Millions of people around the
world drive
with Uber every single week...
I like the freedom about it the
flexibility...choose when
and where they receive business...a
high demand period that Uber calls a
surge...
if you're not intending on taking
rides for a particular period of
time...
I like driving for Uber...
or drive the flat rate
and make a lot less money...
Audio playing
Keith Chen has studied how monkeys
and people including Uber drivers
react to financial incentives.
He's looked at grammar rules
and savings rates
and identified that what language
you speak inherently makes you
a better saver -
or not.
If these seem like widely disparate
intellectual
domains it's because Keith is a
behavioural economist
which kind of makes him a bit of a
David Attenborough of the business
world.
Keith's work offers insights into:
what makes some language groups
natural
savers; how much money will it
take to nudge Uber drivers out to
work on a cold dark night
and even how far away from our jobs
we are prepared to live.
He investigates our most base
emotions to find out what will
humans do for how much money
and why sometimes money does
not matter
at all...
From the University of Sydney
Business School this
is Sydney Business Insights.
The podcast that explores
the future of business.
Hello I'm Sandra Peter
and in this episode of Sydney
Business
Insights - monkey money business.
As part of the University
of Sydney Business School's Global
Executive
MBA program we met
with UCLA economist Keith
Chen who taught monkeys how to use
money
in order to better understand how
humans make economic decisions.
And so actually you know without
much
of a clue
or of a sense of well-founded
hopes that this was going to lead to
anything.
Some colleagues and I began plotting
to teach two groups of
monkeys how to use money.
Not just any sort
of monkey - Keith specialises
in academic monkeys - the kind
whose usual habitat is one of the
great
US universities.
Apparently Yale
and Harvard breed different
monkey personas.
One is a small
tamarin laboratory at Harvard
that has these pair bonding, very
social, almost kind of
socialist monkeys
and the other was these kind of
brown tufted capuchins
who lived at Yale
that were just the opposite of big
alpha male society, like very
hierarchical, you
couldn't ask for two more different
groups
of
monkeys.
Behavioural economics is a fairly
new branch of the
economics tree. Economists like
Keith invert traditional economic
theory:
conventional economics asserts that
people make choices based on a
rational
analysis of what is in
their best financial interest.
Behavioural economists study
people's motivations,
be they psychological,
cognitive,
emotional or cultural,
to understand how we make decisions.
Moreover they study changes in the
man-made
environment to see if those
adjustments
alter our behaviour.
So one of the things that really
jumps out
in behavioural economics is
that a lot of the most puzzling
behaviours we see in people
surrounding
money and the way that we think
about
money and the way that we use money
is kind of critical metrics
and or reference points for a lot
of our behaviour.
Keith's Ted Talk on how your mother
tongue directly influences whether
you are a lifelong saver
or a spender has been downloaded
more than 1.7 million times.
As a fluent Chinese
and English speaker, Keith
understood
the two languages treat time
- the past, present
and future - very differently.
This led me,
as a behavioural economist,
to an intriguing hypothesis.
Could how you speak about time,
could how your language forces you
to
think about time, affect your
propensity
to behave across time?
You
speak English, a futured language
and what that means is that every
time
you discuss the future,
or any kind of a future event,
grammatically you're forced to
cleave that
from the present
and treat it as if it's something
viscerally different.
Now suppose that that visceral
difference
makes you subtly disassociate the
future
from the present every time you
speak.
If that's true
and it makes the future feel like
something more
distant and more different from the
present,
that's going to make it harder to
save.
If, on the other hand,
you speak of futureless language,
the present and the future,
you speak about them identically.
If that subtly nudges
you to feel about them identically,
that's going to make it easier to
save.
Associate Professor Keith Chen is
from the nudge school of economics.
Nudge proponents
advocate the use of incentives to
push - or nudge - people into making
good decisions.
Things like governments
automatically enrolling
citizens into pension plans,
rather than running the voluntary
opt-in scheme,
have increased the number of people
with savings plans.
