithm.
One has the men proposing.
And one has the women proposing.
And so the man proposing
algorithm goes like this.
At the first step
of the algorithm,
each man proposes to
his first choice woman.
And every woman who has
received acceptable proposals--
if you receive an
unacceptable proposal that
is someone who you would
rather not be married to.
You'd rather be single.
You reject them.
But also if you've received
multiple acceptable proposals,
you hold onto the
one you like best,
the one you prefer the
most and reject the rest.
But you don't immediately
accept the proposal
that you don't reject.
This is a deferred
acceptance algorithm.
Acceptance will only
happen at the end.
So what happens?
All the men who have
just been rejected,
they propose to their next
choice woman, their next most
preferred woman.
And every woman who's
gotten the proposal,
now looks at the new proposals
that she gets together
with any proposal, which she
may have held previously,
keeps the one she likes
best, rejects the rest.
And this continues
until no more rejections
are issued, which it must do.
Because it's a finite problem.
And no man proposes
twice to the same woman.
And when no more
rejections are issued,
now the proposals are accepted.
And each woman is married to the
man, if any, whose proposal she
has not rejected.
She might be holding
on to one proposal.
And what Gale and Shapley
observed is that that matching,
regardless of what
the preferences were
that were the input, is stable.
It can't be that there
are a man and a woman who
would prefer to each other
to who they're matched to.
And the reason, of course,
is that if a man is matched
to his third choice woman, he
knows that his second choice
woman doesn't prefer him.
Because he already
proposed to her.
And she rejected him
when she got a proposal
that she preferred.
So that was where they left it.
And later, when we
started to think
about this as centralized
clearinghouses,
we said, you know,
this really depends
on knowing the preferences--
HAL VARIAN: Right.
ALVIN ROTH: --of
the men and women.
And one of the things
that you can show
is that the men can do
no better than to state
their true preferences.
But that the women situation,
when the men are proposing,
is more complicated.
HAL VARIAN: I think,
isn't there a Jane Austen
novel with that plot?
ALVIN ROTH: Several, I believe.
HAL VARIAN: Something
along that line, at least,
I think I've encountered before.
But Gale and Shapley,
to my recollection,
did not cite Jane
Austen in that paper.
ALVIN ROTH: Well,
they didn't deal
with the incentive problem.
They took what, for
better or for worse,
for many years became the
computer science approach--
HAL VARIAN: Right
ALVIN ROTH: --which is that the
person organizing the market
knows the preferences.
HAL VARIAN: And
many of you may know
that a very common exercise
in your first computer science
or programming class is to write
the code for this algorithm.
But without looking at the
incentives-- [INAUDIBLE].
Before we talk about
the incentives,
give us a few more
examples of where
this two-sided matching market
or multi-sided matching market
might be relevant.
ALVIN ROTH: Well,
so as I said, we've
helped build clearinghouses for
a variety of medical and health
care marketplaces.
But my colleagues and I are also
helping reform school choice
in a number of American cities.
And you can easily have
school choice mechanisms that
don't work as well as a
deferred acceptance algorithm
clearinghouse.
And so in recent years, we've
helped redesign the processes
for putting children
into schools in New York
and in Boston and New
Orleans and in Denver
and some other cities.
And a clearinghouse
that operates
like a deferred
acceptance algorithm
solves a couple of problems
that school choice systems often
have.
One is making it safe for
children and their families
to reveal their preferences
to the school district.
For many years, New
York City, the way
you applied for a
school was you who
submitted to rank order list.
This is my first choice, my
second choice, my third choice.
And then that list was Xeroxed
and sent to the schools.
And that was your application.
What that meant was if I
applied to your school,
you might be able to
see that I had applied
and you are my third choice.
And you could, as
a school principal,
adopt an admissions policy that
says, we're a popular school.
We're only going
to admit people who
list us as their first choice.
So that would mean that,
although it appeared
that I had lots of
choices of schools,
I might really have very few.
Because of the schools
I wanted, only the ones
that I listed first
would consider me.
Similarly, Boston had a
system of school choice
where they tried to give
as many people as possible
their first choice.
And when more people
wanted a particular school
as their first choice
than they could fit in,
they used a priority
system to break ties.
If an older sibling went to
the same school, you could go.
But the trouble with
that mechanism-- which
was very benign in intent,
they'd give as many people as
possible their
first choice, then
as many people as possible their
second choice and so forth--
was if you didn't get
your first choice,
your second choice might
already have all its seats
filled with people
who had listed it
as their first choice.
So that even though
you had high priority
at your second choice-- an
older child who already went
to the school, which
means if you had listed it
as your first choice--
you would've gotten in.
If you listed it as your
second choice, you wouldn't.
So when we use the deferred
acceptance algorithm
with students proposing,
that solves that problem.
Because if you don't get
into your first choice, when
you apply to your
second choice, they
haven't filled all
their seats yet.
They've rejected some
applications and kept others.
But they haven't accepted those.
And if you have very high
priority at your second choice
school, you go right to
the top of their list.
And they reject someone else.
So it's safe for you to tell
the city what your choices are.
