MALE SPEAKER: Good afternoon.
Welcome to Talks at Google
in Cambridge, Massachusetts.
Today it's my great pleasure
to introduce Richard Thaler.
Don't get too comfortable while
you're enjoying your lunch,
because Dr. Thaler is here
today to change the way you
think about economics.
In his newest book,
"Misbehaving: The Making
of Behavioral
Economics," he shows we
wacky humans don't,
in fact, make the kind
of rational decisions that
traditional economics has
assumed in creating its models.
At first, these deviations
from the assumed norm
were dismissed, but
now our miscalculations
and their effects are the
subject of serious study.
The book couples
recent discoveries
in psychology with a practical
understanding of incentives
and market behavior
to tell us how
to make smarter decisions in
an increasingly complex world.
Richard Thaler is an American
economist and the Ralph
and Dorothea Keller
Distinguished Service Professor
of Behavioral
Science and Economics
at the University of Chicago
Booth School of Business.
He is the co-author of the
bestselling book "Nudge"
with Cass R. Sunstein and
the author of "Quasi-Rational
Economics and the
Winner's Curse."
Please join me in
welcoming Richard Thaler.
[APPLAUSE]
RICHARD THALER: OK.
Thank you very
much for having me.
The title I've chosen
for the talk today
is "The Behavioralizing
of Economics:
Why Did it Take So Long?"
And this will give
you a little taste
of what the book is about.
So it seems reasonable to
start with a definition of what
is behavioral economics.
And as one of my predecessors,
Herb Simon, Nobel Prize winner,
wrote this-- the phrase
"behavioral economics" appears
to be a pleonasm.
A free book to anyone who
knows what that word means.
It's a redundant phrase.
And so Simon is
right, that it does
seem to be a redundant phrase.
Why do we need the
adjective "behavioral?"
What other kind of
economics could there be?
And then he answers
his own question.
The answer lies in the
specific assumptions
about human behavior
that are made
in neoclassical economic theory.
So that theory is
based on the assumption
that agents in the
economy optimize.
They choose the best
thing all the time.
Is that accurate?
So the idea is that economists
think that the world is
populated by people like Spock.
[AUDIO PLAYBACK]
-My choice will
be a logical one.
[END PLAYBACK]
Cold-hearted,
rationalizing optimizers.
And I entertain the
possibility that there
are some Homer Simpsons around.
[AUDIO PLAYBACK]
-Woo-hoo!
[END PLAYBACK]
So how do they differ?
The people that populate
economic models I call econs.
And they're perfect calculators,
rational expectations.
They have no
self-control problems.
They never are on a
diet, because they
eat just the right amount.
They never have
hangovers, because they
drink the right amount.
And they're complete jerks.
Humans are dumber,
weaker willed, and nicer.
Here's an illustration of
the fact that they're nicer.
This is a picture
I took in Ithaca,
where I lived for many
years teaching at Cornell.
A farmer puts this stand up.
This time of year, he
was selling rhubarb.
And it's an honor box.
You put in your money,
and you take your rhubarb.
Notice there's a box here,
and it's got a lock on it.
And I think the farmer
has just the right model
of human nature, which is
there are enough honest people
that it's worthwhile for him
to put the corn or rhubarb out
there and enough people will
put money in the box for him
to be willing to do that.
But if he left all the
money out on a plate,
somebody would steal it.
When I first started teaching,
I had the following problem.
I gave an exam, and the
students got mad at me.
And the reason they got mad
at me was the average grade
on the exam was 72.
These were Cornell
MBA students, so they
were top students
from top universities,
not used to getting
grades like 72.
And I told them, look, what
difference does it make?
We grade on a curve here.
The curve was some
inflated thing.
And so it really doesn't
matter what that number is.
But they were still mad at
me, so I had to figure out
a way of solving that problem.
So the next exam I gave,
I made out of 137 points.
And the average grade on that
was-- it was a harder exam.
The average was 70%.
But that computed to 96,
and they were delighted.
And in fact, some people
got scores over 100,
and they were in ecstasy.
So this is an illustration
of what I call
supposedly irrelevant factor.
So economists disagree
about all kinds of things,
like whether we
should fix the economy
by having the government
spend more or less.
