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In this environment, when trust in authority.
Seem to have plummeted.
What has given people enough trust of each other that they are willing, for
example, to let a stranger come into their house to sleep?
So Paolo Parigi is a professor in sociology here at Stanford University,
and he is pioneering the research on the engineering of trust.
Paulo.
>> Thank you, Martha.
Thank you for inviting me here.
So what I'm going to talk today is an online experiment that we
are still running as we speak.
So I don't, I ha, I will have some preliminary data
but I hope this is interactive enough for this audience.
So, the engineering of trust field experiment.
We know that trust is a fundamental aspect of relationships and
it makes society function better.
Kenneth Arrow argued that lack of trust makes collective undertakings difficult,
and that an agreement to trust each other cannot be both.
Other have emphasized how trust makes solves the problems with complexity.
That is, if you trust others, you don't have to build complex
organizations to control the behavior of others.
And finally Fukeyama argued that lack of trust make organizations more complex.
And So trust is very important for
society, for relationships, for the economy.
Yet we see lots of variation among different nations, see this is just
example of different countries where somebody recorded the general trust.
So you can see that we have Denmark, Sweden, and
Scandinavian countries who have higher level on average of general trust.
This is just a standard survey.
You will find hundreds of these even going online now.
And you have other countries with lower levels [INAUDIBLE].
For example.
Now, this is interesting.
Perhaps it's important, yet
there is a lot of variation in different societies on general trust.
How much do we trust others?
The question that we want to explore with this project is,
can we, can we use technology to build interpersonal trust?
So now I'm not talking just about trust in institutions.
Trust in political processes.
Interpersonal trust.
Do a trust market.
How can a, thank you I hope, I hope this is reciprocated.
[LAUGH] So, can we use technology to build trust?
Now, perhaps here at Stanford you know the answer to be yes.
But let me tell you the motivation behind all of this work and
there was something that Martha was mentioning at the beginning.
So, we developed this research idea by studying the members
of a particular organization called Couch Surfing.
How many of you are familiar with Couch Surfing?
Perfect, that's exactly what I imagined,
because younger, younger people tend to be more familiar with.
Couch surfing.
Now what is this?
Couch surfing, for
those of you that are not familiar with, is what the name suggests.
People register on couch surfing, registered.
Couch surfing is going through rough times now.
So couch surf, you register as a couch surfer, and then you go and visit
the house of a perfect stranger, your host, and you sleep on his or her couch.
This is an example of one couch surfing community in London
during the period from 2006 to 2011.
That's the data that we had.
We had this data for all the communities all over the world.
This is just a representation of London.
You can actually go on YouTube and find more of these videos that we made.
I can give you the reference later.
So here you see each node.
This is a network.
Each node is a person and there are ties.
This means that these two people are friends with each other.
The ties are actually have two different colors, and
I'm not sure if this comes across.
They're blue, and they are red.
You don't see that many red tie.
The difference between blue and
red is a blue ties are people that knew each other from before.
So two friends are decide to join CouchSurfing.
And red ties are people who actually became friends because of couch surfing.
Somebody was the host somebody else was the guest and
now we can animate this video.
This is month recorded month by month from 2007 to 2000 I'm sorry from 2006 to 2011.
And you can see that the network is growing in density.
There are more appearing.
There are more ties appearing.
But all of a sudden, you will see that there is a, a large,
a larger number of red ties developing.
That is, couch surf, couch surfing is bringing people of different,
that didn't know each other, together, and to form a friendship tie.
Potentially.
It tries, that are, is based on trust as well.
So we animated all of this.
You see here, for example, here is the, a cluster.
See the red ties?
What the red ties are doing, they are burgeoning different clusters of
previously, ic, of previous existing You can see it pretty clear down there.
This dynamic, this is London but we, we had the same video for
many other major cities in the US and in the world.
And it's pretty the same.
It repeats itself all the time.
And it becomes, at one point it becomes all true, truly interconnected.
See the red ties right in the middle.
Now is this specific to cloud surfing?
Is trust just something that people who are strange enough,
crazy enough, brave enough to sign up on couch surfing.
Had they some sort of a predisposition.
To form friendship and trust relationships.
A great question, selection bias,
right, in other language I can say this is a selection bias.
We are now randomly assigned people to couch surf.
so, we decided after studying this, we decided to extend this analysis.
And to look not just at CouchSurfing but to many other
companies that are operating in the same space where CouchSurfing operates.
What is called a sharing economy.
The peer to peer economy is another, is another label used to describe.