Your chance of getting
your second choice school
if you get rejected
by your first choice
is just as large
as it would have
been if you had listed that
school as your first choice.
HAL VARIAN: And
it's interesting.
Both of these examples, the
school admission problem
and the residence
assignment problem,
have this side
constraint that it's
good to have husband and
wife in the same location.
It's good to have older
siblings and younger
siblings in the same school.
So the same
complications presumably
arise if you're trying to
solve that full problem.
ALVIN ROTH: So not quite.
In most cities, the
school choice problem
is simpler than the
couples problem.
HAL VARIAN: I see.
ALVIN ROTH: Because we're
dealing with older siblings who
are already in place.
HAL VARIAN: Ah, OK.
ALVIN ROTH: Now
twins are a problem.
HAL VARIAN: Yup.
ALVIN ROTH: And in New
Orleans, it's common for people
to change schools
at every grade.
In most cities,
you change schools
at kindergarten, sixth
grade, and ninth grade.
So the couple's problem
for siblings isn't so bad.
But in New Orleans, you might
have a first and third grader
and you want to move them
both to a different school.
So they look like
the couples problem.
HAL VARIAN: So the
stable marriage idea
is very attractive.
But it doesn't always exist.
In the two-sided case it exists.
But tell us about
the roommate problem.
ALVIN ROTH: Well, in
the roommate problem
and in the couples problem
it might not exist.
HAL VARIAN: And the couples.
Yes, right.
ALVIN ROTH: But Gale and
Shapley, in their '62 paper,
they said, you know,
there's a big difference
between defining what's
a stable matching
and knowing that one exists.
And what the deferred
acceptance algorithm
allowed them to prove was in
two-sided matching, when you
know who are the men
and who are the women
and who are the doctors
and who are the hospitals,
you can always find
a stable matching.
But they gave an example to
show that it wouldn't always
be so easy.
And they said think of the
problem of matching roommates.
So you have a bunch
of people, any of whom
can be a roommate
with any other.
This, incidentally,
is a little bit
like the problem we face
with kidney exchange.
But that's a different problem.
And we face it in
a different way.
But what they said about
roommates is so your job,
you're the housing office
at some university.
You get preferences
for roommates.
You have to fill double rooms.
And consider the
case of four people,
where person one's first
choice roommate is person two.
Person two's first choice
roommate is person three.
Person three's first choice
roommate is person one.
And person one, two, and,
three, their last choice
is person four.
And it turns out in
this example it's not
going to matter what person--
HAL VARIAN: He was my
roommate in college.
I remember him.
ALVIN ROTH: That's funny.
He told me the same thing.
But what you can see is
there's no stable matching.
Because whoever you
match with person four,
that person, the person who is
the roommate of person four,
is the first choice
of someone else.
And so whoever is
roomed with person four,
together with whoever
regards that person
as his first choice,
they're a blocking pair.
They would both
rather be rooming
with each other than the
matching that you've designed.
So that shows that stable
matchings might not always
exist.
And, indeed, one
of the first things
I observed about the
problem with couples
was that when you have couples
in the medical labor market,
stable matching might
not always exist.
And one of the
differences between being
an economic theorist and
being a market designer
was when I first observe this,
I was still a game theorist
just observing the market.
And it was enough
for me to observe
that therefore the problem
of matching couples
was a hard problem.
And that made for a great
paper and full stop.
So when one day my phone
rang and on the other end
was the Director of the National
Resident Matching Program
and he said, we're
having these problems,
would you redesign
the match for us,
I knew that it was
no longer going
to be enough to say
that's a hard problem.
It was going to become
my hard problem.
And so designing markets
has changed my taste
in economic theory in
much the way, I think,
that it's going to change the
taste of computer scientists
in algorithmic theory.
HAL VARIAN: It's like, you
proved the bumblebee couldn't
fly, now what?
ALVIN ROTH: Yeah.
HAL VARIAN: But it's got to fly.
Actually another paper
of yours that I handled
when I was editor of "The
American Economic Review"
that really appealed to
me was a sorority paper.
Right?
Because there was a
very explicit algorithm
that was used to match
sororities and pledges.
But it had some really
fundamental faults.
Maybe you could say a
word or two about that.
ALVIN ROTH: Well,
so let me actually
take that as an opportunity
to talk about market failure.
Because one of the
things marketplaces
have to do for markets
is make them thick.
And one way you get
markets to be thick
is to agree on a time, hopefully
inefficient time, at which
the marketplace can happen.
But a lot of markets,
and this happened
with the medical market,
they start to unravel.
They go earlier and earlier.
And today, if you
know someone who's
graduating from law
school, they likely
arranged their job perhaps
two years in advance,
particularly if they're going
to clerk for an appellate judge
or something like that.
Well, it turns out sorority
and fraternity recruiting
is called rush for
just this reason.
It used to be that in
the 1800s that sororities
and fraternities were social
clubs for college seniors.
And then, in an
effort to recruit
more attractive candidates,
some of the fraternities
and sororities started to
rush and recruit earlier.
And today, of course, colleges,
sororities, and fraternities
recruit freshman.
They can hardly go any earlier.
