BRADLEY HOROWITZ: I want to welcome you all.
My name's Bradley Horowitz, I'm VP
of Social for Google, Social Product Management.
And I'm here today to welcome Sandy Pentland
to come in and speak with us.
I'm going to be brief.
I encourage you all to Google Sandy.
You will pull up his long list of credentials,
which include academic credentials, business
credentials, across many, many disciplines
for many, many years.
World Economic Forum, Forbes' Most Influential,
it goes on and on.
So I'm going to try to give you a little anecdote of something
that I don't think you'll get on the web.
Nothing too embarrassing.
I'm a student, both former and current, of Sandy's.
Former in the sense that I was a media lab Ph.D. student.
He was my adviser.
And current in the sense that I stay
closely attuned to everything that Sandy does.
As we were walking over here from building 1900,
we were sort of doing what old friends do,
which is play the name game and checking in
on all the old connections and friends that we share.
How's Roz doing?
How Stan doing?
How's Ali doing?
What about Fad?
And we went through them, and turns out
everybody's doing fine.
You know, many of you are here, actually in the second row,
in front row.
Many of you have gone off to become professors
at MIT or Berkeley or Georgia Tech.
And it was just so great.
And thinking about that for a moment,
I recognized that I was just walking
through one of the vintages, the sort of early '90s vintage
of Sandy's students who have all gone off to do great things.
And Sandy has consecutively piled
on top of that, round after round,
of graduate students and students that he has inspired.
And in addition to all of those lists of accomplishments,
one of the things that really touches me most
about Sandy and his work is that he's
such an inspirational educator.
He not only has enthusiasm for his own work,
he's able to impart that to others
and create generations of people that care passionately
about technology and science.
And it's just so great to be in the company, which we will all
get to share for an hour right now,
of a person who can inspire and lead that way.
And so with that, I'll hand it over to Sandy.
Welcome.
SANDY PENTLAND: Well, thank you.
[CLAPPING]
SANDY PENTLAND: Now, I'll have to inspire and cause
passion, which is actually part of what
I'm going to talk about.
So how does that happen?
So maybe this is good.
So I'm going to talk about two things.
One is basic science about who people
are, how we use electronic media, how we use face to face
media, how we evolved as a social species.
And then I want to move to how we can use this knowledge
to make things better, to have a more
sustainable digital ecology, to make government work.
Wouldn't that be amazing? [LAUGHS]
And so on and so forth.
And as Bradley mentioned, I do a lot of things.
I thought I'd stick this in.
I love this picture.
This is the boards back in the 1990s.
And that's Thad in the front there,
Thad Starner, who I think most of you know.
And then two other things I do that are of real relevancy
here, maybe three.
Is one is for the last five years,
I've run a group-- helped run a group at Davos--
around personal data, privacy, and big data.
And that's, of course, a very relevant topic for this crowd,
but particularly, going forward.
And the group includes people like the Chairman
of the Federal Trade Commission, the vice president of the EU,
Politburo members from China, et cetera, et cetera.
So it's a conversation between CEOs of major companies
and chief regulators and advocacy.
And I'll talk about that at then end
and where I think that things are going
and what you might want to do about it.
And I just joined the Google ATAP Board
because it used to be owned by Motorola,
but when Motorola got sold, they intelligently
moved the really creative interesting part over here
to Google.
And as Bradley mentioned, that started a bunch
of companies, which are doing well.
So the thing that I'm really concerned about,
the thing that's passionate, is making the world work better.
And a sort of story for this is about 15 years ago,
I was setting up a series of laboratories in India.
And, you know, we had huge government sponsorship.
We had a board of directors, which
are some of the brightest most successful people in the world.
And it was a complete disaster.
And it had to do with a lot of things.
It had to do with all of the sort of macho,
signaling charisma in the room with the board of directors.
But it also had to do with the way the government failed
to work, or did work.
And looking back on that, I can sort of
see that premonitions of the US Congress today.
All right?
So we went and visited the Indian Congress,
where we saw people throwing shoes at each other
and throwing cash in the air.
And we look at the US Congress today,
and it's somewhat similar, unfortunately.
So I want to make things better.
And what occurs to me is if we knew
how to make our organizations work,
then we could really do things.
Like we could solve global warming tomorrow
if we all knew how to sort of talk about it rationally,
come to a good decision, and then carry that through.
And the fact that that sounds like ludicrous fantasy--
oh, yeah, everybody agree.
Sure.
Not in our lifetime-- tells you just how profound
the problem is.
And that's why I think one of the most important things
that's happened in the last decade, something you've all
been part of, is this era of big data,
which is not about big at all.
It's about personal data.
Detailed data about the behavior of every person on Earth,
where they go, what they buy, what they say online,
all sorts of things like that.
Suddenly we could watch people the way,
say, you would watch an ant hill or Jane Goodall
would watch apes.
We can do that, and that has profound impact
that is hard to appreciate.
I made this little graph, which comes out
of-- inspired by Nadav's thesis here,
which is duration of observation.
These are social science experiments, and the biggest
medical experiments.
So this is like the Framingham heart study.
30 years, 30,000 people.
But they only talked to people like once every three years.
So the bit rate was like one number per month.
So you had no idea what these people were doing.