Likewise countries that require
their
citizens to consciously 'opt-out'
of being an organ donor have
achieved
donor rates over 90 percent.
Nations that don't apply
this forceful nudge tend to have
organ donation rates below 20% of
the available population.
It's about economically motivating
people to do
a good thing rather than punishing
them
for making poor decisions.
And companies also use nudge
principles
to maximise their returns.
Recently Keith has been advising
Uber
on its pricing policy - helping the
ride sharing platform to land on
that sweet
spot where drivers are making enough
money to justify being on the
platform
ensuring a ready supply,
so customers are satisfied
with the price
and the convenience.
But to start with I wanted to know
how Keith's monkey studies
contributed
to his understanding of human
behaviour.
Keith, you're an economist by
training
but you are not the traditional kind
of economist
and I'm a lapsed economist
and we economists tend to think
about
how people make decisions
and try to understand how they part
with their hard earned money,
how they spend their money. You
started with monkeys
at Yale?
Tell me a bit about that.
When I was in grad school,
really interesting
and powerful finding that came out
that
shaped a lot of the way I thought
about things.
So when I was graduate school,
learning all of these kind of
fascinating findings in behavioural
economics
I thought to myself well something
else that's really
striking is how universal they see.
This is not just a British
way of thinking about money
or a Chinese way of thinking about
money
or an American way of thinking about
money.
This just seems to be deeply human.
And the question was will does that
teach us that it's some fundamental
feature of our cognition.
But that's a little hard to
establish because
you could imagine that in some
sense globalisation means
we're all kind of subjected to the
same basic
market forces
and those market forces are pretty
common
across a lot of different settings.
So one thing that I
was curious about as well,
is it possible to think about
how universal our kind
of hangups and our behaviour is
towards
money by asking
a broader question which is saying
well one way of establishing that
the ways that we think about money
are universal across all humans
would be to say well it's even
broader than
that it's universal across a
wide swath of primates.
What Keith was trying to gauge was
how far along the evolutionary
chain did the association of money
as a measure of value hold true?
And so actually,
you know, without much of a clue
or of a sense of well-founded
hope that this was going to lead to
anything,
some colleagues
and I began plotting
to teach two groups of
monkeys how to use money.
Keith set out to challenge the
belief that humankind's
knack for monetary exchange
belonged to us alone.
Monkeys have a bottomless appetite
for sweet foods - so using
marshmallows,
apples and grapes as rewards,
Keith and his collaborators proved
that monkeys can learn to use coins
-
hard inedible metal- as something
of tradable value.
The monkeys not only understood they
could
use money to buy delicious food,
they also learnt to use money
to conduct more complex transactions
such as taking a gamble.
In his experiments the monkeys learn
to
express their preferences for risk
in that they might be willing to
forgo immediate
enjoyment of their treat
and subject themselves to the
possibility
of a loss or gain just
on the toss of a coin.
The monkeys even displayed human
like
reactions to unexpected events
lik price shock when grapes suddenly
cost two coins they would buy less
and price discounts - the apples
are half price, let's buy a few
more!
Interestingly though when we started
to investigate
whether or not these groups of
monkeys
when put in very very similar
situations to humans
in economic decision making
settings,
what was amazing was that there was
no statistical test we could run
that on some very very basic levels
could establish a difference between
these monkeys
and the median American stock market
investor.
Keith and his colleagues decided to
test how the monkeys
responded when they were deprived of
something they expected,
or hoped, to get - compared to how
they felt when they received an
unexpected
treat or benefit.
In economics this is the concept
of loss aversion - the apparently
illogical psychological response
that says we humans feel more
pain at losing something than
we feel joy when gaining something
of equal value.
For example...
How upset do I feel
when something that I planned to
purchase
for 10 dollars now cost 12 -
subjecting me to an unexpected loss
of two dollars. How upset does that
make me feel
vs. how happy do I feel when
something that I plan to buy for 10
dollars is
now on sale for 8 dollars
and subjecting me to an unexpected
$2 gain?