Although, at the beginning of
the last century when there
was a lot less mobility
about colleges, rush actually
extended into high school years.
You'd know who was
coming to your college.
And you could rush
them before they came.
But different fraternities
and sororities
develop different
ways of handling that.
And the sororities
have an organization
that specifies-- doesn't
completely specify,
computer scientists would
not be happy with the way
they describe their algorithm.
Because you can create events
in which it's undefined.
But they have an
algorithm for recruiting.
And it's an
interesting algorithm.
And it starts with
parties of short duration
that everyone is supposed
to be invited to.
And so you get invited
to a first party.
But then the sororities could
invite some young women back
to a second party.
And young women can't
accept every invitation.
They have a limit on
how many they can go to.
So there's information
about preferences
exchanged through parties.
And then finally, there's
a matching algorithm.
Preferences are submitted.
And it has serious problems
that has been heavily gamed.
So when you study how
sororities attract--
it's been heavily gamed in
part, because universities
and the Pan-Hellenic
Council sometimes
tries to allocate equally
the number of pledges
to sororities independent
of their capacity.
And that causes problems.
It means that the outcomes
that they try to enforce
are unstable.
There may be blocking pairs
of young women and sororities
who would like to be
matched to each other
and who could be
matched to each other.
So that changes the way
sorority recruitment goes.
HAL VARIAN: And that
was really a failure
on the incentive side.
ALVIN ROTH: Yeah.
HAL VARIAN: So--
ALVIN ROTH: And stability
is related to incentive.
HAL VARIAN: And
stability, yes, yes.
ALVIN ROTH: Stability
speaks to incentives
to recontract or to
go outside the system
and get your match outside
the official marketplace.
So one of things
marketplaces have to do
is attract participants.
They have to make
the market thick.
HAL VARIAN: So when I first
came to Google back in 2002,
I asked Eric Schmidt
what I should work on.
He said, why don't you take
a look at this ad auction.
I think it might make
us a little money.
And the ad auction started
in February of 2002.
And I arrived in May of 2002
and looked at the ad auction.
I was convinced, oh, this
is so straightforward.
It must be in the
literature somewhere.
And I looked through
all the literature
I could find on
auctions and so on.
And a year or two later,
I discovered actually
the basics were in your
book, the Roth and Sotomayor
book, which was a
kind of compendium,
everything that was known
about the assignment problem
and two-sided matching
problems in that book.
And there in the
chapter on incentives,
there was a description
of a problem
of assigning workers to jobs.
And it turned out that problem
was very similar to assigning
advertisers to slots.
You know, there were
little differences.
In fact, it was a more general
problem as it turns out.
But if you read that
chapter creatively,
you can see the connection to
what happened with the Google
ad auction.
So there's some
hidden connections
that I think most
people aren't aware of.
You probably would
have never guessed
that would be an application.
ALVIN ROTH: If you read that
chapter and you're Hal Varian,
you can see the connection.
HAL VARIAN: Well,
it does require
a little bit of the insight
to see the connection.
And the first time I
read it, I didn't see it.
But after two or
three times, I got it.
ALVIN ROTH: But when
we wrote in 1990,
which surely weren't
thinking about Google.
HAL VARIAN: Yes, yes.
And there's lots more.
I mean, if you think about it,
if you look at the tech world,
not that you're a
west coaster, there
are lots and lots
of applications.
Matching up drivers with
passengers is what Uber does.
Matching up short-term renters
with rooms is what Airbnb does
and many, many other
applications of that sort.
A lot of activity in this area.
And again, I think they start
with these basic fundamental,
simple algorithms.
But then there are
always these edge cases.
And there are always the
constraints and issues
that arise on top
of that that make
things difficult and exciting.
ALVIN ROTH: Absolutely.
And market design
is about details.
But there are also some
general principles.
And as you say, these
are matching markets.
A lot of people, maybe
not here, but a lot of
people when they
think about markets,
think about commodity markets,
markets in which you don't care
who you're transacting with.
When you buy shares of stock
on the New York Stock Exchange,
you don't care who
you're buying from.
And they don't care
who they're selling to.
But as you say, Airbnb
is as a matching market.
You have to match a
traveler with a host.
And there are lots
of markets where,
even when prices
are important, they
don't decide who gets what.
The way I put it in the book
is that matching markets are
markets in which you can't
simply choose what you want,
you also have to be chosen.
HAL VARIAN: And you look
at Google, in a way,
we're matching up
people with questions
with people with answers.
I mean, maybe we give
them the answers directly.
Maybe we send them to a site
where they can find the answer.
So we're doing questions
and answers matching.
And then we're also doing buyers
and sellers on the ad side.
There's a lot of interest these
days emanating from Europe
about multi-sided
platforms, where they're
talking not necessarily
markets, per se,
but places where
people can meet up.
And this would include
Facebook as an example.
Because you're matching
you and your friends
and potential friends and so on.
Do you encounter
that issue, I mean,
where you're talking more
about platforms which
is an industrial organization
sort of topic as opposed
to markets as
narrowly construed?
ALVIN ROTH: Oh, again, I
think of markets very broadly.