They could have been eating fried chicken all the time.
You don't know, right?
Or most of the things we know about psychology
come from down here.
This is a number of bits per second, duration.
This is a bunch of freshman in Psych 101
filling out some surveys.
And that's what we take to be social science,
political science, and medical science.
But now we have these new ways of doing things.
And so what in my group we've done
is we've built little badges, like you all
have little name badges.
And so we can actually know where you go
and who you talk to.
And we do this with organizations.
We'll track everybody for a month.
We never listen to the words, but we
do know the patterns of communication.
And I'll show you a little bit about that.
Similarly, we put software in phones,
and we look at the patterns of communication
within a community.
So I go into a community and give everybody brand
new phones.
And some of the people here have been integrally involved
in these experiments.
And we'll look at their Facebook activity, their credit card
record, their sleep pattern, their communication pattern,
who they hang with, who they call,
and ask, what do all this communication patterns
have to do with outcomes?
All right?
Do they spend too much?
What things do they choose to buy, and so forth.
And what you find from the big data,
and, of course, modern machine learning sorts of things,
is that you can build quantitative predictive models
of human behavior, which you all know.
But I think you know the wrong part.
And I'm going to tell you about the other part that's
much stronger than what you typically do, OK?
And so you can predict behavior.
And people go, well, wait a second.
What about free will?
That may not have occurred to you,
but that's a traditional thing to ask.
And I'll tell you a little bit about that along
the way because it turns out that a lot of our behavior
is very habitual, and that's the part
that we can model mathematically.
So the big picture here, and this
is part of the reason I got off onto this research,
is I go to places like Davos, and you
listen to the president of this and the CEO of that.
And when they talk about changing policy, talk
about doing anything, they use economics metaphors.
And the thing about [INAUDIBLE] economic metaphors
is that they're all about individuals.
So you've heard about rational individuals.
And everybody rags on the rational part.
I'm not going to do that.
I'm going to rag on the individual part, OK?
Because I don't think we are individuals.
What we desire, the ways we learn to go about doing it,
what's valuable, are consensual things.
So they actually are captured by this sort
of model, this independent model.
That matters because those interactions are
the sources, not only of fads and economic bubbles,
but they're really the social fabric that we live in.
So everybody knows about the invisible hand
that are led by the invisible hand
to advance the interest of society.
What that means is that markets are supposed to allocate things
efficiently and fairly, right?
If you've thought about it, you know
this doesn't work in the real world, [LAUGHS] OK?
And the question is, why?
So one of the things that-- there's
several things to say about this.
Most people think that this statement is something
that he made in "The Wealth of Nations."
And I'm just going to [INAUDIBLE]
"The Wealth of Nations."
But it's not.
He made it in a book called "Moral Sentiments," which
very few people read.
And it went on to say something very different.
It went on to say that "it's human nature
to exchange not only goods, but ideas, assistance, and favors.
And it's these exchanges that guide people
to create solutions for the good of the community."
So Adam Smith did not believe that markets
were socially efficient.
He believed that it was the social fabric of relationships
that caused the pressures of the market
to be allocated where they're needed.
And, in fact, a lot of mathematicians
believe now that this sort of despite Nobel prizes
about market efficiency and markets
being good for governance, it's not.
It's really-- you have to have the right sort of regulation
replication mechanism.
But this is another solution.
Adam Smith way back when said, if we could model the peer
to peer relationships, we could understand
how market things eventually resolve
to be much more efficient.
And that's what we're doing.
We're doing something that you could
call, sort of, Economics 2.0.
Instead of the individual approximation,
we're now modeling not only individual behavior,
but peer to peer behavior at the same time.
So that's the sort of big context for what's up here.
So let me give you an example of what that means.
So in a typical situation, you have some people
that influence each other.
So, you know, their political views, it's what's cool to do.
You pick it up from the people around you.
And we have a lot of evidence upon this
from the experiments we've done in our group,
showing that people's attitudes about, you know,
what music to listen to, what apps to download,
what spending behavior to have, is largely
predicted by their exposure to what other people do.
And you may not want to believe this
because it's not the rhetoric in our society.
And you guys are the last people to be saying this
to because you guys are like the best and smartest in the world.
But it really is true that about 50% of the variance
comes from these peer to peer relationships.
And we know that when we do incentives,
when we try to these-- CEOs and governors
try to set up governance schemes to make people do things,
they always talk about individual incentives.
That's part of this mindset that comes from the 1700s,
is that we're all rational individuals.
So we'll give this guy money to behave differently.
But when you do that, what happens is, of course,
you're putting that incentive in opposition
to the social pressure he gets from other people.
And if they're aligned, it works wonderfully.
But if they're not aligned, which happens all the time,
incentives don't work.
Individual incentives don't work.
And the moment the incentive goes away, you know,
you start paying them, they revert to what
the social fabric does.
So when you begin to think about this mathematical framework
that includes the social fabric, an obvious thing
occurs to you, which is that, well,
instead of giving one person the incentive,
maybe I can modify the social fabric.
What we have is exchanges between people,
and we have incentives being applied to the individuals.
And now what I'm going to do is I'm
going to modify the incentives between the people, OK?
And you can write down these equations just the way
you write down economic equations.