Study after study has shown that
psychologically humans feel the
pain of loss twice as powerfully
as the pleasure of gain.
Keith wanted to know how far
along the evolutionary tree this
loss
aversion travelled.
This appears to continue
up until the point when it just
doesn't.
Like pigeons
on this dimension are much smarter
than
us. They weigh that gain
and that loss equally,
it is just two dollars.
You should feel just as sad losing
two dollars
as you feel happy finding two
dollars
and even some very distant primates
like lemurs appear not to
show any kind of asymmetry.
What's kind of cool about those
results is it kind of
almost allows us to in some sense
pinpoint within 10 million
year range exactly when
this kind of way of
thinking about gains
and losses, this kind of loss
averse part of our psychology
appears
to kind of enter our cognitive
toolbox.
So does culture amplify that?
It's a little bit hard to put a
number on
how much culture amplifies
vs. just kind of reflects this
innate
tendency
but one thing that we do see in
some behavioural economics studies
is that
people when they get a lot
of regular feedback
can train themselves not to react
in irrational ways to these gains
and losses.
Professional poker players you hear
a lot about
teaching themselves not to steam.
They just took a loss, that's no
reason to lose your head
and then make a stupid bet on the
next hand,
and good professional poker players
teach themselves not
to feel that way.
Stock traders
with some large amount of experience
kind of show this.
John List, a prominent cognitive
behavioural economist
found that professional baseball
card traders, because it turns out
he was interested
in baseball card trading, don't show
this
after a good amount of experience.
What's interesting is that even
those professionals who have learned
to show this
in their professional lives,
when you take them
just 10 feet away
from what they're good at
immediately
start sharing this again.
So you know the stock market trader
if his hobby is to trade baseball
cards even
though he's trained himself not to
behave in this
particular way with his day job when
he's trading stocks
will immediately start behaving this
way
when someone offers him a less good
deal than
he expected on his vintage Babe Ruth
and vice versa the professional
baseball card
trader who is really really
dispassionate
and smart about that did some really
crazy
things in his retirement portfolio.
So what's interesting is that it
paints this picture of this
very innate,
ancient parts of our psychology
that of course we can
overcome with some thought.
That's part of being a self
reflective
human but that's still deeply a part
of us that we kind of revert to
quite naturally
and easily.
You've studied capuchin monkeys
and how they behave when
they get unexpected amounts of food
with regard to how much money
they've decided to pay that food.
How have you carried over some of
those insights
that you've gained there in your
role
at Uber
and what is your role at Uber?
I should say that this is in the
past
it was my role at Uber recently
I've returned fulltime to UCLA as a
professor of behavioural economics,
when I was at Uber one
of the main things that my team
worked on and thought a lot about
was
how to get both drivers
and riders on the Uber platform
to act dispassionately
and for drivers make more money
and for riders that take the most
convenient
ride for them.
It's interesting because we
often think about people
as needing kind of nudges
to work
and keep good work habits.
Let's take drivers for instance so
the
core of the business model was that
Uber only
makes money when the drivers make
money.
So Uber roughly speaking takes 20
percent
commission of all of the trips
that riders book.
So Uber as a platform
only really makes money when drivers
make
money and in that dimension
their interests were very well
aligned
with drivers and a lot of what
my team at Uber tried to do
was to help drivers make
as much money as possible in as
short a
time as possible
and maximise their hourly earnings.
And in doing so help drivers
appreciate the platform hopefully
spend more
time on the platform
and make both themselves
and Uber more money.
That was something that was really
important.
And you could imagine a couple of
dimensions on which
you could do that.
So one dimension is
trying to help drivers figure out
if they could preplan being on the
platform
what times of day would be most
useful for
them. A more important thing would
be
if there are times when the platform
really needs more drivers like
rush hour, a lot of people are
using Uber to get around downtown.
If you could give drivers a little
bit of extra
incentive to spend time on the
platform
at those times that was going to be
a pure win
for both drivers
and for the platforms.
