>> Li Deng: Welcome to the lecture by Professor Ray Liu. So let
me give you a very brief introduction to our speaker today. So
Professor Liu is a chair professor from University of Maryland.
He has been extremely active in a wide variety of research in
signal processing, information processing and in many type of
decision learning areas. So today, we get the opportunity to
very many to come over to teach us something that what he trying
to be beyond the traditional machine learning by incorporating
the decision making into part of the learning group. And a few
[indiscernible] about Professor Ray Liu is as follows. And he is
the leader in our signal processing society in IEEE, and he has
been doing a tremendous amount of leadership work, including
running some of our society's best publications such as Signal
Processing magazine, which he started [indiscernible] really sort
of excited our entire community and some related communities to
our Signal Processing. So without further adieu, I will let
Professor Liu teach us more detail about decision learning.
Okay. Professor Liu?
>> K.J. Ray Liu: Okay. Thank you, Li. I would like to share
with you something that we have been working on recently, that we
try to combine learning and strategic decision making together.
And I'm going to use some example, a few example to illustrate
the possibility that why this is important that we combine
together.
So first, we are in the era of big user generated data, and user
generated is a very important keyword here. It's not just
collected from this nature. Now, more and more decision and
activity in our daily life is being record, track, and share. So
this is lots of data [indiscernible]. And that is all because we
have this lots of more mobile device sensor, social network,
global cloud. And this abundant and still growing this real life
big data have a tremendous opportunity for us and for many
problem that we can study, okay, just illustrate that we can
study all the behavior, sentiment, propagation modeling, traffic
and there's many things we can study.
And up to this moment, machine learning have been a very popular
tool that we can study this, and it is trying to use reasoning to
find new and relevant information and given some this background
knowledge. There are many applications, I believe, that for the
audience they all know that, and this machine learning algorithm
have basically three elements. That is this representations,
evaluation and optimizations. And representation is for to
extract this feature from the data. And this evaluation is the
objective function that we built in from this knowledge.
Optimization is the method that we use to optimize the objective
functions. So there are three very basic element here. And it
seem to be more sufficient enough for many problem that we solve.
However, there's a limitation and constraint here. The
generalization assumption may not hold. What does that mean to
the generation assumption? That is for this machine learning
algorithm to hold, the training dataset had to be consistent with
the testing set, okay. That may not be true for many user
generated data, especially user behavior may change at different
time under different settings.
And also, most of the machine learning, this algorithm, they are
for single objective function, okay, from system designer point
of view, it's a single objective and cannot cover users'
different interests. And most of these application and in the
modern era, in media, social media and all of these, there are
many uses. They all have different objectives. They interact,
they make different decisions and these decision affect each
other's decisions.
Okay. So that is have different [indiscernible] and different
objective and user also they are rational and therefore they're
naturally selfish. They want to [indiscernible] their own
objective. And the knowledge on contained in this data is
difficult to be fully explored, and because of this data is the
outcome of users' interaction. So this is such a different
element and the user's interaction will [indiscernible] that each
user had to make their own decision that cannot be
[indiscernible] by traditional machine learning algorithm. So
that's why we need strategic decision making here. And game
theory is an ideal tool which is started as strategic decision
making. First, it is of multiple objective functions. Every
individual, every element, every machine all have their own
objective functions, okay. And this is naturally involved in
this concept of equilibrium. That would be the best for
everybody. Just like in our society. We have a rule and policy
and under this rule and policy, we all individually decide what
to do and we have a common equilibrium and users' local
individual interests will be taken into account.
So this is basically to try to explicitly take into consideration
of this user interaction and we need to come up with an optimal
decision and, especially, this is optimal decision for smart and
connected user. Why is a smart connected user. We can say that
they are intelligent, they will learn and [indiscernible] from
our environment. And especially that is a lot of this computing
paradigm has become distributed. Each one is different, has a
different individual learning, basically is for making optimal
decision. And learning and decision making, in fact, often are
coupled together. They cannot be separated. And due to what?
Due to network externality. So what does that mean, network
externality? Network externality, you will see again and again
here, that is the influence the others' behavior on one's reward.
Somebody else decision may affect my decision, okay. And just
for example, like we are going to a restaurant. And if on that
day, many people make decision to go to the same restaurant, and
then you have to wait for a long line. That affected each other.
So it is not we, each individual make a decision and that's it.
Many this modern social media environment and in many
application, the influence affect each other with this network
externality.
So we try to come up with something that up to this moment,
learning, machine learning has been an item by itself. There's
many thing have been developed. Strategic decision making is an
item by itself as well. That is using game theory to study all
these different principle. We have been using this for more than
a decade in communication, in security, in behavior. Recently,
we just, we found that in many applications that we see, these
two items need to be a bridge in between. And this bridge is
something that we are trying to build and we call this decision
learning is learning with strategic decision making.
So today, I'd like to use three example to illustrate that. We
have developed some tool and you will see later, and for given
the interaction with Li, I tried to pull out with three
particular item to illustrate. One is on this online marketing,
and that we will see that how information diffuse over online
social networks. And you may ask, how can that have anything to
do with decision making. I want to illustrate with you with some
example, although I may not get to the detailed formulation. We
have some paper [indiscernible] all the material.
Second, we'll use a Groupon. We develop a family of this new
game called Chinese restaurant game that learn combined with
Chinese restaurant processes in machine learning, with decision
making that will be able to study how customer learn and choose
the best deal. And their interaction affect each other.
And also, from crowdsourcing points of view, how can we design a
mechanism in such a way, in such a way that we can achieve the
desirable outcome, okay, and that is something that we are going
to see.