So this was in "Nature Scientific Reports"
just last year.
So you could write it all down, you know, with utilities
and peer pressure and externality cost.
And you'd discover something interesting, is
when you add these second order terms in,
you find that incentives that modify the interactions
are generically more than twice as efficient
as incentives that go to individuals.
Generically that way.
And in the data that I'll show you,
it's sort of four to 20 times more powerful.
So this is the thing that I want to really get across to you,
is that this sort of power of economics that has mathematics
and prediction is very limited.
It doesn't include anything that has strong peer
to peer effects, like bubbles, like fads.
But you can now write those down because we have enough data
and enough math be able to do it.
So let me give you some examples of what that means.
These are simple examples.
You can do much more complicated ones.
So this was, again, sort of done in Nadav's thesis.
We got a community, where we divided them into two pieces.
It's actually three pieces, but I'll just
talk about two pieces.
In one piece, we had a little app
that showed them how active they were.
And this was in the winter in Boston,
where people tend to just put the blanket over their head
and go away, right?
So we wanted them to get out and around and stuff.
And so we can show them their activity level,
and we can give them money.
So you can say, oh, if I was more active than last week,
I made $3.
Wonderful, OK?
But in the other part of the community,
we assign people buddies.
And you would be his buddy.
If he is more active, you'd get rewarded.
Not him, you.
Your buddy gets rewarded for you being active.
And what you do is you pick people
that have a lot of interactions with each other to do this.
And it sounds a little creepy, but actually, people
got into it.
Almost everybody signed up for it.
And what you got is you got everybody's, like,
looking at the other guy and saying,
well, are you being active?
Because they're reminded on their phone all the time,
and they're getting a couple bucks for it.
It's not a big thing, OK?
But remember that that incentive scheme, that social network
scheme, if I'm not incenting the individuals,
I'm incenting the network, is generically more than twice
as efficient.
In fact, in this experiment, the way we did it,
we found it was four times more efficient.
And if we'd done it the right way, if we went back and did it
again, as sort of a post hoc analysis,
we would have gotten eight to 10 times more efficient, OK?
Pretty interesting.
Oh, and one other thing.
It stuck.
When we ran out of money, we turned off
the money, no more incentives.
People kept being more active for the period
that we could observe, OK?
Because what we'd done is changed the social fabric.
We made being active a topic of conversation,
a topic of social pressure, of prestige, of interest.
And, of course, that has momentum to it.
Here's another example that's online.
We went to a canton in Switzerland,
which was trying to save power.
Keep power below a certain level because they
had hydroelectric power up to one level.
And beyond that, they had to turn on diesels.
And noisy, expensive, polluting.
And they tried educating people, and they
tried financial incentives, and nothing really worked.
And so we convinced them to sign people up as buddies.
So you would sign up with him as your buddy,
and if you saved energy, he would get a reward.
Now, the rewards were, like, pathetic.
The budget was $0.50 per week, OK?
And little dancing bears on your website.
It was, like, really stupid.
But what happened is that for the people that signed up,
you got a 17% reduction in energy usage.
Now, that sounds good, but let me give you the comparable.
There have been some places where people have raised prices
to get 17% reduction.
On average, you have to double the price of energy
to get that reduction in energy use.
That's the price elasticity curve.
So for $0.50 a week, we could get the same effect as doubling
the price.
This one is by a friend of mine, James Fowler.
Done with Facebook in 2010.
He's sent 61 million people a message
about getting out and vote in the election.
The sort of simple summary of it is it had almost no effect.
People didn't do it.
A few people did.
He could go back and sample people
and say, how many people did this have in effect.
He also included a "I Voted" button,
which would then show your face to your Facebook friends, OK?
This also had no effect, except with one particular class
of people, which is the people you had strong face
to face relationships with.
If you appeared in the same images with this other person
regularly, then among that group,
that "I Voted" button would generate two
to three more people voting.
So greater than yield one, a cascade of behavior.
So, again, what was happening there
is social pressure, the peer to peer things.
The centralized thing didn't do it.
It was peer to peer pressure between people
with strong ties.
And, in fact, actually, it's not captured
by the electronic network at all.
These are things that were outside of that, OK?
So that's an example that I think
is really interesting that you can build on.
And somebody mentioned, actually,
how many of you know the red balloon contest?
[INAUDIBLE] knows it.
[LAUGHTER]
SANDY PENTLAND: [INAUDIBLE] OK.
But so DARPA had this social network grand challenge
where we had to find 10 red balloons somewhere in the US.
And everybody tried using the economic incentives.
You know, you get some money if you
found a balloon a reported it.
We used something like this mechanism.
We're able to recruit what we estimate
to be about two million people in 24 hours, and won.
Again, not giving people directly the incentive.
In that case, it's a little more complicated,
but giving people these peer to peer things.
So that's cool.
Why do you think humans are this way?
Well, let me give you an example that I think really
tells us why the action is not between our ears.
The action is in our social networks, OK?
We are a social species.
We evolved that way.
Why?
Let me give you a really graphic example of that.
So this is a site called eToro.
It's a social network site.
On this site, people buy and sell dollars and euros
and gold and silver, and stuff like that, OK?
And unlike almost every other trading platform,
it's a social platform.