Keith started to experiment using
the nudge
mechanism of surge pricing
to see how it might change the Uber
driver
behaviour - Uber drivers
being independent agents who are
under
no contractual obligation to
roster on.
So early on surge pricing
came into the picture as an idea
where when there is an imbalance
between the number of people who
need
rides, and the number of people
who would be willing to provide
those rides just
a flat fare.
We could increase the price to say
listen hey now is a time when
it would be really valuable to
people trying to get where they need
to go to have
you on the platform.
If you can spare a little bit of
extra time you should
you could imagine the presence of
surge
pricing doing kind of three things.
So imagine that the price goes up at
some point
in time in your city.
You could imagine that it brings
more people
onto the platform.
There's somebody sitting at home
they're watching
the basketball game
and you know their team's losing
and it's just getting depressing.
If for example the little
notification could pop
up on the phone and say hey you know
somebody just two blocks away from
you wants
a ride somewhere
and how would you feel about taking
them to the mall
and earning 20 dollars.
If this happens to be a time when
you're losing interest
in the game that might be a great
idea.
So it could bring new people onto
the
platform because we've just
increased prices
at a time when they're willing to
work for that price.
That effect turned out to be
relatively
small when people are not planning
on driving on the platform.
The price nudge touches Uber drivers
in different ways depending on their
current
location.
You know it takes a lot to get them
to
change their plans
and hop on.
What turns out to be much bigger
effects
and what surge pricing was much more
effective at was if someone's
already
driving on the platform they're
already
taking a few Uber trips,
let's say this is a person
who normally does three hour shifts.
If it's surging,
a very large observed effect was
that drivers were willing to take
a couple of extra rides at the end.
Hey if we're getting paid twice as
much
as we normally do like the surge is
2.0
a driver who would normally do a
three hour shift
is more than happy to do a five hour
shift.
It's really valuable because those
extra
two hours mean that the next day
that they were planning to do
another three hour shift they
could take that entire day off
and that turned out to be a pretty
big effect.
What surge pricing on a citywide
level was able to do was not
necessarily
at any given moment in time bring
more drivers
online but it was able
to give them extra incentive to the
drivers
who are currently driving to do a
few extra
trips.
It turns out a really big part
of surge was also
the ability to get drivers
both through the price mechanism
but also because it gave them a good
visualisation
to move a little bit to the parts of
the city where they were needed
most.
To appreciate the Uber story it's
helpful
to consider a few facts.
It started nine years ago in San
Francisco.
Uber now operates in 81 countries
and more than 600 cities.
More than two million Uber drivers
have carried out over five billion
rides and by some estimates
Uber is now worth around 70 billion
dollars. And at the end of 2017
it was operating at a loss of about
4.5 billion dollars.
One of the operating principles
driving
Uber's thinking was the service
has to be....
As reliable as running water.
But in order to do that sometimes
there are more people who want rides
than there
are people who are willing to
provide them
or vice versa.
So the search mechanism for riders
the thing that we always hope that
it would do
would be allow people who need
to go now to pay a little bit of
extra
to purchase that reliability
and when you're late for a flight
you
can always get an Uber car.
It's just that if it happens to be
at a time when
a lot of other people need those
cars too
it's going to cost you a little bit
of extra like 30% more
or something like that.
Uber customers also responded
differently to price nudges
and again Keith found that riders
behaviours defied traditional
economics.
One really interesting behaviour
that we saw was that
people responded very
differently to price increases in
that surge multiplier depending upon
the exact number that was framed.
So think about it this way,
1.0 surge that's exactly
the normal price,
1.2 you're paying 20 percent more,
1.3 you're paying 30 percent more.
And just like rational economics
would suggest 1.2, 1.3, 1.4, 1.5
demand just kind of drops.
A lot of people say to themselves
wow
I've had experience with this
platform,
it's 1.4 right
now. I don't need to go right now,
I can wait 15 minutes
and see if the price drops
and often it does within the next 15
minutes.
But in real life it's not just
about the money.
This does start to break down
or what you start to see is
interesting
psychological phenomena.