Okay. So I will start from this one. And I will
because
this, this lots of detail, I will try to point out to you the
conceptual idea and concept so that we can understand and discuss
more if we need to get to detail, we can discuss offline. So
first, motivation. Information diffusion. This is a Twitter,
this hashtag for 2008 U.S. presidential election. You can see
this Palin, Sarah Palin talk about this lipstick on a pig. So
when she say that, there is a star moment, and then everybody
tweet or try to post it or forward, and then there is a pig
intensity in the study [indiscernible]. And different phrase,
people track at different time. They always will be propagate
and then spread.
So now our question is can we understand this phenomenon? How
can we model this phenomenon. This is what online edification
and for many application I believe
[indiscernible]. Yeah, I
think that I ask my student to make sure we put this correct
here. Not an Apple one. So online advertisement and also
motivation for this like this malicious or erroneous information
suppression.
As you can see, some of right word eventually become the
completely wrong word just because something happen in the
middle. So we had to understand what is this process, okay?
Information diffusion problem. How information diffuse. User
exchanging information over social network. This is another
physical phenomenon. This is a physical phenomenon that we
simply drop water into a pond and then see how this water
diffuse. That is a physical phenomenon. But with the human
being's interaction, it is not physical phenomenons. If I tell
you something, if you are going to post it, you are going to tell
other, you had to make a decision first. If this is interesting
to you, if this is right to you, do you think other would be
interested if you say so, like enhance your reputation or get you
a [indiscernible]. After you make a decision that you had to
that you would do so, it is a decision processing bar.
So why we need to study this? We can grasp the dynamics of
information process and predict and control what is the start and
the peak time. Can we do so. For example, you have a new
device, you want to do an advertisement or it's a president
election. This is election day. Then if you didn't design this
information, [indiscernible] then the pig and the message will
happen after the election. So then why bother to do so?
Okay. So we can study this. Now, most of this decision is based
on machine learning to study this information diffusion. This is
no surprise. That is a well established tool. Analyze the
characteristics of information diffusion, that's one aspect. The
second is to model the dynamic diffusion process. The constraint
and limitation is very similar to what I had just illustrate. We
rely on the dataset and then we structure, okay, because machine
learning is you have to be given a dataset, structure and from
that we learn. It's not given what do you do. Totally ignore
the users' action, decision making. We do have a decision making
here and difficult to involve mechanism design to achieve desired
output. You will see the other example I will not talk about
here.
So why game theory? There is already illustrated, there is a
strategic decision making here. For information to diffuse, it
rely on other user to forward information and then rely on them
to make a decision. Now, we have seen that and it depend on
information is exciting, is friend will be interested or not, and
focus on. So we cannot ignore this aspect. We have
this is
the micro aspect of this user interaction. If we want to model
this well, we need to also be able to model that behavior and
also how can we achieve this mechanism design. And this is
[indiscernible] and we use evolutionary game to model. Why?
Because information diffusion is just something evolve. It's
just like evolution process. So we are using one branch of this
[indiscernible] information diffusion is whether to forward or
not. Somebody had to decide. And evolution process is this
mutant, okay, our gene suddenly change one day, and then the
question is whether to take this mutant or not. And to take this
mutant or not, sometime is not have a choice. Sometimes it's
nature's choice. It's a good day, everything [indiscernible].
Bad day, [indiscernible] can come, okay. So that will decide.
And so we had to understand this and social network, we're
talking about this social network have a great structure.
Therefore, we also use a graphical version of this to study. Now
what is that? But before we talk about graphical structure, let
me explain what this evolutionary game is. That is something
that has been used a lot in biology, ecological study. Now, to
study the population shift, okay, of certain mutant in gene and
if which one [indiscernible] in fact, we can also use that to
study let me explain to you, what this? This is first all the
equation. You can view this as total population. But populace
change of people moving to Seattle, okay, is in proportion to if
you live in Seattle, what [indiscernible] do you get minus the
[indiscernible] utility of all the utility you can get in other
city. If this is a much higher, many more people will move into
here. So it's a dynamic. Is it lower, people will move out
Seattle. So that is basically called replicated dynamic. That
use a lot in this evolutionary game. And this does have a stable
condition. We call evolutionary stable strategy. At the end,
there will clearly be a stable population in Seattle, okay, and
we have to understand that and we are using to use this concept
later on to study information diffusion, and this is the example
for that. This is the evolution of the deer with antler. Yes?
>>: I haven't meant this before so this may be a naive question.
Why is the average utility that suggests that I have no choice
where I move next? For example, I wouldn't move to a city I
really don't want to, but I would move to a city I would want to.
It should be a max.
>> K.J. Ray Liu: I think that is this. That is to study that
move to this particular city against the rest and average, okay.
You can also against one particular city. That is also fine.
>>:
Against the maximum user.
>>:
Against [indiscernible].
>> K.J. Ray Liu: Yes, so this is to study how population shift
may move to Seattle, okay, compared to the average sense. Depend
on which one you want to study, you can always change that, okay?
So now let's take a look deer antler, with antler, okay, and with
antler, suddenly, there's a mutant and suddenly the deer have
this and some deer without. So with and with, if they come
together, they have to file for female, and what damage they can
have to each other. If with and without, what advantage so with
no C would be larger. With is better. Why better? We
[indiscernible] and A and B really depend. Without, without may
be the best because they don't hurt each other. This, AA, may be
very bad. So anyway, replicate [indiscernible] will basically
say that what were the population of [indiscernible] X1 is with,
with antler. That really depend on the
what advantage that he
may get, the differential of the advantage that you may get with
this. And the equilibrium point is when this becomes zero. That
zero meaning is not changing, okay. That is the equilibrium
point.
Okay. So this is just a very small example to explain this
[indiscernible] dynamic. And we will see this result later on.