It would seem to be pretty
basic that we could get that one
right, but you can
find economists
arguing on both sides of that.
But one thing economists
all believe in
is that there's some stuff
that just doesn't matter.
So for example, the number of
points available on the exam
should not matter.
And there are lots
of other things.
Some costs shouldn't matter.
How much you paid
for some dessert
shouldn't influence
how much of it
you will eat if you're already
full and it's very fattening.
But if you're human, then the
more you paid for that dessert,
the worse you feel
about not eating it.
So I started collecting examples
of supposedly irrelevant
factors, but economists were
not impressed with these.
And they had a long
list of excuses
why these things don't matter.
And let's talk
about some of those.
So an early one came from
the famous Chicago school
economist Milton Friedman.
And it has just a
two-word-- as if.
And in fact,
economists can dismiss
an argument with the wave of
a hand and the phrase "as if."
And so what does as if means?
It means we don't
care whether people
can't compute these things
that you assume they can do.
All we care about is whether
they behave as if they could.
And so the fact that people
are incapable of figuring out
how much they need to
save for retirement
doesn't matter as long as
they behave as if they can.
And this was taken to-- this was
a winning argument for 30 years
until two psychologists named
Daniel Kahneman and Amos
Tversky came along.
And they had an
insight, which was
that there are predictable
circumstances where people
make predictable mistakes.
So for example, they coined
the term "the availability
heuristic," which is when
people judge frequency
by ease of recall.
And that's typically
a good idea.
Things are usually more
frequent if you can think
of lots of examples of them.
John is a common name, and you
know lots of people named John,
but it sometimes
can lead you astray.
So for example,
people think there
are more homicides than suicides
when, in fact, there are
about twice as many suicides.
But homicides get more press,
and so they're more available.
And so we make a mistake.
And this example illustrates
a simple refutation
of the as if argument, or
the more general argument
that we don't care
whether people
make mistakes, because
if they cancel out,
we'll still be right.
And the point is,
they don't cancel out,
because the errors
are systematic.
Another argument I
got for many years
is, OK, you run some
experiment, and people screw up,
but if we get the
stakes high enough,
then people will get it right.
And another argument
I would hear-- often
from the same person-- would
be, and in your experiment,
there's no real
opportunity to learn.
So I ask you the homicide
suicide question once,
and you get it wrong.
But if I asked you 100 times and
presumably told you the answer,
then you'd get it right.
So I'd hear these two arguments.
And in fact, once
at a conference,
I was hearing both of
those from an economist
called Ken Binmore.
And it caused me to
create what I now
call the Binmore
continuum, which
is-- think about the frequency
with which you buy stuff.
So there's some stuff
like milk that you
buy almost every time you
go to the grocery store.
Then suits-- maybe once a year.
More often, I guess, if you
have to wear them often.
Houses, maybe once a decade.
Marriages-- most people no more
than two or three per lifetime.
Saving for retirement--
most of us only
get to do that once
barring reincarnation.
So notice as we're
raising the stakes,
we're reducing the number
of times you get to do it.
So in fact, there's
simply no reason
to think that when
the stakes go up,
people are going to get it right
more often, because they've
had no opportunity to learn.
Another argument I would
hear is, well, look,
if the stakes are
high enough, people
will hire some
expert to help them.
But here are three examples
of high stakes decisions.
Do we really think experts
are jumping in and helping
people make great decisions?
I don't think so.
The whole financial
crisis really
was caused by-- it started at
the bottom of mortgage brokers
who were being paid for
each mortgage they issued,
and they didn't care whether the
borrower was capable of paying
the mortgage back.
And he wasn't really giving
the borrower very good advice.
And all the way up the
chain, that worked.
Marriage is a pretty
important financial decision.
Hardly anybody goes
to an expert for that.
And across the board,
there's simply no evidence
that as we raise the
stakes, people do better.
So let me hide that for a
minute and just tell you
a story about a dinner
I had with Amos Tversky,
one of the two
psychologists I mentioned,
and a guy who was
a colleague of mine
for a while at the
University of Rochester,
and then later moved to the
Harvard Business School--
a guy called Michael Jensen.
And Jensen, at that time, was
a hardcore rational economist
and hardcore believer
in the efficient market
hypothesis in finance.
And Amos decided at that dinner
to have some fun with him.