So 1.9,
and that's a really rare event,
Uber almost never gets to 1.9
but say a concert is getting out at
a stadium near you there's just not
enough cars to take everybody home.
The interesting thing is that
between 1.9
and 2.0 there's a very big drop off.
The amount that demand drops
off between 1.9
and 2.0 is six times
larger than the amount that demand
drops off between
1.8 and 1.9 even though the absolute
price change is exactly the same.
What's also interesting is that at
least
for new Uber riders
you actually see something that's
even more puzzling from a normative
economic
perspective when the price goes up
even a little bit more from 2.0 to
2.1.
For new Uber riders slightly
more people are willing to take
rides at
2.1 than they are 2.0 even
though at 2.1 those rides are
slightly more
expensive.
And what appears to be the case
is that imagine yourself taking
a ride you're preparing to go
somewhere
and a warning flashes up
and says hey listen you know we're
really sorry demand
is off the charts this ride is going
to cost
two times more than normal.
Do you accept or do you reject vs.
this ride is going to cost 2.1 times
more
than normal do you accept
or do you reject.
What's interesting is that what the
evidence
seems to suggest is that at least
for new Uber riders
that 2.1 seems
not arbitrary.
There must be a very fancy algorithm
going
on here 2.1 okay well that must be
the fair price, fine I'll pay 2.1
whereas 2.0 they're just they're
just
ripping me off.
Rides are twice as expensive right
now,
I'm not doing that,
what do you take me for?
And that's quite robust.
So a lot of what my team
at Uber had to deal
with were these interesting
quirks of psychology that meant
that the platform performed
a little bit differently than the
perfectly
rational,
highly tuned model of dispassionate
economic agents would tell you.
One initial proposal was
well we think people should actually
dislike higher prices
and like lower prices
and so if they're actually taking
more trips at 2.1 than at 2.0 then
we
think they're probably making a
mistake
and we should take some blame for
that.
You know we're presenting this
information in a
way that is eliciting this response.
One suggestion was well
if this is how people treat these
prices,
the fully efficient thing
that saves people as much money as
possible
while allowing as many trips to
happen
on the platform as possible would be
to avoid surging
2.0 at all costs.
As the search mechanism it says we
should
be charging higher prices to keep
the
service reliable you could imagine
us
holding surge 1.9
longer than we normally might have
and then at some point jumping
straight
to 2.1 instead of 2.0.
You could think of that as using
behavioural insights to basically
make decisions for new Uber
riders as if they were experienced
Uber riders. Help get a little bit
more quickly to behaviour that we
would see from
experienced riders on the Uber
platform.
In the end we decided that even if
that
might have made sense,
what deeper lesson this was teaching
us
was that this form of presenting
the surge price information was not
helping people make the best
decisions
in their own self-interest
and the right solution was just to
make the
price more transparent.
So what we started to do was instead
of saying this is
1.3 times the normal price 1.5
times the normal price, we just
started
estimating that price for you
and calculating it
and saying oh you want to go to that
museum
downtown.
The trip is going to cost you
$10.67. Oh I'm sorry demand is
off the charts, there are no cars
near you.
Now the trip is going to cost you
$13.62. How do you feel about that?
And when we started presenting
prices
in a much more natural way which
you know to tell you the truth we
should have been doing from the
beginning but in terms of the dollar
amount that it's going to take you
to get from A to B
you saw all of these kinds of
somewhat irrational behaviours
just immediately disappear.
It sounds like you time at Uber has
allowed you to gain some incredible
insights
and some of this is because of the
tremendous data
that Uber has
and it's allowed you to think a bit
differently
about some of the types of problems
that you could be investigating.
Where to next?
Wow.
I think as an economist who's
had the good fortune to work
with this amazing amount of data
and really insightful data about
people's
behaviour,
Uber and Uber's data have provided
researchers insights into two what
I think of as a really important
changes
in the way that we work,
in the way that we live.
So the first is that
the possibility that technology
like Uber, but I think
this is only expanding to other
areas
of the economy,
provide more
and more flexibility to people
and the use of their time
and how they spend it and how they
work.