So I can see what every other person,
what every of these 1.6 million people are doing.
You can't see the dollar amount, but I
can see that they're shorting euros,
long dollar, leveraged 25, right?
One day contract, and I can see how much money
they made at the end.
I could see their return on investment.
So here are people playing with their own money,
substantial amounts of their own money, millions of them
all over the world, doing this very regularly.
Sort of average maybe one a day, that sort of transaction,
right?
A couple, maybe a couple days.
And we can make a graph of the social network.
And that's what this.
This is 1.6 million people.
These are the same 1.6 million people.
And wherever there's a dot, this person
decided to follow this other person.
And follow in eToro has a different meaning
than Facebook.
Follow means I'm going to take 10% of my money,
and whatever that person does, my 10% percent of the money
will be invested exactly the same way.
So this is follow with teeth, OK?
And this is the graph of following.
So these are people learning from each other,
looking at each other strategies, and trading.
And you see some people are going it alone.
They read the newspaper, they look at the wire,
they browse the web, then they trade.
Other people are in this orgy of social dialogue, right?
All following each other.
And, in fact, if you look at it, you
see that there are all these loops in there.
So, you know, I follow you, you follow him, he follows me.
Uh oh. [LAUGHS]
And what happens in this loop is that you
get very few new ideas.
It's the same ideas going around and around.
And this is the sort of thing that's
a precursor of an economic bubble.
That the question is, which of these sort of social strategies
gives greater return on investment?
Why are we a social species?
The way that people almost always analyze it
is greater information.
That these guys all read the newspapers and everything.
They have all the information in the world, OK?
These guys read the same newspapers and everything,
but they also look at each other.
What would you expect to happen?
Well, you can write down the equations here.
And what you can do is you can look
at the propagation of new strategies
across this population.
So when this guy comes up with a new thing to do,
how likely is it to propagate throughout the social network?
And in that way, you can quantify
the number and diversity of new strategies any one person sees.
These people will see about no new strategies
because they're all on their own.
These people will see very few new strategies
because they're all listening to each other.
It's the same thing around and around.
And these people are much more diverse.
If we look at that return on investment,
we get a curve like this.
So, again, this is a mathematical function
that has to do with the number of new strategies.
So the rate of propagation of strategies
through the medium, the social network.
If you look at the number of new things,
this is very low, this is very low,
this has many new types of strategies.
And this vertical axis is return on investment.
So this is, like, one of these no BS types of majors, OK?
Real people, their own money, doing it
on their own choice, making money, or not.
People who trade by themselves are market neutral.
You might expect that on average.
They hire the market.
They lose a little bit of money in trading costs.
People who are in these momentum echo
chambers don't do very well either.
And what isn't shown here is sometimes
there are crashes that blow them all up.
So they actually do pretty badly on the long term.
But people in the middle make 30% more money.
So this is not something that is in traditional economics.
What we're talking about here is a blend
of social strategies for learning
from other people, plus individual information.
It's the peer to peer interactions.
And probably the reason that we have a social species,
this learning from each other, is
because it has this much more efficient output.
And there's a big literature about this.
Don't just believe me.
This is a wonderful example because I can quantify it.
And every doc here is all in the trades
by millions of people for a whole day.
So this is, like, more data than you know, right?
And if I did just one asset class,
like dollars versus euros, it wouldn't have this spread
that it does.
It would be a nice band.
So as you get more diverse learning
from your social environment, your return on investment
goes up until you begin getting too many loops.
And then it goes back down.
Now I like this example because I think this example applies
to the government, it applies to making decisions in companies.
If you begin thinking about it, we're
all living these social networks,
and what we're trying to do is make good decisions.
Here, I'm showing you that a mixture of social learning
plus individual learning-- I can tell you a lot more about it,
it's in the book-- gets you better decisions.
And not just better decisions by one person,
this is better decisions of the entire 1.6 million people.
Now, that's a really different thing.
I should also mention that one of the things we
did with this platform is when we discovered that they were
in this echo chamber state, that's
not good for the platform or them, OK?
Everyone's going to lose money.
So we looked at the loop structure,
and we figured out how best was the optimal way
to break it up was.
And we gave coupons to key people,
small group of key people, that would cause this echo chamber
to break up in an optimal manner.
And that doubled the return on investment of the people
in the entire network.
And that lasted for about three days, four days,
and they went back to being stupid again.
But [LAUGHS] that's their problem.
We've done this sort of repeatedly.
We know it works.
So you can actually control the flow
of ideas in a network like this and improve
the average function of the people in the network.
It's a very different way of thinking
about things than the normal way because you're not
concerned about individuals.
You're not concerned about their education and their decision
making.
You're concerned about the pattern of learning
and the performance of an ensemble, rather
than the individuals.
So one of the other things that this big data tells us
is that this process can be broken up into two pieces.
And to illustrate that, I'll show this diagram that's
from Danny Kahneman's Nobel Prize lecture.
He's the father of behavioral economics.
And he makes the point that people
have two ways of thinking.
There's a slow way of thinking.
[INAUDIBLE] probably knows about thinking
fast and slow, very popular.
Slow way of thinking that's the serial reasoning that we do.
And there's this fast way of thinking,
which is associations.