And so Amos started
out by asking
Jensen to describe the
decision making of his wife.
And Mike regaled us with stories
of his wife's pathetic decision
making.
And then Amos moved on
to members of Congress,
his dean, his students.
And basically, anybody
Amos could name,
Jensen thought were
economic dolts.
And then Amos pulls the rug
out from under him and says,
so Mike, here's the
thing I don't get.
Everybody you know
you think is an idiot,
but the agents in your
models are geniuses.
What gives?
And Mike was nonplussed
by this question.
And he said in a kind of
condescending manner, Amos,
Amos, you just don't understand.
Now, Amos Tversky is
probably the smartest person
I've ever met.
There's hardly anything he
didn't understand or couldn't
understand.
So what is it that Jensen
thought he didn't understand?
He didn't understand markets.
And Jensen launched
into what, in the book,
I call the invisible hand wave.
[LAUGHTER]
Here's the way the invisible
hand wave argument goes.
Yeah, in your laboratories,
people misbehave,
but in markets-- and
then it's my claim
that no one has been able
to finish that sentence
with both hands still.
It's simply not
possible to do it.
So what exactly is
going to happen?
Suppose that I choose
the wrong career.
I probably did
becoming an economist.
And what can you do about it?
You can't short my career.
If I married the wrong spouse,
you can't short our marriage.
And if I don't save
enough for retirement,
then you can't short my
rounds of golf when I retire.
So there's no way,
really, for the market
to correct the decisions
that you make on your own.
But for many economists, this
invisible hand wave argument
was quite compelling.
And so I would say we
were stalled at some point
and needed to go out and collect
data in markets where people
are misbehaving and try to show
that meaningful stuff is going
on.
So the line in bold is
really the key point,
which is it's much
easier to make
money catering to
people's foibles
than it is in educating them.
So the last I looked, $27
billion of extended warranties
were purchased.
That's almost always a
mistake for anything small.
So people are making $27
billion selling those.
No one is making any
money convincing people
not to buy extended warranties.
I've been saying that for years.
I haven't made a
nickel off of that.
So here's another kind
of dismissive argument.
There was an article written
about me in the Chicago Alumni
Magazine.
And in good University
of Chicago style,
they go talk to my
colleague, Gary Becker,
whose views on this are
approximately mine multiplied
by minus 1.
And he said about
behavioral economics,
"the division of labor strongly
attenuates, if not eliminates,
any effects caused by
bounded rationality.
It doesn't matter
if 90% of the people
can't do the complex
analysis required
to calculate probabilities.
The 10% of people who can
will end up in the jobs
where that's required."
So I call this the
Becker Conjecture.
And basically, he's
saying, don't worry
about what Thaler is saying.
In places where
it matters, people
will be good at this stuff.
So one of my students
and I decided
to investigate this
hypothesis in a high stakes
situation-- namely, the
National Football League.
And I think we'd all
agree this is high stakes.
The owners are billionaires.
In fact, they have
a spare billion,
which is approximately the cost
of buying the cheapest team.
And they have payrolls, their
players, over $130 million
a year.
So I think we'd all agree
this is high stakes.
Do things work there in the
way they're supposed to,
or can we find misbehaving?
And the market we chose to study
is the market for draft picks.
So as I'm sure most
of you know, teams
pick players in an annual
draft in which the worst
team last year
gets the first pick
and the winner of the Super
Bowl gets the last pick,
and then they repeat
that for seven rounds.
And there's a market for picks.
And we estimated the
price for those picks.
And you can see, we estimate
it with great precision.
I'll show you why in a minute.
But the first thing to
notice about this curve
is that it's really steep.
It says that the value
of the first pick
is worth about five
times the value
of the first pick
in the second round.
So our question is,
is that correct?
Is that market rational?
If you know some
finance, it's like asking
when Black and Scholes
invented the option pricing
model, if they had made a math
error, what would've happened?
So if you've ever
played with data,
you know life
doesn't normally look
like this with a curve that
fits the data almost perfectly.
We thought maybe we had
discovered Newton's fifth law
or something like that.
But instead, what
we had discovered
is something that,
in the league,
they referred to as the chart.