So as a professor I'm actually
in one of the most flexible
professions
around.
I mean you know there's times when I
have to be in the classroom
and I have to be teaching
and those are pre-scheduled.
But besides that basically nobody
tells
me when I should be working,
when I shouldn't be,
where I should be working,
I can work from my office
or I can think as I go for a walk in
the park,
I have a lot of flexibility
in the control
and the use of my own time.
What I had started to realise was
that that's a tremendous luxury
that I shouldn't take for granted.
Most people who work in the economy
if
anything experience not just
less flexibility
but in fact negative flexibility.
So for example if you work in
the restaurant industry not only
do you sometimes not have control
over when
you work sometimes you can get a
call
from your boss that says I'm sorry
you
know somebody else is sick
and didn't come in for the chef,
you're going to have to come in
today.
So that's like not only a lack of
flexibility it's not only that you
don't control
your hours, you in fact don't even
control the surprises
and the surprises can be negative
surprises.
It's kind of negative flexibility.
What I think is interesting is that
technology
is allowing more
and more of the economy to move
in the direction that kind of
professors enjoy.
We're just going to have a lot more
control of our time
and the way that people respond
to that is really fascinating. Uber
and other kind of gig economy
platforms
are basically the first time in
history
that large swaths of the economy
can basically tap their smartphone
and turn on work
and then whenever they want to take
oout their smartphone
tap it again and turn off work.
That's a tremendous change
and I think something that is
potentially
revolutionary
and that we don't fully understand
the implications of yet
but the Uber data gives us a first
peek at what I think of as
relatively massive
change in how we get to organise our
own
lives.
There will be unintended
consequences of
even things like flexibility for
instance as much as Uber allows
for more flexible work,
it also has the ability to
incentivise
me to work maybe more than it's
optimal
for me or to get me to work when
I shouldn't be working simply
because of the
incentives that it can build into
the platform.
Yes certainly in a traditional kind
of world where your job
asks you to be there from 9:00 to
5:00.
In an ideal world just giving
you complete control over those
hours
can only make you better off.
It just gives you flexibility to
kind of make your
work kind of work for you as opposed
to the
other way around.
But certainly you could imagine
a transitional period where
people have to learn how to deal
with that flexibility.
And we all have experiences where
sometimes a little bit of rigidity
kind
of helps us.
We can set up arbitrary rules for
ourselves
about every morning having
to go to the gym between six
and eight am
and that rule kind of
maybe not being exactly the right
thing
in the middle of a snow storm
or in the middle of a heavy rain,
but we just kind of feel compelled
to stick to it because that kind
of rigidity actually helps us all
live our lives a little bit better.
I get the feeling that in the advent
of technologically provided
flexibility in how we all work
we're all going to have to become a
little bit more sophisticated
about the rules that we impose on
ourselves
in order to make that new world
just work a little bit better for
us.
Beyond Uber lies the yet to be
realised
era of self-driving vehicles.
Keith thinks autonomous vehicles are
coming
and their impact will be profound.
In both a really liberating way
and potentially a really disruptive
way.
Up to now professional drivers have
been
immune to the off-shoring
encountered in
other low skilled jobs.
Not only might autonomous vehicles
decimate these jobs
the impact could be huge,
not in the least because...
...long haul truck driving is over 3
percent
of the American workforce which is a
surprisingly high number.
And in more than 20
states in the United States it's the
most common job that
people who live in that state
report.
So I think that's going to be
extremely
disruptive, but hopefully
more efficient
and safer for the economy.
It also turns out to be a relatively
taxing
job both on people's personal
relationships
and their health, as a whole a
somewhat dangerous
job.
Keith also predicts that driverless
cars will change the way we work
and the shape of our cities.
A relatively persistent
pattern in the United States
has been that the average
commute for an American is
remarkably
constant across time at around 37
minutes. You build a brand
new highway that on average
increases
commuting speeds from 50 miles an
hour to
65 miles an hour.