HAL VARIAN: Pretty broadly.
ALVIN ROTH: So
absolutely, I think
that the things we talk about
platforms-- in the book,
I talk about things
that are often
thought about as platforms,
like for instance credit cards
and smartphone operating
systems and things like that.
So the same reason
there are just
a few credit cards is the
reason why they're are just
a few smartphone
operating systems.
Because you want an operating
system that has a lot of apps.
And the app developers
want operating system
that has a lot of users.
Yeah.
ALVIN ROTH: So I think of
it as being part and parcel
of making a market thick.
HAL VARIAN: Yeah.
And when you think about
Google, again, we've
got the people, the
questions, and the answers.
And we've also got the
content generators.
And so you want to look
at the whole ecosystem
and try to figure
out how can I develop
a system that sort of satisfies
all the parties involved.
And of course, there are
inevitably trade offs.
In fact, let me go back to the
very simple stable marriage
problem.
You talked about the
case where the men
are doing the proposing.
But, hey, there's another side.
The women could
do the proposing.
So what's the
difference between?
Do you get the same
outcome, different outcomes?
ALVIN ROTH: Well,
in small markets
you get different outcomes.
And the difference
matters systematically.
The men like better
the stable matching
that results when men propose.
And the women like
better the outcome that
results when women propose.
Now it turns out
in large markets
or in markets with different
numbers of people on each side,
those differences go
away pretty quickly.
So when we look at most of
the markets we deal with,
the set of stable
matchings is very small.
So it turns out you can
always get a stable matching
in these two-sided markets.
But there's a sense in
which it's very hard.
Having to get a stable
matching pretty much
determines the outcome
in many of these markets.
HAL VARIAN: So we
talked about some
of the existing players in
this matching world, the Ubers
and Airbnbs, and others.
Have you talked to any
interesting startups
that are not widely known yet?
I know one.
I'll tell you about mine.
But I want to hear
from you first.
ALVIN ROTH: I've talked
to some startups.
But I also think about markets
that might now lend themselves
to startups that could
use some help in matching.
One of the startups I
mentioned int he book
is a company called
BandwidthX that's
trying to match up travelers
and their smartphones
with unused Wi-Fi.
This would be seamless as
far as you were concerned.
But sometimes your
smartphone would
connect to the Wi-Fi
in my apartment
while I'm here and
things like that.
And so the contract
would be with the cable
provider things like that.
But among the matching markets
I'm a little bit thinking
about these days as I read
about refugee resettlement,
it turns out refugee
resettlement policies
are a little bit like early
days computer science.
They don't always take account
of people's preferences.
So mostly we're reading
about refugees in Europe,
in Calais trying
to get to England.
But here in the
United States, we
have a policy that we
try to resettle people
sort of broadly across the
country with the idea that
helps assimilation and
doesn't put too much
of a burden on communities.
But of course, once someone
comes to the United States,
they can get on a bus and
go wherever they want.
So it so it turns out that
there's a thriving community
of Somali immigrants in Maine.
Not because the Maine climate
is a lot like Somalia,
but because there's already
a thriving community there.
And so people are prepared to
give up the housing subsidies
that we offer them in far places
in order to go to the places
they want to be.
So I think that as we think
about things like refugee
resettlement, we have to think
not just about individuals,
but about communities and
about what they would like.
HAL VARIAN: There was a great
story in "The Wall Street
Journal" several years ago
about Cambodian doughnut shops.
It turned out that
something like 25% or 30%
of the doughnut
shops in California
are run by Cambodians.
And it all started with one
guy brought his brother over.
And the brother had a son.
And so they expanded
into this industry.
But the punchline
of the argument
was that Cambodians really
didn't eat doughnuts, which
is maybe a good thing to do if
you're running a doughnut shop
if you don't really like them.
ALVIN ROTH: But
that's a common--
HAL VARIAN: Common occurrence.
ALVIN ROTH: --pattern
of emigration in the US.
So there are concentrations
of Koreans in dry cleaning,
of Indians in motels.
And it has to do with
coming over and having
a cousin who will take care
of you while you get settled.
And in the course of
taking care of you,
you work in his business.
And he tells you how
to contact suppliers
and how to deal with customers.
And then you can set up.
HAL VARIAN: Yup.
Yup.
Tell us a bit about
your kidney exchange.
I think that's a very exciting
and interesting story.
ALVIN ROTH: So organ
transplantation
is a fascinating area.
But it's also a critical
health care need.
Right now, there are 100,000
people in the United States
on a waiting list for
a deceased donor organ.
So those of you who have
California driver's licenses
if you look at your license,
you might see whether there's
a little pink dot
on it or not, which
will have to do with
whether you registered
to be a deceased organ
donor when you've
got your driver's license.
And you can still do that.
If it's not on your
driver's license,
there's a website at--
I forget the name now.
It's part of the California
government website.
donatelife.gov.california.
But the aren't enough
deceased donor organs.
Even if you all registered,
there aren't enough.
Because it's very hard to die
in a way that makes your organs
donatable.
HAL VARIAN: Well,
that's a relief.