You take the experience you had, you
say, how is this situation like my previous experiences?
Maybe you interpolate a little bit,
and you make your decision very, very fast.
This is a very old mechanism, 100, 200 million years old.
This is pretty much unique to humans.
Interestingly, this is the much better mechanism by far
if you have the right set of experiences.
If you don't have the right set of experiences,
this is a disaster waiting to happen because you're
going to associate the wrong things with the wrong things,
and follow right off the cliff.
And when I look at the learning that people
have from each other in these social networks,
I see a qualitatively different type of behavior.
So when I look at slow learning--
so this is a learning that people integrate
into their conscious representations.
So the new song you heard, the new fact,
the new product that came out.
People are very promiscuous about this.
It only takes one exposure to integrate that
into your ensemble of things you know about.
And this is a way almost everything
that you guys build is based on.
Oh, we're going to have more information, right?
But information is not behavior.
It turns out that to get behavior change, which
is what I call idea flow, you need something different.
So this is an exploration.
We are trying to find new possibilities and new facts.
But it's relatively isolated from behavior change.
You could learn about all sorts of things
and never change your behavior.
This is why it's hard to stop smoking,
this is why it's hard to stop overeating,
why all sorts of things are hard is
that our habits, our actual behaviors, that reside here
are largely independent of this.
Now, there's some leakage.
If you concentrate real hard, some early adopters, yes, it
does happen.
But as I showed with the voting experiment,
the transfer from here to here is very weak.
On the other hand, what is the case
is that if you see multiple people experimenting
with the same idea, people whom you have strong relationships
with, then you will with very high certainty
tend to adopt that behavior, OK?
So what you're doing is this social learning.
If I see for some of my peers that
doing this results in a better outcome,
then without even thinking about it, I'll begin to adopt that.
If I hear about it, you know, through email or on the web
or something, it's very unlikely.
We have a database of all of-- I can't
say the name of the company, but a competitor
bought it for $1 billion.
The social network for the inside companies.
So we have the deployment for over 1,000 companies in there,
using that social network.
And what we find can be summed up in an interesting statistic.
If you get invitations to join this intracompany social
network from as many as 12 people in a half an hour,
you're still unlikely to sign up,
unless those people are people you already
have strong relationships with.
If they're people you know face to face or people
you work with regularly, then as few as three invitations
makes it almost certain that you'll sign up.
So that's just like the voting thing,
it's what I'm talking about here.
Behavior change, which is what you usually care about,
has to do with this social reinforcement mechanism
that I call engagement.
It's community vetting of ideas and behaviors
that results in the adoption of a new behavior.
It's not the broadcast, in fact, that we often think about.
So let me show you an example of this.
So this is data from a German bank.
It has five departments, managers, development, sales,
support, customer service is the last one.
And this is all the email, and the red stuff
is all the face to face.
We get this off of little badges we put on people.
So you probably can track this stuff,
but you've never tracked the face to face stuff.
Nobody does.
And what we find is that the sort of punch line
is that the email, the pattern of email,
has very little to do with productivity
or creative output.
But the pattern of rich channels of communication
has a huge amount.
So I'll show you a slightly distracting thing first,
and then I'll tell you the real punchline here.
So these guys are going to do an ad campaign.
They're starting now where the boss sends out lots of email
to have lots of meetings to figure out how to do it.
During that time, nobody talks to customer service.
They deploy the thing, it's a disaster, and as a consequence,
they deploy it now.
And then they have all day meetings with customer service
to figure out how to fix it, OK?
So the real punchline, because we've
done some dozens of companies now,
is that you can see the pattern of rich channel communication,
and that predicts typically 30%, and sometimes 40%
of the variation in productivity of work groups.
30% to 40% is bigger by far than anything that you look at.
I mean, you'd have to like kill the people to get
that big of an effect.
And the mathematical formulation of it
is basically a probability that if I talk to you
and I talk to you, what's the likelihood that you
two also talk to each other?
It's those loops.
And it's this learning from each other,
keeping people in the loop, nice little mathematical
relationship, that predicts this productivity.
And there's another thing, so that's
that engagement I was talking about.
There's another [INAUDIBLE] exploration,
and that's the stuff that your boss tells
you is not in your job description.
That's going to talk to the people in sales,
or the janitors, or the people at the other company.
Just picking up new ideas, new facts, new observations,
and bringing them back to your work group
to bang against each other and see if they make
sense to do that social learning process, OK?
I wrote a paper for Harvard Business
Review that lays this out.
It's called the "New Science of Building Great Teams."
And it won Paper of the Year award, which is nice.
But it also won Paper of the Year
from the Academy of Management, which is the academic side.
And that's the first time, I believe,
that Harvard Business Review and the academic business guys
have ever agreed.
So maybe it's worth taking a look at.
Anyway, so that's companies.
Let's look at the real world.
So this is a company I helped found in 2006.
Sold to AT&T's mobile advertising
that's been out recently.
People moving around in San Francisco.
Big dots are the most popular places.
Maybe some of you guys have seen this before.
I like it.
I show it often.
Looks like a nicely mixed city, but actually,
if you analyze it, if you cluster people by their paths
and by their exposure to each other, what you find
is you find that it's a very segregated city.