Graphical evolutionary game is now I have
that is
[indiscernible] population. Now I have a structure. I'm here,
and then I know [indiscernible], I know recall and we newly met
today so we have different graph structure and now we have a
graph structure we have to consider from [indiscernible] concept.
It's no longer random. So graphical evolutionary game say that
okay, now, we have this consider structure population. There is
a notion of fitness. And this fitness now those who fit well may
have some advantage or may survive or can [indiscernible]. We
will see some example. And users fitness depend on its own
fitness as a baseline plus this interaction with environment,
okay. With the environment, there's a selection, selection from
the environment, what would be my fitness. So anybody will have
the notion of a fitness. And this graphical evolutionary game
usually is analyzed under three updated rule. There's birth
death, death birth, and imitations. I will show you a few.
>>: [indiscernible] evolutionary game theory. And not
evolutionary game theory that what kind of examples like
[inaudible].
>> K.J. Ray Liu: This is the one place, because this you will
see we try to model how it evolve. There are other games, such
as cooperative games, noncooperative game, and potential game and
also this is [indiscernible] game. There are many different
games, okay. Depending on the scenario. Okay. Now, this is
strategy update. You can see we have a population. We are going
to use a few of these to model. I will not get to the details.
I just want you to get some feeling. What does that mean, this
birth death update. In this evolution, there got to be a
randomness there. So here, we have initial population. We
select birth is select proportional to the fitness. We will
calculate fitness. I will not get to the detail how we
calculate, okay? I had a paper if you want to show it. We
select one. We saw very high fitness. And then this is
[indiscernible] and we're going to select somebody for test. And
then within this, we are going to select its neighbor, randomly
select one to be dead. And then what does that mean, dead? Dead
would basically be replaced and copied from this particular one.
That mean this will eventually evolve in such a way it's a
mutant, that change.
[indiscernible] is test is randomly select, we select randomly.
Once we select randomly to select, this is according to die, but
after it's dead, which one had to copy then goes look for the
neighbor and select one with the highest fitness number and then
this guy is going to copy from that one. There's always
randomness there and there's always a selection. It's a
randomness and selection process just like nature evolution
process. Imitation rule, we randomly select one to imitate
unless select one and then this one is going to select whom to
imitate from and then have to [indiscernible] proportional
[indiscernible] to determine who is [indiscernible] going to
imitate.
So we are using this rule to model, in fact, in our paper, we use
this and this both to calculate, and I will show you this result.
Maybe before that. So graphical evolution formulation, a social
network. So now we have seen this graphical evolution game. A
social network is like this. The only difference from
traditional, this communication network is all this link are
abstract link. There's no direct link morning them, okay. This
can be
but we can foresee there is a graphical structure here.
And the information diffusion is going to diffuse among here and
every neighbor is going to decide what I am going to forward or
post for you or not, and that depends on the fitness will become
a notion if this information is something that I like, my
neighbor is interest, you have to quantify that. And then from
there, you decide you want to forward or not. So these two have
similarity. Graphical evolution in the game, social network,
they all have something that has graph structure. This is a
[indiscernible], this is a user in a social network strategy.
Strategy here in social network is if I forward you the
information, if I post the information, you also post. What is
the utility? This is the utility to determine that I mean if I
post and you also post, you and I will gain such a utility. If I
post, you don't that will be the utility between you and I. And
if I don't post and you post, if I don't post, you also not to
post, that's a utility again. So this is the case of the payoff
function [indiscernible] interaction between you and me. And as
we say, utility fitness and [indiscernible] point.
>>: So would it be fair to say that in this case, the neighbor
in terms of the note is the people that you have in your
[indiscernible].
>> K.J. Ray Liu: Yes, it could be that I have Facebook, right,
and all the people that I
>> But now according to this formula, the way you have here, you
don't model how this list is created and how do you decide
whether you want to ask somebody [indiscernible].
>> K.J. Ray Liu: That's a
I did not
okay. I skip
uniform structure and also
information. You will see
information here, yes.
graph structure, and in my study here,
all the detail. I study this all
the other one is this [indiscernible]
some information. I use Facebook
>>: [indiscernible] if I don't forward information, how my
neighbor can forward it? She doesn't have it? [indiscernible].
>> K.J. Ray Liu:
>>:
If I forward but I don't forward, okay.
If I don't forward.
>> K.J. Ray Liu: If you don't post, okay. If you don't post,
but for some reason your neighbor may not learn from you.
Because everybody have a connection. Your friend is not just me
today. You also have other friend. Okay. So I skip all the
detail. To calculate the fitness, everybody will calculate from
its own neighbor. Okay. So I will just show you some of the
result and interpret from the result the detail you can look the
paper that we have. We can calculate a dynamic of the
information diffusions. Also, we can find a final steady state
of this population percentages that agree to post. And so what
does that mean? Okay. Let's look at this as an example first.
This is the one that we used from Facebook. Here we have about
[indiscernible] user. Many ages. In total here, there are ten
big circle, okay, ten big sub group, okay. And this network is
scale free, okay. Scale free, meaning it satisfy the parallel,
satisfy the parallel meaning the number of user that with this
degree of this connectivity that increase, it would decrease as
the [indiscernible] gamma to the minus two or minus three. So
when you take a look, you'll be straight line. You'll see this
is almost a straight line, right. So this is the parallel.
Parallel meaning the more and more [indiscernible] with this
connection.
So when we verify this is that, now we use our game to model. We
have four different cases. Let's look at four different cases.
Case one is if I post, you also post, we get very high benefit
from each other. The last one is if I post, you also post, we
both may not have a good reputation, because that may be
something very bad, okay? We don't want to talk about it, okay.