ALVIN ROTH: Well,
transplantation
is a funny subject.
There's good news and
bad news mixed in.
So the bad news is there
aren't enough donors.
The good news is traffic
fatalities in the United States
have really dropped
since we were young.
But kidneys are unusual.
Because you each
have two kidneys.
And if you're healthy,
you can remain healthy
with just one kidney.
And that means that if you new
someone, if you love someone
who is dying of
kidney failure, you
might be able to donate
a kidney to them.
And donating a kidney
has two parts to it.
First, you have to be healthy
enough to donate a kidney.
But second, your kidney
has to match them.
Not everyone can take
everyone else's kidney.
And often, that
second part fails.
And that's where kidney
exchange comes in.
It could be that you love
someone who needs a kidney.
And you're healthy enough
to donate a kidney.
But you can't donate it to them.
And I'm in the same situation.
But maybe you can donate
a kidney to my patient.
And I can donate a
kidney to your patient.
And so that's a simple exchange.
It doesn't involve any money.
Because it turns out
it's against the law
to pay money for a kidney
in the United States
and almost everywhere in the
world, the single exception
being the Islamic
Republic of Iran,
where there's a cash
market for kidneys,
a monetary market for kidneys.
For the rest of the world,
you can't buy a kidney.
But you can exchange kidneys.
There's an amendment to the
National Organ Transplant Act
that makes it clear
that this is legal.
But we had already started
kidney exchange in the United
States before that.
And over the years we've
gone from simple exchanges,
like the one I just
described between two pairs,
to very complicated exchanges
that may involve long chains.
And some of these are
reported in newspapers.
And you see sets of pictures
of 60 people in them
or more, which means 30
donors and 30 transplants.
So this is where the
computationally difficult
optimization problems come in.
But so far, we're
able to solve them.
And kidney exchange
in the United States
has become a standard form of
transportation still growing.
About 10% of the
living donor kidney
transplants in the United States
are now done through exchange.
My computer science and
economics colleagues and I
have worked with a variety
of exchanges and hospitals.
And it's spreading
around the world as well.
So that's been a lot of fun.
HAL VARIAN: So there
is a constraint there.
And it's different than the
brother, sister, husband,
wife kind of constraint.
And I know you've thought
about this issue of repugnance,
that people are
opposed at this idea,
they find it distasteful that
somebody might sell a body part
or something like that.
ALVIN ROTH: And it's a
felony in the United States.
HAL VARIAN: A felony.
ALVIN ROTH: So the National
Organ Transplant Act
makes it a felony for you
to sell me your kidney
or for me to buy your kidney.
So I got interested
in the question
of repugnant transactions.
Because of course,
there are 100,000 people
waiting for kidneys
in the United States.
Thousands die each year.
It's not that this
is a costless wait.
So it's interesting
that everywhere
in the world, just about,
it's against the law
to buy and sell kidneys.
At the same time, there
are black markets.
There are people
desperate to buy kidneys.
And there are people
willing to sell them.
But black markets, in which you
have to deal with criminals,
because they're against the law,
often work very, very badly.
So they give few
guarantees and little
post operative care to the
donor or sellers and things
like that.
But there's lively debate among
surgeons and among patients
and among other people about
whether this is a good law
or not.
Without presuming to
answer that question,
I can't help but be
impressed by noticing
that this is something that's
illegal just about everywhere.
So I started to get interested
in the question of what
I call repugnant transactions,
which are transactions
that some people would
like to engage in
and other people think
they shouldn't, even
though those people
may not directly
be harmed by the transaction.
So there are a lot of
examples from the profound
to the trivial.
For instance, I just had
lunch at Google cafeteria.
And one thing I can
tell you with confidence
is they never serve horse meat.
Because it turns out
it's against the law
to serve horse meat for human
consumption in California.
And that's not an ancient
cowboy law from the time
when a horse was a
man's best friend.
That's the result of
a 1998 referendum.
It turns out there were
sufficient petitions
to get that on the ballot.
And Californians voted for it.
And of course, it's not
because no one in California
wants to ear horse meat.
It's because some people in
California do want to eat horse
meat and other people
think they shouldn't.
There's no law against eating
cockroaches in California.
HAL VARIAN: Or New York.
ALVIN ROTH: Or New York.
HAL VARIAN: Now
were you around--
the Harvard Faculty Club
used to serve horse meat--
ALVIN ROTH: It did.
HAL VARIAN: --when
I visited there.
And you could have it.
It was put in place, as I
remember, in World War--
ALVIN ROTH: World War II.
HAL VARIAN: --World War II?
ALVIN ROTH: Or
World War I maybe.
HAL VARIAN: I think it was World
War I. Because the horses were
used to World War I.
And the beef was being
sent to the soldiers and so on.
And so it was a
patriotic measure.
But Harvard being Harvard,
it never took it off.
So I think it was 1970, 19--
yeah, it was a 60 years.
For 60 years at least,
you could eat horse meat
at the Harvard Faculty Club.
But I think even that's
gone, these old traditions.
ALVIN ROTH: It is gone.
And it's gone for-- I
don't know exactly why
at the Harvard Faculty Club.