There's these groups of people that hardly ever
are exposed to each other.
And then there's other groups within the group,
they don't know each other, but they go to the same places,
they see the same things, and they have the same habits.
So in other words, they have very strong engagement
within the groups and they learn habits of behavior from that.
Now, sometimes that's good.
So, for instance, sometimes it's sort of trivial.
It's like you might discover that one group here,
you get a fad for red dresses.
No particular reason.
It's just what people in this group do, OK?
In another group, though, what you
find is you find that they have a different attitude
about paying back credit cards than maybe you do.
And so they don't have such good risk scores.
Again, it's not anything that they thought about.
It's just what people in their group do.
They learn from each other.
[INAUDIBLE] George just, like, threw it away, got a new one.
Nobody came after him.
It's the smart thing to do, right?
And then the other thing that you find,
which is very important, is chronic diseases
vary by group because of behavior.
Chronic diseases are mostly a function of your behavior.
You eat too much, you drink too much,
you don't exercise enough, all those things.
And a lot of other things we don't know.
But we don't know why that particular group is
susceptible to diabetes, but we know
they're very much more susceptible than other people.
It seems to be that they learned bad habits from each other, OK?
So I'm going to give a TED talk in a little bit,
so I thought I'd put this in.
So what TED does-- and his idea's worth spreading, right?
Make his wonderful videos blast him out to everybody.
And what that's doing is it's increasing
people's exploration.
You got a million views of this little movie, da da da da,
right?
But it doesn't change behavior.
That's my prediction.
That's what I see in all of this data.
What changes behavior is all the peer
to peer communication that comes afterwards,
where you say, what do you think of that?
Another guy says, oh, yeah, that's awesome.
And then the third guy says, well, OK.
And you get this validation among your peer group,
and that's what leads you to actually change your behavior.
So if you like what I'm saying, if you think it's interesting,
talk to your peer group, [LAUGHS] right?
Maybe it'll change your behavior.
So you can actually do something interesting today,
which is you can use these ideas to map
stuff in the entire cities.
So this is mixing of different communities in Mexico.
And I should start it over again.
So the red stuff is where the most mixing happens,
and the yellow stuff is where very little mixing happens.
And if it's blank, we don't have the data.
So you can see on Sundays, there's very little mixing.
People stay home.
Monday, a lot of people come out.
So according to the things that I've told you,
it's this mixing between communities
that's the source of a lot of the innovation
and creative output in a community.
It's the banging together of ideas
that causes innovation and better social outcomes.
And you can now do this-- and this is just cell tower
activity.
This is not any personal data at all.
This is just at the level of cell towers.
You say, well, which parts of the community
do the people at this cell tower come from, OK?
You don't know the individual people.
But you can use this in interesting ways.
If I use that same method-- this is
something we did in the Ivory Coast.
I helped convince the carrier, Orange,
release all of their cell tower data for the Ivory Coast.
Ivory Coast is a very poor country
that also had a civil war.
So the government can't go in the northern half
of the country.
What they do is they have poverty statistics
for the lower half.
And using this method, you can fit the statistics
for poverty in the lower half and extrapolate them
to the upper half.
And the poverty that your measuring is interesting.
So this has to do with two factors.
It's this exploration outside the community and engagement
within the community, OK?
And so this MPI is a multi-factor thing.
And it's a combination of poverty,
but also life expectancy, crime, and infant mortality
because they all co-vary with one another.
So that's an example.
So you saw it with Mexico City.
You see it here.
This is something a former student
of mine, Nathan [INAUDIBLE] did.
He took all the data from councils in the UK.
So there are neighborhoods that are
administrative units in the UK.
And he looked at their socioeconomic outcome
index, which is, again, poverty, crime, infant mortality stuff,
and compared it to the land line phone records,
and measured two things, which are very similar to what
I call exploration and engagement
and generated this graph.
So when you get a community that doesn't talk to itself
and doesn't talk much outside of itself, all the babies die.
Well, not all the babies.
A lot of babies die.
When you get a community where they're richly integrating
into the rest of society and they talk among themselves,
very few babies die.
And, of course, this is a richer community,
that's a poorer community, that has
less crime, that has more crime.
So we've seen this now in several places.
We've seen it in England, we've seen it in Ivory Coast,
we've seen it in Cambridge.
And you can begin doing things with this.
So for instance, you can use this
to be able to predict GDP in cities.
So we took the GDP for 150 cities in Europe and 150 cities
in the US, and we measured the amount of banging together
of ideas that you got in rich channels of communication,
face to face primarily.
And we did that by using things like Foursquare
to ask, how often do people come together
from different communities and at what distance?
You can print a nice mathematical function.
It's the same form in Europe as it is in the US.
It varies by the density of the city and the transportation
infrastructure.
So if it's a very dense setting with a really good
transportation infrastructure, you
run into a lot of different people,
there's a lot of ideas banging together,
and you get a lot of innovation.
And so what this is showing is a measure
of this face to face engaging and exploration.
And this is GDP per head.
I put kilometer, I think actually that one is.
And you can see that it accounts for around 90%
of the variance, which in social science, is like a, you know,
law from above or something like that.
In other words, if you tell me the density
of the city and the transportation infrastructure,
and actually just need to tell me the average commute time,
I can tell you the GDP almost perfectly.