Company like, Airbnb, Lift, TaskRabbit, Uber acts.
There are so many actually.
Most of them are here in the, in San Francisco, in the valley.
So our goal with this new study is to study participants in the sharing economy.
And to measure how their trust toward general others
changes as the result of participation experiences in these companies.
So this is not just trust toward another members of your,
of that company but trust toward general others.
We make two assumptions to carry on this project.
The first is that the trust levels of participants would have, on average,
remain pretty much stable.
During the period of our analysis.
Which is now.
Had not been for their participation in one of these companies.
And recruited participants are representative sample
of the sub-population of sharing economy members.
If we hold this, if these two assumption holds we then we can have a we
can estimate what is normally referred treatment.
[INAUDIBLE] Now I'm going to skip this part.
This is just to give you the research design.
This what we are doing now.
So there's a website and
then once you get on the website you are randomized into one of these three groups.
And we ask you to play games.
Depending on where you are.
The different waves.
We are finishing wave one and we are starting wave two.
Now the, the website is, looks like this you see the exchange is the name website.
Here is where you register.
First we measure your risk profile, this is a way to segment the participant.
Because trust is correlated with risk.
So it may be that certain people are more willing to form
trust relationships because they're risk takers.
So this is a way for us to cement that population.
And so we can measure the risk per file.
The game is actually fairly simple.
It's one of those things that takes forever to explain so
I'm going to skip this.
And then after we have segmented you on the basis of your risk profile,
we ask you to play the investment blame, investment game, classic investment game.
You're given a certain amount of money.
You don't know the other person, and you have to decide how much to invest.
And then that person, the other person, returns a certain amount.
By measuring how much you invest,
we have a measure of how much you trust the other person.
So this is more about the experiments.
Just want to say that we spend a lots of time to figure out
cute images of animals and flowers.
To aren't.
Do not buy this.
The results had actually took quite a long time. yeah.
Everybody has
their opinion of flowers and [INAUDIBLE].
You know, it's, pretty difficult.
So here, once you complete the experiment we give you a gift card.
The value is between $5 and $10.
We build a representative sample by connecting with the companies.
That are part of the shared economy some of them,
not all of them we hope to increase the sample so if you,
if you know somebody in one of these companies please advertise this study.
Now what I want to show you the interests of time,
is some of the descriptive statistics of the people who are now in our experiment.
We roughly have 600 people.
We send 5,000 emails,
and we had 600 people in the experiment that are completed.
But for these 600 people we are collecting lots of data.
Correct?
They, they come on the website, and we control that website.
So we see, we ask the question about trust or cannot trust.
You can see the majority of people think that others can be trust.
There is a difference in gender, and a significant age
also has an effect on on this variable.
Whether people can be trusted.
The risk profile is interesting.
You see that we have quite a large number of people here under risk adverse.
But overall it's pretty balanced.
It's not a bad distribution, it's pretty balanced.
Here we have some of the companies that members are participating.
In these in these experiments are coming from, or have leaked.
Some of them are familiar names Airbnb, Lyft others less, HomeExchange,
Freecycle, TaskRabbit is is another company.
What is impressive is that most of these people are participating in many of
these trading economies.
companies, not just one.
And so here, I'll show you some of the initial results.
This is how many of these companies are participating, too and
we can see that there is, dependant, so race has a negative effect,
African American, black, are less likely to participate in multiple sites.
We have, we see age also has a negative effect.
And cannot trust also has a negative effect.
That is if you believe, as we expect, that, that you,
most people cannot be trusted, you're less likely to participate in many of these,
sharing economy commitments.
We, which is, you know, what we would expect, there is kind of a validity there.
First that we are not doing crazy things.
now, what I want to show you here, because I'm running out of time, on these graphs,
but the interpretation of these graphs here because we
are analyzing how they're playing the games essential, essentially.
Here is an interpretation of the preliminary finding.
Participants do not differentiate between sharing economy and not sharing economy.
Members whenever they play this game and how they place trust.
They react to the strategies of the Roberts that they encounter in the games.
In particular, the amount return difference between sharing economy and
non-sharing economy whether or not they were defeaked, defected by the robot.
And so there's some implications that are actually interesting.
Experiences in the sharing economy would impact measures of behavioral trust.
Both within group trust toward other shared economy members and
general trust toward others.
So toward building a community and reputation,
reputation is a more important factor when facing a sharing economy players and
when facing a non sharing economy player.
I'm out of time. Thank you, and
this is obviously a large project.
We have a large research group.
It's not just me.
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