But in fact, although it's
only illegal in California,
you can't eat horse meat
anywhere in the United States.
It's not illegal,
but you can't get it.
And the reason you
can't get it is Congress
on several occasions tried
to pass a law against eating
horse meat and failed.
But you can't eat any
meat in the United States
unless it is graded grade A,
fit for human consumption,
by the US Department
of Agriculture.
And Congress for many years
now has withheld funds
for the Department
of Agriculture
to inspect horse meat.
So there is no USDA
grade A horse meat
in the United States.
Although, it would be perfectly
legal for you to eat it.
But there are more
profound kinds
of repugnant transactions.
Think about same sex marriage.
I think of that as a sort
of prototypical repugnant
transaction.
Some people wanted
to marry each other.
And other people, who weren't
involved and weren't planning
to necessarily marry
themselves, other people thought
they shouldn't, so a
repugnant transaction.
And in the last 12
years, we've gone
from having that legal in
no American state to being
legal in every American state.
So repugnance can change.
But it's not that as
we get more modern,
old repugnances
necessarily die away.
There are things that used
to be not so repugnant that
are now repugnant.
We used to sell slaves
in the United States.
We don't do that anymore.
And the most common
way of buying passage
across the Atlantic
Ocean to North America
used to be to sell
yourself into a five year
contract of
indentured servitude,
of voluntary slavery
for a fixed term.
And we don't do that anymore.
So there are things
that used to be
not so repugnant that we didn't
do them that are now repugnant.
There used to be
things that were
repugnant that are no longer.
And it's possible
that our understanding
of how to elicit organ donations
may start to be in flux.
It's certainly a topic
of lively conversation.
HAL VARIAN: To change
gears a little bit,
there's another
interesting market
that's being developed now
by some of your colleagues
at Stanford.
That's the incentive
market for spectrum,
where they're trying to get
spectrum from TV stations
to sell in to the government
as an intermediary, who then
repackages that
spectrum, moves the TV
stations to different
frequencies,
and then sells the freed up
spectrum to the mobile phone
carriers.
And so this is a very
big efficiency from this.
Because there are
stations, TV stations,
that have very tiny
audiences where
the spectrum their utilizing
would be immensely more
valuable from the point of view
of mobile telephony and mobile
data use.
Have you been involved
with that at all?
ALVIN ROTH: Well, Paul Milgrom
and some of my colleagues
are deeply involved in that.
So I hear a lot about it.
One of the important
market design
features that you mentioned
in passing there has
to do with the property rights.
What is it that the
television stations were given
and that they own?
And what the Federal
Communications Commission
decided is that what
they had been given
was a bandwidth, but not
particular frequencies.
HAL VARIAN: Right.
ALVIN ROTH: And so that means
that when the FCC buys back
some spectrum from
some TV stations,
they can then compactify
the remaining TV station so
that the rest of the
spectrum could be
used efficiently in other ways.
HAL VARIAN: Yup, yup.
ALVIN ROTH: And that was an
interesting property rights
decision that has this
very nice consequence
that we can make more
efficient use of spectrum now.
You could imagine
that they would
have decided that what
the TV stations owned
was the particular band
on which they broadcast.
And that would
make it much harder
to repurpose the spectrum.
And of course in the
old days, TV stations
were given their spectrum
through political processes.
They didn't bid for them.
And they originally
had analog spectrum.
Then they were given different
spectrum that was more
suited to digital purposes.
And many TV companies, therefore
broadcasting companies,
now own both kinds of spectrum,
even though there only making
active use of one.
HAL VARIAN: Yeah.
And also there's an interesting
computational problem
in doing that repacking.
Because at one point, people
were somewhat skeptical
that it could be done
in a time frame that
was appropriate for
this auction process.
But it turns out there have
been made great advances there.
And doing their
repacking of spectrum
is now pretty straightforward.
ALVIN ROTH: Right.
And a lot of this has to do
with machine learning things.
HAL VARIAN: Yeah.
ALVIN ROTH: So
Kevin Leighton Brown
is one of the people
who is involved in this.
HAL VARIAN: Yup.
We're going to be
coming up-- at Google,
we're quite interested
in this 3.5 gigahertz
spectrum, which is
going to be coming
available in the next few years.
And it will be kind
of Wi-Fi on steroids.
So it's high frequency.
You can pack a lot
of data into it.
But by the same token, it has
very poor propagation policies.
So the question is
what kind of market
or what kind of, let's say,
platform or arrangement
is appropriate for
utilizing that spectrum?
Because it would be something
that we could really
increase wireless access
quite dramatically
at much lower cost.
Well, I've talked enough here.
I'd like to take some
questions from the audience.
Do we have any questions
out there for Al?
ALVIN ROTH: Or for Hal?
HAL VARIAN: Or for Hal?
Yeah, yeah.
It's only a little
aspirated consonant away.
Yeah?
AUDIENCE: I noticed that
your degrees were in OR.
And I wondered how did you
make the transition from OR
to economics?
And is that a
natural transition?
Obviously, game
theory shows up there.
ALVIN ROTH: OK.
So how did I make the
transition from OR to economics?