Similarly, if I tell you that mobility or the call pattern
in a neighborhood, I can tell you
the GDP, the number of infant mortality, and the crime rate.
Again, r squared of about 0.85 is like a law.
It's amazing, OK?
And that opens up the possibility
of doing cool things.
You could, for instance, change the infrastructure
to make more ideas bang together, right?
You could make it so it's easy to get around
than a place like New York or San Francisco,
as opposed to an incredible pain in the butt, all right?
And so, you know, we, at MIT, have done things
like this shared car, shared scooter.
I'm on the advisory board for Nissan
and helping them build what we hope
to be the first autonomous vehicle-- commercial autonomous
vehicle, all right?
Because we actually built one.
I helped to build one almost 20 years ago,
but it was never deployed commercially.
And so now we're going-- the CEO says we're going to do it.
So that's [INAUDIBLE].
But the thing I think is most-- so let me back up.
So this is a tool we built at MIT-- this
is Ken Larson and Ryan Chin primarily--
which allows you to simulate things.
So this is actually the area around MIT-- Media Lab.
That's the Media Lab there.
So they're built out of LEGOs.
And they have a laser range finder,
which scans the 3D thing.
And then you do some computation,
and you project back what ought to happen.
So, for instance, you can project back
how the wind will go, or traffic patterns.
But you could also project back things
like anticipated creative output.
How many different ideas are going to bang together?
Are all the communities little silos,
or are they actually mixing?
So this is something I'm sure people who
plan these buildings talked about all the time,
but you never measure it.
And yet, all the data say, that's
the source of real creative output.
It's also the source of getting everybody on the same page.
You have to mix those two things.
So where are we getting to build the interactive tools to do
that.
So another way which probably resonates more with this group
is this, which is trading ideas with each other electronically,
rather than face to face.
Rich channels, like face to face, are important.
But you can supplement them, you can extend them in various ways
by better data sharing.
And to a certain degree, that's why
people call personal data the new oil of the internet
because all those personal experiences are not
only good for learning peer to peer,
they're also good for advertising and lots
of other things.
And as I think I've shown you, it also
is something that worries a lot of people
and could be used in very bad ways.
And so about five years ago, I helped start group
at Davos, which included people like the Chairman
of the Federal Trade Commission, Justice Commissioner of the EU,
people from the Politburo in China, CEOs of things
like Vodafone, Microsoft, et cetera,
to be able to talk about these problems.
And so what we were looking for was a win-win-win solution,
where citizens would feel protected and be
able to see more value from their data,
from trading information, where companies would
be able to make money and where governments would
be able to provide public goods, be
able to make a more resilient society, cyber resistance,
and so forth.
And the nice thing-- this is the sort of diagram
they do at Davos.
While people speak, some incredibly talented artist
draws the discussion.
You can't really interpret it, but it's just
amazing to see them do it.
But the bottom line was is that the ideas that came out
of that, which are now enshrined in the Consumer Privacy
Bill of Rights in this country and the privacy directives
in the EU, and are being considered in China,
have to do with changing the way we treat
data, personal data, the sort of personal stories,
in a very fundamental way.
And that's to put much more control
in the hands of individual through notification
that people are collecting data about you, informed consent.
That means you show them what the data is, you describe
to them what the value they're going to get from sharing is,
and they opt in to that particular use.
And then they can opt out if they don't like it.
Auditing, to make sure that you did what you said
you were going to do.
And that's the retraction I've always talked about.
So that's where things are going.
In anticipation of that, I got DARPA to give us a lot of money
to build an infrastructure like this because they wouldn't
do much, except small experiments.
But in this country, I got [INAUDIBLE] in Italy
to be a living lab, to try to live in the future,
by giving citizens more control over their data,
using this infrastructure in conjunction
with Telecom Italia, Telefonica, the local government, and so
on.
And the experiment is to be able to say,
if people have a repository, a copy of all the data
that's about them, and that risk reward ratio is different
because they can opt into things,
and they know what they're opting into, they can opt out,
they could audit it.
You've changed the risk reward ratio.
Will the sharing go up?
Will companies make more money?
Will people do more sharing?
Will you get greater innovation through that sharing
of ideas and information?
Will government be able to do a better
job at providing public goods?
And so we've deployed this for the last year and a half.
It has many sort of technical elements.
Some of the ones that I'm proud about, as we've
talked MITRE, which is a government defense
contractor, to releasing something
called OpenID Connect as an open source.
Identity mechanism, that's really quite state of the art.
It's now being supported by MIT Kerberos Consortium.
Their intent is to make it a basic element,
the internet security.
If you are interested in this stuff,
you should take a look at it.
And also trust network technologies,
whereby it's a combination of long computer code
that give people more control and audit
ability over personal data.
An example of this, as you might be familiar with,
is the SWIFT network, which is for interbank transfer.
So the SWIFT network handles $3 trillion of money a day.
And as far as we know, it's never been hacked,
despite operating I think at 164 different countries
and with a lot of dodgy banks.
And it has to do with a marriage between the sharing
protocols and the legal contract,
which is a consumer contract.
It's not special regulations.
It's just contract law with close match between that.