And if I post, you don't post, it's very high, but the
if I
forward and you also forward, it's 0.6. Number three is forward
and forward is 0.4. So now let's see what happened.
dynamic of this situation. You will see more.
This is the
Eventually, it will go up. Eventually, it will reach to the
steady state. The [indiscernible] one is case one. In the case
one, eventually, you will find that almost 100 percent of the
population, everybody posts. Because if you post, I post, we
have very high advantage. The last one, almost zero percentage,
because the utility of that is not as good as all the other.
That is the worst so people will choose not to. And this one,
case two, is this red one because still has an advantage but not
as good as this, so there is a certain population, almost 70
percent, it doesn't say here, this one is almost 30 percent
people will post and follow with each other. That's a steady
state.
>>:
But in this case, the utility [indiscernible].
>> K.J. Ray Liu: Yeah, you quantify that whether determine how
that is, okay. Yeah, we have to determine how that is. Now this
is the one that we use this Facebook information, we had ten
subgroups and each subgroup, one to ten, we used this utility and
we can find that they all match with the simulation. One is our
theoretical result, one is the simulation result based on this
real data, okay. So yes if we are able to quantify this utility,
how do you quantify it
that's a different issue
we are able
to verify that the result is similar to the model that we have.
And from the extra data that we can obtain.
>>:
So you choose this zero [indiscernible].
>> K.J. Ray Liu:
We use all this, yes, yes.
>>:
[inaudible].
>>:
[indiscernible].
>> K.J. Ray Liu:
I'm sorry?
>>: Can you have [indiscernible] information flow just
[indiscernible].
>> K.J. Ray Liu:
that.
You will see, okay.
I'm going to talk about
>>: How do you know? I mean, can you relate somehow the extra
data has [indiscernible] using this as sort of [indiscernible] so
how do you know that [indiscernible].
>> K.J. Ray Liu: I think they are very close, okay, because this
is all the number
how many data that you use average, okay. I
will talk about that. This is just one now.
Now, the second we use is a memetracker data. This is for phrase
cluster dataset to track. You can see that this is larger
amounts of data. And let me explain what this memetracker is.
It is to track
this is a news organization when there is a
[indiscernible] post. We are not commenting on the story, I'm
afraid. Then some other news organization or news or website
will pick it up. We'll say this one. And at different time, all
the time, we are not commenting on that, you see. This one is
not there, but it's there. So we have all this, and then give
the other one this pickup a different kind. We are not
commenting on that story up to here. But this to pick up. So
this is a tracker that track how sentence and phrase may change
and may propagate and who propagate at what time. So now we use
this to do what? We want to verify the dynamics. You see that
because now we have this
we able to calculate and model the
dynamic. This is to track the word called GoogleWare. This
GoogleWare, okay, that and this is to David Archuleta, and this
is NIN and this is Tehran. You can see that the
this gray
color is the actual data and the red one, the record is the one
through our model. Through our model. And you say how do we
know the utility function? We don't know. So we do
[indiscernible], okay? We do [indiscernible]. From a
[indiscernible], then we are able to determine. This is the blue
one that is using is this in this [indiscernible] framework. As
you can see that we can really produce a very accurate result in
order to disquiet this dynamic.
>>: The idea is that you use this [indiscernible] which I call
training data and then the [indiscernible] whether the same kind
of parameter [indiscernible] that you learn from this data
[indiscernible].
>> K.J. Ray Liu:
>>
I think that
Is that the trend data or test data?
>> K.J. Ray Liu: This one. This one, I say one only had to be
used as training data, but I don't know which one is that, okay.
And the rest of them would just use to reproduce for other
different [indiscernible].
>>: Just a quick question about this.
network structure here?
>> K.J. Ray Liu:
>>:
What do you assume is the
This is uniform structure.
Everything
>> K.J. Ray Liu: Yes. In fact, it is [indiscernible] so it's a
uniform, but it's a scale free network. We assume it's a scale
free network and the detail of this equation we start with the
right in the paper, yeah.
>>: I thought that the physical data, this come from Facebook.
So memetracker is not
>> K.J. Ray Liu:
Memetracker, okay?
>>: [indiscernible] Facebook data probably give you the
structure.
>> K.J. Ray Liu: Yes, yes. And this is from the memetracker,
okay. It's popular. Now, use this. This is a question you ask
this one we do the experiment in a different way. We find these
five group of site in the database. Each group had 500 sites.
Ske will estimate the equilibrium for each group. And once we
find this equilibrium, then we will go over the corresponding
payoff matrix, okay. The payoff matrix go back to estimate. And
then from there, we are going to do experiment, okay, for based
on that data. We will estimate.
So what do we find here? You can see that group number one is
within this group, if somebody posts a message, about 19 percent
of the people this that group will post. Group number five, if
somebody posts this message in that particular group, 81 percent
of the rest eventually will post the same message. What does
that mean? That mean people in this particular group, they share
major common interests with each other. However, this one may
not. Therefore, what does this mean? If you want to particular
to [indiscernible] some political message or some marketing
effort, this one, they are more cohesive, so you may be able to
identify a group like that, and then you can be sure the message
you will be propagating more amount the user here. So we use our
method, we are estimate the equilibrium state and then we
estimate the corresponding payoff matrix. You can see that this
is a simulated result, the black one, from our model. The red
one is we estimate a payoff matrix and then we do experiment
based on the data. And this is average and variance. We also
plot the variance. So as you can see it's quite consistent to
what we have predict by using our
this [indiscernible] model.
So this is one example to show that even for information
diffusion, there is interaction between user where decision
happen and if we take that into account, we can have a very good
model to describe that.