Somebody at the Dallas Cowboys
about 25 years ago-- the coach
there at the time,
Jimmy Johnson,
said, can you tell me what
various picks should be worth
so when people ask us to make
trades, we know what to do?
And he drew that
curve by free hand,
and then translated it
into a numerical table.
And that chart circulated
around the league,
and now all the teams use it.
So including this year, if you
looked at any of the trades,
they're almost dead on
the numbers in this chart.
So the first pick
is worth $3,000,
and it says that you could get
the seventh and eighth picks
for the first pick, or you
could get-- this is the-- well,
we can look up-- the
33rd pick is $580,
so you can get five or six
of those for the first pick.
So is that rational?
Well, I won't go through
the whole analysis we did.
But we calculated the value to
the team of each player picked
over a 12-year period.
And that's this top curve.
And you can see
they know something,
meaning that the
value of the players
declines as the draft goes on.
The first round players
are, on average,
better than second round
players, and so forth.
This line is how much you
have to pay those guys.
Now, what is the value
of a player to the team?
It's the difference
between those.
It's what you get minus
what you have to pay.
That's this line, which, you
may notice, is sloping up.
Let me reproduce
that curve here.
And in the bottom half,
I have the first graph
I showed you, which is
the market value of picks.
The top line is what
they're really worth.
So remember, you can trade
this first pick for five
of these guys, each
of which are worth
more than the pick you gave up.
This is major league
misbehaving for high stakes,
for people who should supposedly
should be in Becker's 10%
of people who get it.
The biggest stakes market
is the financial market.
And it's kind of
surprisingly the place
where behavioral economics
has had its biggest impact.
And in 1980, no one would
have predicted that.
In fact, Michael
Jensen, the same guy
who I told the story
about with Amos diversity,
wrote a sentence saying the
efficient market hypothesis
is the best-established
fact in social science.
Now, there are two aspects
of the efficient market
hypothesis.
One is what I call the no
free lunch component, which
says you can't beat the market.
And you can't predict the future
from the past, for example.
That component of the efficient
market hypothesis, I say,
is approximately true.
I say approximately.
I'm a partner in a
money management firm
that actively tries
to beat the market.
And so far, we can, but not
by a lot, and not every year.
So it's approximately true.
It's hard to beat the market.
The other part of
the hypothesis--
the more important part-- is the
assertion that prices-- I call
it the Price is Right
Component-- that prices
are equal to intrinsic value.
So the market value of
Google is what it should be.
Now, for many years,
efficient market advocates
had the comfortable
illusion that this part
of the hypothesis
was untestable.
There's nothing better in a
hypothesis than an inability
to test it.
And who can know, after all,
what the market value of Google
is?
Nobody can prove that
it's right or wrong.
So for a long time,
that part was not
really considered testable.
But we found ways.
So let me give you one
example, a recent example.
There's a mutual fund, a
closed-end mutual fund,
which means they sell an
initial number of shares,
and then the shares are
traded on the market,
so you have to go buy
shares on the market
rather than sending
money to the fund, which
means that the price can differ
from the value of the assets
that it holds, which
is already embarrassing
to efficient market advocates.
So one such fund happens to
have the ticker symbol CUBA.
Now, needless to say, it
has exactly zero investments
in Cuba.
It's not legal for
Americans to invest in Cuba.
And frankly, there
wouldn't really
be anything to invest
in even if you could.
Now, let me show you a graph.
The day that President
Obama announced
that he was going to
relax relations with Cuba,
the fund went from a 15%
discount to a 70% premium,
which means you have to pay $170
to get $100 worth of assets,
whereas you used to be
able to get it for $85.
Now, I can't tell you
what the right price
of the assets the
fund owns is, but I
can tell you you
shouldn't be paying
$170 for $100 worth
of those assets.
And whatever those
assets are worth,
they were unaffected by
what the President did.
It's another supposedly
irrelevant fact.
We've seen lots of
examples on a larger
scale of apparent violations
of this component-- the crash
in 1987 when prices
fell by 20% on a day
with no news, the tech bubble
in the '90s, the real estate
bubble in the 2000s.
Fischer Black-- one of the
inventors of the Black Scholes
formula-- in a
paper wrote that he
thinks markets are
efficient, meaning prices
are right within
a factor of two.