I'll answer the
question two ways.
Because in some moods I
feel like I never transited.
I just stayed doing
what I was doing,
and the disciplinary
boundaries shifted around me.
So I did get my degree
in Operations Research.
I wanted to help make
things work better.
But the things that
I was interested in
were multi-user systems.
So I started to
study game theory.
And it looked like game theory,
when I got my PhD in 1974,
it looked like game
theory was going to thrive
in Operations Research.
But the game theory
in 1974 didn't yet
have an engineering
aspect to it.
So it didn't initially thrive
in Operations Research.
It thrived in economics.
So that's the part of the
story that says I just
stay doing what I was doing.
And now that we've
developed an engineering
part of game theory,
market design,
it's something that
crosses boundaries.
When my colleagues and I
teach a three quarter sequence
in market design at
Stanford our students
come not just from
economics, but also
from the Department
of Management Science
and Engineering, which
is where OR settled down
and from computer science
and from the business school.
So I think of market
design as being
something that is inherently
multi-disciplinary.
AUDIENCE: Thank you
so much for coming.
My question has to do
with the kidney exchange.
I read that many of those
chains, the longer ones,
require an altruistic first
give in order for them
to all fall in line.
ALVIN ROTH: Yup.
AUDIENCE: I was wondering if
there was other markets that
require an amount of
altruism or that first person
to say they don't
have a preference
or will give something in
order to make the chain work.
ALVIN ROTH: Well, it's true that
to do a non-simultaneous chain,
we like to start with a
non-directed donor, someone
who doesn't have a patient
who needs a kidney.
That way each patient donor
pair that gives a kidney
first gets a kidney.
So that if there's a
break in the chain,
it doesn't cause a
disastrous outcome
for some pair that's given a
kidney, but didn't get one.
I think that when
housing markets are
either very hot or
very cold, you often
see chains in real estate.
So let's think of housing
markets that are very cold,
so not something you guys in
Silicon Valley know about.
But when it's hard
to sell a house,
house contracts often
have contingent agreements
which say I'm going to buy
your house unconditioned that I
can sell my house in a timely
way for a certain amount.
And then you wait and see
whether I sell my house.
And I might, of
course, have an offer
to buy my house from someone
who needs to sell his house.
So the chains form
when someone-- so that
was the kind of market when we
sold our house in Boston when
we moved out here in 2012.
And the chain is broken
when someone moves out
from California and
has cash in their hand
and doesn't need
to sell a house.
They've already
sold their house.
And indeed, we moved
here from Boston
and didn't need to sell a house.
Now, in a very hot
market, it could sometimes
work the other way.
You say before I sell my house,
I want to find a house to buy,
so I'm sure that I
have a place to live.
Because houses are
going so quickly.
And so someone who's selling
their house here and moving
to Boston doesn't need that.
And they're the end of a chain
or the beginning of a chain.
HAL VARIAN: That's interesting.
And this point about
having an altruist
really helps in design.
Another case where this
happens is in matching grants.
So I'm trying to raise
money for some cause.
And I say, well, for
every dollar you give,
I'll give a dollar.
So I'll do this matching grant.
And I wrote a couple papers
on this some time ago.
But I dug into the
history a little bit.
And guess who came
up with the market
design of matching grants?
ALVIN ROTH: Peggy Guggenheim.
HAL VARIAN: You're off
by a few hundred years.
It was Benjamin Franklin.
Benjamin Franklin was the
first market engineer.
Because he came up--
he's very proud of this.
It's in his autobiography.
You can read the
chapter where he
describes how he came
up with this idea
and how well it worked.
So a lot of his
projects were actually
financed by this
kind of matching.
Question?
AUDIENCE: So clearly
employer-employee matching
has been very successful
in the medical industry.
And I can imagine reasons why
that is, like all residents
graduating at the
same time of the year.
Are there other markets that
you think it could also work in
and what's blocking more
fields of employment
from using similar systems?
ALVIN ROTH: That's
a good question.
So there are certainly
other markets.
But they tend to have
the same characteristics
that you mentioned of
the medical market, which
is first of all,
lots of people are
graduating at the same time.
Second, they are all going
into the same kind of job.
So lots of MBA students
graduate at the same time.
But the set of employers
who employ MBA students
is much bigger than the
set of employers who
employ new medical graduates.
The employers are all
residency programs basically.
So there are lots of
two-sided matching markets
that are having problems and
that might well work well
with centralized clearinghouse.
But markets the are
simply having problems
don't necessarily lend
themselves to redesign.
Sometimes they're
having problems
because of conflicting
interests in the parties.
So there's some congestion.
And in, for instance, college
admissions in the United
States, it's now pretty easy
to file a lot of applications
to college.
And of course,
college admissions
are matching markets.
They're not commodity markets.
Stanford doesn't choose
its freshman class
by raising the tuition
until supply equals demand.
There are all these other
market institutions.
And Stanford
probably doesn't have
to worry too much if you
apply to Stanford about how
much you're interested.
Because there are
a lot of people.
Everyone who applies
to Stanford is at least
potentially interested in going.