Next, I want to talk about is a Groupon. Use as an example to
illustrate how customer can learn from each other and choose the
best deal. Okay. So what does this mean? This is sort of the
actual Groupon data that we collect. You can see that this is
the data that we have. We found that. I don't know if to use a
surprise or not. You know Groupon, right? Sometimes we will get
Groupon, we will get good deal. If this is a restaurant where
you go very often, if the price is dropped 50 percent, we will
buy. Many people will buy it. Sometimes it will tell you how
many. So if this is successful Groupon deal, we collect lots of
data and we do some average and we calculate Yelp ranking, okay?
We relate it to Yelp. We will find that it is Yelp ranking
decline. Is that a surprise? It was a successful Groupon deal.
You know why? What happened is this. You think it's a good
deal. He think it's a good deal. Everybody think it's a good
deal. Many people buy it. Now suddenly, you have quite a lot of
people who have this and then in the next few weeks, these people
will show up. So most of the time, many people show up and then
the quality of the restaurant decline because of such a demand
suddenly you have so many people.
>>: If the price is low, they may say that, well, they are
willing to wait a bit longer. So what do they give the rating
on?
>> K.J. Ray Liu: The rating is all of that. I think many people
[indiscernible] many people who buy it and then many people will
show up in this, therefore the quality during this particular
time before you can actually [indiscernible] go down. But you
know what? This restaurant, they don't want to see their rating
go down because the reason that many people want to go is because
they have a good rating. So that is why. This is a typical
phenomenon that have this so called negative externality, meaning
you and I, we all go show up at the restaurant. You and I had to
wait for one hour and we will never come back again. So that is
what happens.
Okay. So this is learning and this is decision making. So we
want to formulate a list in terms of this Chinese restaurant game
problem. Chinese restaurant problem that is very standard this
machine learning use a lot in machine learning basically
[indiscernible] processes where infinite table and each one with
infinite seat. And people have to come in and choose, decide to
a new customer come in, decide to take a seat in one of the open
table or open a new table. Okay. If you had not been in Chinese
restaurant that would be more difficult to explain. But
especially this is in the old [indiscernible] Chinese restaurant,
it's all the roundtable, okay. So these are all nonstrategic.
Now we formulate a game called Chinese restaurant game. We try
to introduce the strategic behavior with decision making. So
what is this? Let's take a look at this first. Hm.
>>:
[inaudible].
>> K.J. Ray Liu: Okay. I don't have [indiscernible]. We put it
into these
I had [indiscernible] from the first one, from the
first customer come, the first customer can choose a table to
sit. And then based on the second customer can come in and also
can choose a table to seat. And what's the problem with that?
Our model is this is a
the same as the example I had used
before. If you sit in a table with lots of space, you are very
comfortable, okay. There's too many people there and you are not
enjoy your eating. So when you come, you decide you want to open
a new table, you want to sit in this big table, you want to
choose a small table. And what happen is when you come, you only
see two people there. Then therefore, you have to be able to
predict, through learning or whatever that eventually, how many
people is going to sit where and therefore, at this moment, what
would be your best decision to choose which one, okay. So table
size, here we have a table size that is X and theta is the system
state, is the restaurant state, meaning this guy has spent 200
something dollar to [indiscernible] the restaurant, only spend
one dollar. That is the system state of that. The customer has
signal. The signal is all the [indiscernible] happen there. You
have all this signal that you know, okay. And some customer may
request the same table, of course, then that would be negative
[indiscernible] externality. Meaning what? When we have more
customer in one table, less space for each other. Then it's a
negative effect. So through this, question now is what will be
our best decision? That is the same as I can say now, because
later on example, it's just like you to an airport and you turn
on your iPhone or Microsoft phone, Windows Phone, whatever you
turn on. So many this selection. Everybody select the one with
the highest power state, right? You know what? Nobody can get
connected, although there's the [indiscernible] one. That is the
negative externality when you all come together, you cannot. So
what is the best? And you say this is a typical example. Your
decision affects my decision. My decision is not independent of
you. When you make decision, and if I make decision, if we just
look at our own [indiscernible], we consider how we mutually
affect each other, it will not be the best decision. Okay.
Sequential decision making. Everybody comes sequentially.
Therefore, we have the information observation space. Action of
previous players that is for the NIJ is number of customer and
table J when the customer I come. So that mean when the customer
I come, there are so many customer on table one, so many customer
on table K, okay? And also, previously, we had already had so
many
we already had [indiscernible] customer did arrive so we
can see. We come in, we can see [indiscernible] there and we can
know that.
Oh, [indiscernible] is here. Okay. So the first one arrive.
Second one, choose that, and maybe number three choose. Okay.
So that is what happened to choose. So now we have this signal.
Signal, we assume, if it is perfect, meaning I can see
everything. I know everything. Everything is so precise. Then
it's become very simple. We have equilibrium theorem to show
that we do have equilibrium, okay? We have given customer set of
that many and so many table, this is the Nash equilibrium. That
means the utility of how many users choosing table X and then
given the size of this table X and the size of condition, that
would be better than whenever one of these move to the other
table Y. Move to table Y had no other advantage because this is
bigger. That is equilibrium. We so that this is [indiscernible]
very simple. Okay. You are going to have so many so we can
study from the first one, but first this put into table one and
the next put into table two and so on. Now it's equilibrium.
The signal is perfect.
However, in real life, signal is not perfect. It's not perfect
learning have to involve, now we have decision making. Now we
have to combine with learning. So we had the signal with rumor
we have condition on the state of the system. We can use
[indiscernible] whatever we can that is to learn the belief. How
do we learn the belief. Now we don't see signal clearly. There
is a belief we have to estimate a belief and everybody, based on
the [indiscernible] making decisions. That's a typical learning
thing and then we have to also couple with the decision making.