I think had he lived long
enough to see the tech bubble,
he would have realized
that to three,
since the NASDAQ went
from 5,000 to 1,400.
And I don't think we would
want to call that efficient.
If cars were priced right
within a factor of three,
I don't think we would call
that an efficient market.
So where do I think the
field of economics is going?
I'm calling for something
that I cheekily refer
to as evidence-based economics.
And again, you might ask
what other kind of economics
there might be.
And the answer is
fiction-based economics.
Because economic theory is a
theory of fictional creatures.
It might as well be
a theory of unicorns.
Econs don't exist, so
theories based on econs
are fictional theories.
And what we need is theories
that are based on humans.
So let me-- I'll
conclude with this
and allow you to just read it.
It's the closing
sentences of my book.
And I'll take whatever
questions you have.
AUDIENCE: So
traditional economics
uses mathematical models a lot.
Mathematical models
naturally need
to make simplifying assumptions,
like all math models do.
And they can get a
lot of neat stuff
by being able to run
around with formulas.
If you take away
that simplification,
you've obviously beaten
up on that simplification
of the rational actor.
Can you get to a simple
enough model to have any math?
RICHARD THALER: Yeah.
So you've put your finger
on-- look, economics--
and I talk about
this at some length
in the book-- economics was
behavioral until about 1940.
Adam Smith was a
behavioral economist.
John Maynard Keynes was a
great behavioral economist.
It has nothing to
do with whether we
should have deficit spending.
Just read his chapter
on financial markets.
It's a brilliant bit of
behavioral economics.
Then in the '40s and '50s,
the mathematical revolution
started.
And you're right that--
the reason why economics
got more and more
rational is because
of the bounded
rationality of economists.
So the easiest models to write
down are of people optimizing.
Because if you took
high school calculus,
you know how to take a
derivative, set it equal to 0,
and solve.
So that's optimizing.
If people are
human and emotional
and pay attention to
supposedly irrelevant factors,
then that simple
model will be false.
Now, can you build formal models
that capture actual behavior?
Yes.
One example is a
theory of decision
making under uncertainty
called prospect theory that
was developed Khaneman
and Tversky in 1979.
It's a much better
description of how
people make choices in
uncertain situations
than the previous theory,
expected utility theory.
And there are other-- so the
new wave of behavioral economics
includes theorists writing
down mathematical models that
try to capture some
aspect of truth.
What we don't have and will
never have, in my opinion,
is a new overarching theory.
If you want one
parsimonious theory,
stick with the one we have.
It's just wrong.
But we need to have
those theories.
I couldn't do behavioral
finance without efficient market
hypothesis as a benchmark.
And there's nothing wrong
with writing down those models
unless you start to
think they're true.
And so one of the reasons
we got into trouble,
into the financial crisis, is
that people like Alan Greenspan
believed the efficient market
hypothesis, and didn't think
there could be a
real estate bubble,
and didn't think that banks
would give loans to people who
couldn't pay the money back.
And he gave a famous
mea culpa speech
where he admitted this much,
but it came a little late.
Yeah, right here.
AUDIENCE: My question of the
efficient market hypothesis
was that it was about
efficiency of finding
the true price of things.
Is that roughly right?
RICHARD THALER: Well, as I
say, it has two components.
One is the price is
right, and the other
is that prices
are unpredictable.
AUDIENCE: And so on
that price is right,
I've had the impression
it was inherently wrong,
because in most big
financial transactions
I've been involved with,
everybody else is lying
or concealing information.
You're buying the
house, and the person
you're buying it from
carefully doesn't tell you
that they think the roof really
needs replacing, et cetera.
RICHARD THALER: Well,
so in financial markets,
the invisible hand wave
is like a windmill.
Now, a part of that is deserved.
So if you're buying
a house and you
buy a house with a
leaky roof, there's
no trade I can make
to exploit that.
I could try to sell you my
house, but otherwise-- now,
in financial
markets, presumably,
if stuff is going wrong-- so
when tech prices were soaring
in the '90s and companies were
selling for 100 times sales
because there were no profits,
so we couldn't compute price
earnings ratios-- we had to do
price sales ratios-- you could
short the NASDAQ.
But people started worrying that
NASDAQ was overvalued in 1996.