But there are lots of American
Colleges and Universities
that have to think hard not just
about how much they like you,
but about how much
you like of them.
Will they be able to be
successful in recruiting you?
And there was a
time, of course, when
it was harder to apply to many
colleges before the common app,
say, where the mere fact
that you would apply to them
was a pretty strong
indication of interest.
And that has gone
away a little bit.
So I think there's some
room to explore ways
to help break that congestion.
And one of the markets
we've done that in
is the market for
new PhD economists.
And the American
Economic Association
has developed a
signaling mechanism
where new PhD
economists can apply
to all the available jobs.
And the Department
of Economics that
wants to hire an
assistant professor
can get 600 applications.
And we go to these
meetings in January
where we interview people.
But even a hardworking
junior recruiting team
could only interview
20 or 25 people.
So what the American
Economic Association now
allows you to do is to send
two signals of interest,
with the idea being that you
can lots of applications,
but could only send two signals.
An employer who gets
one of your signals
should take a look again
and think maybe they
should interview you.
And I have some colleagues
at Stanford, Muriel Niederle
and Soo Lee, the who did a
study of an online dating market
of this sort.
And dating markets
are also congested.
People with attractive
pictures and profiles
might get more emails
than they can answer.
And people who aren't
getting their emails answered
might send more and more
emails to more and more people
so that there's less
and less information.
And so what they did is they had
a dating site where they gave
people two virtual roses a day.
So you could send as many
emails as you wanted,
but only two of them
could have roses attached.
And what they observed was
that messages with roses
were more successful in
leading to subsequent contacts
than not.
So I think a lot of
markets can't really
use the solution that
the doctors have used,
but still have to deal
with problems of congestion
and thickness in ways that
the clearinghouse solves
for the medical market.
AUDIENCE: OK.
So I gave you a softball
question at the beginning.
I'll give you a
hardball question now.
ALVIN ROTH: Hal will answer it.
AUDIENCE: OK, good.
Either one of you.
As you were describing the
Gale and Shapley setup,
I was thinking in
my mind about how
you can change the assumptions
and how faulty the assumptions
were in some ways,
like there's a barrier
synchronization for
marriage or the fact
that people know
their own utility
function for what a
good match is just
seemed like faulty models.
So I could ask which
of these you think
is a better thing to follow.
But I'll ask a
harder question even,
which is mathematicians
would identify
a few problems like
the Riemann hypothesis
or the twin prime conjecture
as the big unsolved problems.
Computer scientist
would say p equals np.
What would a person in your
field, in market design,
say are the biggest
unsolved problems that
could be framed mathematically?
ALVIN ROTH: That's
a good question.
I don't have a
ready answer for it.
And one reason I don't
have a ready answer
is I think of market design more
as an engineering kind of thing
than as a mathematical
kind of thing.
So when you think about what
are the big unsolved problems--
and let me go back
to the beginning part
of your question, maybe
the softer ball part.
You say each of our
models has assumptions
that may not be exactly right.
And that's certainly true.
Markets are complex.
People have large
strategy spaces.
When we make models, we try
to take a piece of the market
that we think is the most
important piece and model
what's going on there
and build something
informed by that model.
But you have to
constantly be aware
that all the things
you left out may
be impinging on the
boundaries of your model.
So when you prove a
theorem, it's true forever.
You know, Pythagoras's
theorem tells us
about right triangles,
ancient and modern and here
and in China.
It really nails right triangles.
But when you look at
bridges, you know,
the Romans built some
great bridges, so great
that they're still standing.
But when we build bridges today,
we build them differently.
And I don't know that we solved
any particular outstanding
problem on bridges.
But over time, we got
better at building bridges,
because of all sorts of things,
better materials, better
designs.
And the underlying physics
is the same as it always was.
But our bridges don't look
like the Roman bridges.
So I think the
things we're building
and the flaws in our models are
things that some of those flaws
are important and
some are not now.
That will change over time.
Because markets are
little bit like bridges.
When you build a bridge,
people start to use it.
And that changes the
traffic patterns.
And then you might need better
roads and bigger bridges.
And that's the way
it is with markets.
Marketplaces have
a lot of problems.
They have to make market thick.
They have to deal
with congestion.
They have to make things safe,
and simple, and untrustworthy.
And how to do
those in each case,
these are all often
outstanding problems.
But they may have
different solutions
in different environments.
So I don't think there is going
to be a Riemann hypothesis
or something so
central that forever
after people will be
able to point to it.
Because as Hal already
mentioned and as we discussed,
some of the cool
things about, say,
Gale and Shapley's deferred
acceptance algorithm
no longer apply to the
more complicated models
that we're dealing with even
in things like medical matching
when we start to have married
couples, things like that.
So again, I could put
the question back to you.
Maybe I'd understand
better if you told me
what engineering
problems you think
of having the status of
the Riemann hypothesis.
I think of engineering problems
as being more incremental.
Over time, we get
to do things better.
And that's good.
HAL VARIAN: I think
that's a great wrap up.
Our time is up.
Thanks so much for coming, Al.
ALVIN ROTH: Well, thank you.
[APPLAUSE]