Okay. So here we do have the best response in the Nash
equilibrium. The best response that is when a user choose table
when a
when a user I come in, he choose table J and then given
all of this previous user the signal he had and the history, what
is the best this expectation it can get. And this best
expectation, because of this belief is distribution so we have to
average out through this belief then that would be our best
result. And how do we calculate this? Very simple. Now we have
all the [indiscernible] we have to do backward induction, because
everybody depend on the future information. So when the last one
arrive, we can, if we want to get optimal result, there's no
future, from here we know the result and then we do backward
induction to calculate everybody's best decision.
Okay. So now I want to show an example here. This is a typical
setting. We have two restaurant, one high quality restaurant,
the other one is a low quality restaurant. This low quality
restaurant, the quality factor increase from here to here. The
average utility of one of the customer and that's the other
customer. So you can see that the best response is the one that
we propose. We always have the best, and this may not be the
best example, okay, just before I came my student just give me
this one because I want to use Groupon to do an example. This is
particular one. The average utility of the best response is
almost the same as the myopic one. The myopic one is based on
what you know just currently. But in many situations, there is
[indiscernible] and however, this myopic one is not Nash
equilibrium, meaning people would be able to change it the state,
okay, they are going to change. And learning, this is the one if
we know the signal, we only know the signal that we can have if
we learn the signal, learn the signal meaning we try to learn the
belief, okay, we start to estimate a belief, but we don't take
into account of what, of the negative externality, okay. We not
considering the negative externality. So that is the worst.
That is basically what we observe in this Groupon. In the
Groupon, we see this rating going down. That is because of this.
It is the worst. Why? Because it didn't take into consideration
of other users effect. And therefore, the overall performance
can be very bad, rather than if we take into consideration of all
this effect together. Okay. So this one is to show based on
what we have formulated what would be the best strategy that we
have for new restaurant? For a new restaurant, we can see that
this is a deal price. If the deal price is going down, the
number of customer will be going up. That is for low quality
restaurant. For high quality restaurant if a deal price going
down, yes, number of customer will going up. That's all the
same. And this part doesn't change much.
Now if we look at this for low quality restaurant, if we look at
the revenue, if we look at the revenue, that when the signal
quality, meaning the advertisement or this rumor for why people
had come, if the signal quality tend to be not as good, okay, if
the signal quality is not that good for low quality restaurant,
you may have a higher revenue. And the best pricing would be
somewhere here. However, for high quality restaurant, if the
high quality restaurant want to have a better revenue, then what?
Then the signal quality had to be good. And then along this
line, you can have a very high revenue. Okay. So depend on you
are running a low quality restaurant or you're running a high
quality restaurant. They are different strategy that you can
use. You can maximize your own
>>:
Why is that low quality restaurant [indiscernible].
>> K.J. Ray Liu: Because for low quality restaurant, if a signal
is too good, the customer may choose not to come. They may know
that is a low quality restaurant.
>>:
Oh, I see.
So the quality tells.
>> K.J. Ray Liu: If it's a high quality restaurant, you want
many people know that is true. The more who know that
>>
[indiscernible] quality actually is the rating.
>> K.J. Ray Liu:
>>:
The rating, yes.
[indiscernible].
>> K.J. Ray Liu: Yes. Okay. So this is a summary of what I had
just said. And the whole thing is I want to show this one. It's
highly nonlinear. You can see it's highly, okay. It is not just
occur. It is indeed the whole outcome is highly nonlinear if we
combine this decision and learning all together. It's very
difficult to describe by using a very simple equations.
With this, we develop a family of this decision learning game,
starting from this Chinese restaurant game, and this dynamic
Chinese restaurant game we use to model this as a network
selection just like the one I mentioned, if you come to this
network. You come in to, you are going to select which network
to join and many people come and go. And you have many option.
The one with very high energy, very power and then some would be
less. Which one to choose. Indian Buffet game is when you can
choose not just one, you can choose many. This is
[indiscernible]. So we have a family of this that we have
developed that we can use. What application that we can have.
This is what we can see. This is typical application in social
computing. This is the one that such as in Amazon and Yelp,
where people sequentially write a review, okay, and this review
may affect each other's decision and this is something that we
see in Yelp or this is some the typical question and answer sign.
We had the question and answer and people can write. With all of
these, and not only in this, we can also have in YouTube and
other video. With all of these, we find something in common in
this social computing system. User will write sequentially and
then they will make decision on all of these. Whether to produce
a piece of content. And if so, what quality. And also, whether
to [indiscernible] this encounter or not. So that is for the
system that we see. So the sequentially. Second, there are
externality among users' decision, meaning your decision affect
my decision. My decision affect others' decision. Okay. So
with this, the family of tool that we develop here can be used to
model and analyze this result. You can analyze the business
model of a social computing system and also to design effective
incentive mechanism to seduce desirable behavior.
Okay. Now I want to focus on the last one. This is some other
applications that we have. The last one is the cloud sourcing.
This one I want to emphasize with this decision making tool, we
can specifically design a mechanism to achieve the desirable
goal. Rather than we just learn the outcome. Okay. So this is
about cloud sourcing. So let's see what problem do we have here.
Large scale labeled dataset is very important for this learning.
And because of this more data can be more accuracy, and larger
scale annotation is very expensive. Therefore, this often
becomes a bottleneck. So what solution do we have?
Microtask crowd sourcing. We can have a microtask crowd sourcing
for many people to come in to help us. It can be very large
volume, short time and low cost. What is the problem? The
problem is the collected data can be low quality because the user
may not have the right incentive to do so. And you may say,
let's give them incentive. Let's pay them. That's a problem.