And if you shorted
the NASDAQ in '96,
you were broke before
you were right.
So financial markets are more
efficient than the markets
for houses and cars, but
still not perfectly efficient
for reasons I go into in
some length in the book.
AUDIENCE: So maybe this is what
you're already saying, but is
it simply that you can't
predict when people are going
to stop being wrong, and so
it doesn't matter if they're
wrong as long as they stay
wrong longer than you can afford
to disagree with them?
RICHARD THALER: Yeah.
I once-- yes.
So I once had a conversation
with George Soros,
the famous hedge fund investor.
And this was sometime
during the late '90s.
And I asked him whether he
would ever consider just
shorting the S&P 500,
because it certainly
looked to him and to me
like it was too high.
And he speaks and
writes cryptically.
And his answer was
much in character.
He said that, well,
you can bet on prices
returning to intrinsic value,
but you'll die of boredom.
And so that's the
fundamental problem,
that we can see--
I remember having
a discussion with my brother,
who lives in Scottsdale,
and he had just bought his
third property in Scottsdale.
And I said, diversification,
have you heard of that concept?
You now have three
pieces of property
within four square miles.
It seems like you have all
your eggs in one basket.
And he said to me-- and I'm
quoting him precisely-- no,
you don't understand.
Real estate prices in
Scottsdale never go down.
Now, if I could have-- if there
was a broker I could call,
short Scottsdale, I
would have done it.
But Bob Schiller, my fellow
behavioral economist,
tried to create such markets.
And there wasn't enough volume.
It turned out that that
effort was unsuccessful.
But as a result, there's no way
to short a frothy real estate
market.
So many people-- certainly Bob
Schiller was pointing-- well,
I could have tried
to show you a graph.
But if you plot real estate
prices from 1950 to 2000,
they're going up on a log scale
like it's a straight line,
1% to 2% a year.
And it's tracking 20
times rental price.
And then it just shoots
off into Never Never Land.
And in places like Vegas and
Scottsdale and South Florida,
it really just shot off.
And you had to either think
this time, it's different,
the world has changed, or
jeez, this looks like a bubble.
But if you think
it's a bubble, it's
really hard to take
advantage of it,
and it's really hard to know
when it's going to break.
And if you read Michael
Lewis's book "The Big Short,"
there are guys there
who had figured it out,
and they came very
close to going broke
before they were right.
And it was a matter
of a month or two.
And that's the
essential problem,
and that's why we
can have bubbles.
And many people think that
there are very frothy aspects
of the tech market now.
But if you think-- well,
Uber isn't listed yet.
But if it were
listed, and you don't
think it's worth $40 or $50
billion, and you sell it short,
you better be right
soon, because you
start getting margin calls.
A mic to the
gentleman in the back.
AUDIENCE: I had a question
about your NFL study.
RICHARD THALER:
Finally, football.
AUDIENCE: Football, yes.
I can see how compensation
is directly observable,
but how did you estimate
the value of the draft pick?
Because it seems like there
lies a lot of assumptions.
RICHARD THALER: We
had performance data.
For the graph I showed you,
we used pretty crude data,
like games started.
And we did that so we
could cover all positions.
But if you
replicated-- actually,
Nate Silver was with me at my
event last night in New York,
and he was telling me that
he had replicated our study
for quarterbacks using
the most sophisticated
new analytical measures
of quarterback ability.
And the result was
an exact replication,
which he thought was
too boring to publish.
I almost hit him on
the spot, and I'm
going to have a stern
talking to him later.
So we can measure--
in the places
where we can
measure performance,
we replicated it for wide
receivers, and it works.
AUDIENCE: Right.
I'm familiar with the work of
Football Outsiders and places
like that in
translating draft picks
to performance measures,
football performance
in various measures.
But you still then have to make
the translation to financials.
RICHARD THALER: Yeah.
So what you do is you
look at how much you
have to pay free agents
that are of similar quality,
and then we assume that
market is efficient,
which could be wrong.
So the way it works
is-- take RG3.
So his first year, he was
an all-star quarterback.
And we look, how much do
all-star quarterbacks in years
six through eight
of their contract--
how much do you
have to pay them.
Then his second year, he's hurt,
so he's a reserve quarterback.
Well, that's, say,
worth $4 million.
So we do that for every player.