People move just a little bit
further out
to that next beautiful suburb that's
just
been built and basically have no
change in their commuting time.
I think self driving technology may
be the first major technological
change that kind of cracks that nut.
I think I would be willing to
commute an
hour in the morning if
what I'm doing during that commute
is not having to pay attention to
the road
and drive
but instead could kind of sit back,
check my email,
check my Twitter feed.
It's a little bit more like allowing
all of
us to enjoy a train commute as
opposed
to driving. And I think it's got to
make car
travel dramatically more efficient -
when
cars are self driving, it's possible
for one lane
where all of the cars in that lane
are
self driving
and can be speeding along at 80
miles
an hour only a foot between
each car just bumper to bumper.
That's both much more efficient
because
chained cars like that save a lot of
energy because of minimising air
resistance
but also that means that that lane
can both experience zero traffic
setbacks
and also just carry a lot more
people.
And I think that efficiency is going
to mean a lot for people's
lives.
Is there anything you're afraid of?
Do you think that even a techno
optimist
has to pause
and say that with the advent of new
technology
at the very least users
we're all going to have to grow up a
little
bit in our use of technology in
understanding
its applications for how we live.
So we don't quite understand
the extent of it yet
but the degree to which social
media
and what is called like fake news
spreading
on social media had an impact
on the last US national election
I think, it is not fully understood
but appears likely enough
that we kind of really have to
understand how does social media
change the way
that we all participate
as citizens in a democracy.
I do think that we're going to all
have to become much much more
self-conscious protectors of
our own privacy
and stewards of our own data
in ways that we never have been
before.
So in principle
you could always have learned a lot
about
a person by sifting through their
garbage
every week.
But we were all kind of protected
from
that a little bit just by the fact
that
it's kind of inconvenient to sift
through somebody's
garbage.
And maybe I've been lucky
but I've never been interesting
enough of a person
to ever have someone want to sift
through my garbage.
But now thanks to technology
sifting through millions of people's
effectively kind of digital garbage,
using machine learning techniques
and using kind of big data
techniques,
has dramatically dropped that
barrier.
Because of that,
things that we didn't need to use to
worry about we may
have to become all a little bit more
self-conscious about.
Keith, thank you so much for talking
to us today.
Yeah thank you so much I've really
enjoyed it.
If you'd like to hear a bit more
from Keith
on his groundbreaking work
with money or monkeys,
you should definitely check out the
University
of Sydney Business School's Global
Executive
MBA program.
In the meantime here's a short clip
from
our fellow podcaster Stephen Dubner
from
Freakonomics.
Dubner is describing one of Keith's
more surprising findings,
when he was working
with the Yale Capuchin monkeys,
a discovery he did
not intend to make.
And then out of the corner of his
eye,
Keith Chen would see something
that later he would really regret
having seen which is one monkey
who is still holding on to a coin.
He goes over to the second monkey
and gives the second
monkey the coin
and now Keith Chen the economist is
thinking
what am I seeing in the monkey
economy
- is it monkey altruism?
Is it the repayment
of a loan of some kind?
There were a couple of seconds of
grooming
between the monkeys
and then yeah bam it's monkey sex.
It is the first recorded instance
of monkey prostitution in the
history
of science.
And then to show how thoroughly the
monkeys
understood the concept of money by
now after
the sex was over which was just like
8
seconds because they're monkeys -
after the sex
is over the monkey who had received
the coin for sex probably marches it
over to Keith Chen to buy some
grapes
with it.
People
like Keith Chen are part of the
group
of international thought leaders
involved
with the University of Sydney
Business School's
Global Executive MBA program.
To find out more our immersive,
experiential learning delivered
in multiple countries around the
world,
from the UK to India,
from Israel to the US go to our
website mba.sydney.edu.au.
This
podcast was made possible by
Jacquelyn Hole and Megan Wedge who
made this
story feel good
and sound awesome.
You've been listening to Sydney
Business Insights,
the University of Sydney Business
School podcast
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