So the communications begin with the offer
of a contract, the signing of a contract, it's auditable,
there's joint liability so everybody's watching out
for everybody else, and it seems to work.
The Visa network is a similar sort of thing.
Some of this is used in the federal government
for medical data sharing.
And one of the things that's particular about our solution--
and I know this is too quick, but I'm hoping that there's
people that are interested in this--
is that like the SWIFT network, we
don't provide sharing of data, except in extreme.
So generally, there's no reason to share data.
What you want to do is you want to share answers to questions,
and you want to share stories.
You want to say, oh, yes, I'm in San Francisco,
not, I'm at this lat long.
Or I'm in San Francisco today, not lat long at 3:15 PM.
So by providing the most, sort of,
general story elements possible, you
get the commercial opportunities,
the public good opportunities, with much, much less exposure
in terms of raw data sharing and unintended uses.
It's not a perfect answer, but it's a good answer.
So we've deployed this in [INAUDIBLE],
we're deploying it in MIT.
We could do it other places.
I'm going to tell you just a little bit
about the basic architecture, but then I
see I'm running too long, right?
I am, yeah.
So I won't tell you too much.
There is an architecture.
The cool thing about this is once people have personal data
stories, you can spin up applications trivially.
There's no collecting the data to get bootstrapped.
The data is already there.
So we're doing this with Mass General Hospital.
Were doing it with lots of other people where, you know,
when you opt in, day one, it has a whole history of you
because you've been storing all that data.
Anyway, so taking too long, sorry.
Big data, better life.
That's what we're about.
The book describes all this in detail.
Thank you.
[CLAPPING]
AUDIENCE: So you show these beautiful correlations
between some outcome for society and the number of interactions,
right?
And I'm wondering, is there strong evidence
of causality there?
But for instance, if we just tweak how much interaction
is going on in a given society, would that, in and of itself,
escalate it?
SANDY PENTLAND: So we know that it's causal in small groups
and in groups of 100 people because we've
done interventions.
We don't know that it's causal in big groups.
But you can look at, for instance,
the architecture of lots and lots of different cities,
and it makes a certain amount of sense.
You see the same pattern.
Unless, of course, [INAUDIBLE] fits so well.
Basically, what you're talking about
is sort of the Jane Jacob solution, which
is small communities with very good transportation
infrastructure between them.
A small community where you could walk around
gives you the strong engagement and culture and social support.
And the very good transportation infrastructure
lets communities interact with each other.
That's the way the design shakes out, basically.
So we think it's causal.
We don't know.
We're trying to work with cities to show that it is, all right?
AUDIENCE: So I work in privacy, and I liked your remarks
on modification and firm consent, auditing,
and the rest.
What do you think about actual automatic expiration of such--
SANDY PENTLAND: I think that's a great idea--
AUDIENCE: Would it increase the value over a long time,
or would it have a negative effect
to the value of society over a long time?
SANDY PENTLAND: I think it's one of these things
that you have to experiment with,
but I would expect it would increase it.
I mean, you know, the fundamental thing
is risk reward, right?
You want to know what's going on,
so you don't want to be spied on.
You want to have control over it.
And you want to be able to share an expectation of a return
without a lot of downside.
So expiration means that it's less likely to spread.
Auditing means that it's less likely to get stolen.
It's still will sometimes, but what it is really
is it's a framework that's a lot like our financial network,
our banking.
You know, you have these strings of numbers
that are the amount of money that you have,
and you give it to this thing called a bank.
And then you could look at it, and the federal government
comes and audits them, and you could
take it out if you don't like it.
And so we're talking about that with personal data, where
I put it in a bank, and I say, I will give it
to these people in return for these sorts of things.
And if I don't like what you do with it, I'll take it back.
And then the next objection is, wasn't this too complicated?
And yes, it is too complicated.
That's why we have things like mutual funds and 401ks,
and junk like that is because it's just way too complicated
for a human.
But you'd have the same sort of thing with personal data.
The AARP would have a standard way for elderly people
to share data that is deemed safe.
AUDIENCE: Specifically what I mean
is I opt in into something that that
opt in is not treated as indefinite.
SANDY PENTLAND: It should be absolutely.
The opt in should be part of the contract
that it expires, right?
AUDIENCE: Yes.
SANDY PENTLAND: Yeah.
AUDIENCE: Thank you.
AUDIENCE: I had a quick question about the trying to break up
the investment trading circles.
Is there a reason you chose an individual incentive
to try to break up the social networks,
or was that just the easiest way to try to break those up?
SANDY PENTLAND: So we tried several different things.
One was just giving-- first of all,
they're not individual incentives.
What it is, is saying, here's a coupon
if you follow that person.
So it's saying, build a link in the social graph.
It's not like you think about it more or something like that.
So we tried several things.
One was to give people random coupons.
So just pay attention to a random person that did nothing.
We gave people coupons to pay attention
to the highest performing people.
That did something.
That returns by about 2%.
And then we took people that were targeted to break up
the feedback loops, and that was the thing
that had this much larger effect, OK?
But notice that it wasn't an incentive
for any particular person to do well, all right?
Some of the people we gave coupons did less well, OK?
But I don't really care.
What it did is it broke up the loops,
and that the average performance went up higher.
[CLAPPING]