Most of the time, we also don't have very high budget to do so.
So the reason we want to do microtask crowd sourcing is we don't
have high budget. We even may not have budget or we have very
low budget. But if you say give them more, if we had that, then
we have this problem, but if you say give them incentive, then
it's no longer meet the purpose of this. So what to do? I will
show that we can design a mechanism to do so. Machine learning
solution is what? Okay. Now we know this problem. We will add
a data curation phase to filter out low quality data. Let's
filter out or modify the learning algorithm to accept noisy
label. That's what people have done. But these are basically
[indiscernible] problem. What is the best we can do? We can do
some trial and error. Now with this decision learning solution,
what we propose is how about let's incentivize high quality data
from the first place by devising a mechanism to do so and pay a
very, very minimum cost to achieve that.
Okay. So what to do. Crowd sourcing is for something repetitive
and tedious. [indiscernible], correct? No? This is not a very
simple one. That's why we can do this [indiscernible] crowd
sourcing. And each task, the reward is often very small. So now
you think about if you are low quality
somebody with a low
quality skill, you can only do this. But because iter very
small, how do you maximize your profit? You can only use
quantity to maximize the profit. In order to maximize your
quantity, your quality of your work may not be too good, because
it's too small and who care, okay. So you want to increase that.
So it's small and there's no competition among the user. This
microtask crowd sourcing lacks proper incentive. And so it's
profitable for worker to submit poor solution because nobody
know. As long as nobody know, they will submit that. We need to
provide incentive. If we provide incentive, then our problem is
this mechanism become costly, okay. So question is what kinds of
incentive mechanisms that we have that we should request employ
to collect high quality solutions in a cost effective way? Can
we come up with that? Yes?
>>: Just as a point of reference, for Amazon Mechanical Turk,
there is an incentive system. There are two. The first is that
the person that requests the work can reject the answer that they
get back and not pay.
>> K.J. Ray Liu:
I know.
>>: And the other is that each worker has a statistic which is
what fraction of their work has been rejected.
>> K.J. Ray Liu:
>>: So there is
but it does exist.
Okay.
I'm not saying that's an optimal mechanism,
>> K.J. Ray Liu: Yes, I will talk about that, and we
and
people also has shown, and, in fact, we also had derived there is
a minimum cost there, and the minimum cost can be very high. And
we want to propose something different so I will talk about that.
Okay. So the model is this. Strategic worker model. That's
actions that it's produce quality from zero to one. One is being
the highest quality. Gain. There's a reward given by the
requester. And the cost, the cost is something that will be
convex. This cost could be convex. Why? Because it is
basically more costly to improve higher quality solutions. So it
should be [indiscernible] functions. And differentiable is for
[indiscernible] and also is a first order derivative is larger
than this zero because answer the higher quality are more costly
to produce.
Okay. Each worker will act to maximize own utility. So that is
worker's model. Requester's model. Single requester. We will
publish tasks and solicit solution. Decide whether to submit
whether a submitted solution should be accepted or not. There's
a decision. So it is how and what to do with this. And then
design mechanism. Which specify rule for evaluating submitted
solution and for rewarding worker.
Okay. So now there's a problem formulation. Solution concept,
they had to be a symmetric Nash equilibrium. Symmetric saying
now we assume everybody is the same, okay. So it's a symmetric.
They all have the same utility and all the same conditions. And
interested in desirable Nash equilibrium where worker will choose
Q equal 1 as their equilibrium, because we assume such a task is
so simple.
You will check this is correct and that is not correct.
everybody can easily achieve very high quality.
So
Now, there is a mechanism cost, CM. And this mechanism cost can
be reward paid to worker or the cost to evaluate. That is the
most important one, to evaluate the submitted solution. So the
question now is, what is the minimum mechanism cost that can we
guarantee the existence of such an equilibrium, that equilibrium
in [indiscernible] the best for the requester and the best for
the worker.
There's two basic mechanism that we know before. And just like
what you had mentioned, one is a reward consensus mechanism. How
do we evaluate, based on everybody's result and then we will
work. The one with the maturity is the right answer. We had to
do these evaluations. And the next says the reward accuracy
mechanism. We had to evaluate the accuracy, okay. We had to
evaluate the accuracy. Then these two mechanism have minimum
cost. The cost of that is this and the cost of that is that.
That's a minimum cost. And why? Because we had to evaluate.
And the problem is the minimum cost constraint in the two basic
mechanisms, this the requester had no control. Those minimum
cost is something that we had no control. The better that we can
evaluate a result, the higher the cost.
So this basically, this trade off may negate a low cost advantage
of microtask crowd sourcing. So what can we do here? So I would
like to illustrate one example that we tried to
that we
proposed that can overcome this problem in a very nice way. We
propose a mechanism that do so. We employ quality aware worker
training to reduce mechanism cost. Why is this? Instead of
[indiscernible] scheme at the beginning, at the beginning provide
some kind of training for worker at the beginning, we don't do
so. We say that because this is very simple scheme, we try to
assign this training to worker when what? When they perform
poorly.
You can view this as what? You can view this as like
punishment state. I mean, all the time they have, if
use that to produce result, they can make money. But
perform poorly, and then if they perform poorly, then
to get into training state and then that basically is
punishment, okay, more like a punishment state.
a
they can
if they
they have
more like a
Okay. So there are two states in this proposed mechanism. One
is a working state that can produce result and for them to make
money and the training state, this training state you will get N
training task to gain qualification for the working state.
Okay. So now let's see how does this work. We have to evaluate.
Now we have a training state and we have a working state. And
there's all this probability, workers action right now not only
affect the immediate utility, but also future utility. So
therefore, each worker will choose his action based on what?