There are details in the
book and in the paper
on which that chapter is based,
which is available online.
Go to my personal website
and you can find them.
Any other questions?
Yeah.
AUDIENCE: I have two, so
I'll pick one arbitrarily.
I have an economist
professor friend
who often likes to say that
if you don't have a model,
there's no point to
looking at something,
because you may
observe differences,
but you won't learn
anything unless you
can compare it to a model that
you think models something.
RICHARD THALER: That's
a very popular thing
for economists to say.
AUDIENCE: I've read
that a lot, so I've
seen that a lot of
economists believe that.
But this as if
idea that you gave
seems to be the
opposite of that.
It seems to say, who cares
whether the model represents
anything at all.
All that matters is
whether you see something.
RICHARD THALER: Right.
So I think that
models can hide a lot.
And I think the idea that
you can't learn anything
without a model is preposterous.
So there's no model
in our football paper.
But look at those graphs.
They tell a pretty
powerful story.
And there is an
underlying model,
which is that prices are
equal to intrinsic value.
And in order to get
the paper published,
we put in some Greek symbols.
AUDIENCE: But what I wonder
is how do you think--
RICHARD THALER: But they
were completely unnecessary.
AUDIENCE: How do you
think economists squared
these two ideas
that seemed to be
both popular and contradictory?
RICHARD THALER:
You use whichever
one comes in handy at the time.
That may sound
unnecessarily cynical,
but I don't think I
have a better answer.
Maybe ask your other question.
AUDIENCE: My other
question is this price
is right idea-- or the
price is wrong-- seems
to assume there is an
actual objective price
hiding under there somewhere,
whether people are getting
it right or wrong.
And I wonder what
you think of the idea
that there may not be the,
that the right price is just
defined by whatever
people happen
to think it is right now.
RICHARD THALER: Well, so
that's the view people had
when they thought they
could hide behind the idea
that they had a hypothesis
you couldn't test.
So one of the papers I
wrote was on an episode
during the tech bubble in
which the company 3Com happened
to be the owner of Palm.
And somebody already knows
the punchline of this story.
So Palm-- you will
remember if you're
old enough-- a Palm
Pilot was about the size
of a deck of cards.
And as far as I could tell,
it could keep your contacts
and also serve as a calculator
and maybe a calendar.
But it was considered
pretty sexy.
And eventually, they had a phone
that was the first smartphone.
Anyway, 3Com owned Palm,
but 3Com was not sexy.
And their stock price
was flat in a period
when any decent technology
company was going up 20%
a month.
And so they decided to
carve out Palm and make it
a separate company.
And so they did that, and
they had an IPO for Palm.
But they kept-- they only
sold 5% of the shares of Palm.
They kept 95%
within the company.
And then the Palm IPO
happened, and Palm-- anything
that looked sexy at that time
sold for a ridiculous prize.
But here's an equation.
I'm going to give
you an equation.
3Com shareholders each
got 1.5 shares of Palm.
Therefore, the
price of 3Com-- it's
an inequality, actually,
not an equation.
The price of 3Com must
be greater than 1.5 Palm,
because companies can't
be worth negative amounts.
But the day of the Palm IPO--
if you computed the stub value
of 3Com, meaning its price
less its interest in Palm,
it was minus $23 billion.
Now, that's wrong.
That's really, really wrong.
And I talk about in the
book-- when my co-author and I
presented this paper at
the University or Chicago,
we ended up getting into
a discussion of icebergs.
And my friend Gene Fama, who's
an efficient market advocate,
said look, these
are trivial examples
that you're showing us.
OK, so this is wrong, but the
rest of the market is fine.
And I was saying, no, this is
just the tip of the iceberg.
And I've studied a bunch of
these, like the Cuba example.
And I call them the
fruit flies of finance.
Fruit flies are not a
particularly important species
in the grand scheme
of things, but they've
been incredibly
useful in genetics.
And these special cases,
like closed end mutual funds,
and Palm and 3Com,
are where we can say
for sure the price is wrong.
And so the debate turned.
Gene was saying that this
was the whole iceberg,
and I was saying it was
the tip of the iceberg.
And it's a discussion we
continue to have to this day.
I think our time is up.
Thank you very much.
[APPLAUSE]