Long term expected utility, okay? For this, basically this
problem can be formulated as MDP. However this MDP had one
thing, that is it is not each individually, just evaluate its own
MDP is faced by each worker also depend on other worker. So it
depend on each other. Therefore, this is a kind of game, the
reverse MDP. Okay. It's MDP process, but it affect each other.
So our result is this. Just summarize this, interpret this. In
this proposed mechanism, if the number of training task, N, is
large enough, okay, N is large enough, meaning if you don't do
well, you go to punishment stage, you are going to spend a lot of
hard work for nothing. Then there is a symmetric Nash
equilibrium that can force you to produce quality one result.
[indiscernible]. What does this mean? This mean that given any
parameter in this working state, we can always guarantee the
existence of such a desirable Nash equilibrium. At what cost?
This one is very interesting. At a cost that syntactically, it
can be zero. This is the sampling probability for testing. This
is reward. The cost can be zero. So let's interpret what does
that mean. We can collect high quality solution with an
arbitrary low cost if we choose the right parameter. And the
requester will have always, we always have a pretty
[indiscernible] budget.
>>: But the [indiscernible] cost, that's a training cost, not
the reward cost.
>> K.J. Ray Liu: This is the cost to, yeah, to total that you
want to pay them and also there's two cost. One is the reward to
the worker and then the other one is your evaluation. The
budget, this budget is influenced by many things, okay. So how
do we interpret this? I mean, given any budget, the proposed
mechanism in anybody requested to collect high quality solution
while still staying within the budget. Although this is a zero,
that mean it can be very small, okay. We can do so.
I want to show you one example. This is an example that we,
actually, we performed experiment in our department. So the
task, very simple task, calculating the sum of two randomly
generated double digit number. Middle school, elementary school
can do that. Ten points for each accepted solution. The goal of
participant is to maximize the accumulated point. Very simple
one.
And then the task assigned to participants is three sets. Three
different set. Each set has three minutes. We need to control
the time because it help participant to quantify their cost of
solving a task with various qualities in terms of time. If they
want to maximize that within this three minutes, then they have
to determine it will be very high quality work or very low
quality work. They have to perform the trade off. So we have
the mechanism we actually set.
Set one, the reward accuracy mechanism with sampling probability
one, meaning with probability one, we will accept every solution.
It's very costly to [indiscernible]. Set two, sampling
probability 0.3. I will only randomly select 30 percent of the
solution to exam. And set three is this. But with this, we
build upon it the proposed mechanism. So what is the result?
This one. With probability one, we are going to examine every
solution everybody produce. Here we collect results from 41
participants. They are mostly engineering graduates, okay. And
use accuracy of each participant to indicate the effectiveness of
this mechanism. So this is for set number one. We want to exam
every solution. Then almost everybody produce very high quality
result.
Said two, only 30 percent of the solution will be exam, with very
high probability in order for them to make profit to produce very
bad result. Only one guy here, the here because this guy have
very high working axis, okay. Maybe Microsoft want to hire
people like that, but that. Why? Because you see only 30
percent chance will be evaluate. I want to maximize my own
utility. That's the best way to maximize my utility, not with
this. We impose this training. It's more like a detergent you
actually implement. After that, you can see this is very similar
to that one. And the cost of this may not be that high because
with this, user may choose not to? Get into that kinds of
behavior.
Okay. So conclusion and future work. Decision making is a tool
that combines learning and strategic decision making. We be a
lyze users' optimal behavior from users' perspective, okay. That
we try to argue, that cannot be ignored and that play a very
important role and, in fact, we can also design optimal mechanism
from system designer's point of view. So it is not simply from
years' perspective, but it's also from system designer's
perspective, depending on how you want to look into the problem.
And here, we use three examples to show the effectiveness of this
decision learning. One, we use online marketing to demonstrate
what? We can learn users' utility function for strategic
decision making. The second one is the Groupon. We demonstrate
that we can learn from each other's interactions, okay. They all
affect each other for better decision making. Otherwise, the
purpose of Groupon would just decline. It doesn't serve the
purpose. And for crowdsourcing, we argue that we can take into
account users' strategic behavior to obtain better quality data
for better training.
So the final thought is this. That is what we see in the current
big data world. In the current big data world, especially in
social media, there's lots of data there. These are big user
generated data. And then one of the user will take this data, do
all the modeling and study, then perform decision making and test
sequential action with the outcome. This outcome will come back
to affect the data. And the second user will again do the same
thing and its own decision may eventually come back to affect
that data. Therefore, for big data, this data is not just a
steady, unchanged data over there. It is a data that collected
the change with time and also keep changing depending on each
different user's interaction and decision making. Therefore, all
of these have to be taken into consideration so that we can make
optimal decision through the best learning that we can.
Okay.
much.
This is the topic that I wanted to talk today.
Thank you
>>: I just have a question on the crowd sourcing, at the end you
propose that according to this decision methodology, you have
three stages or two stages of process, yeah, training state and
working state. So I just wonder, how does this proposal
[indiscernible] how does it depend upon your analysis of using
[indiscernible].
>> K.J. Ray Liu: Okay. It will be this, right? So you are in
working state. With that probability, this is the quality you
produce. This is the quality that other people produce, okay.
The worker's action at working state. And then with this
probability, you can keep staying in the working state. With
that probability, you will be getting into training state. And
in the training state, that worker's action and training, okay,
in the training state, you also decide how long you will stay
over here, and then you will go back to here.
>>: Yes, okay.
case?
>> K.J. Ray Liu:
>>:
Yeah, in this example, it's fixed.
So the learning part is [indiscernible].
>> K.J. Ray Liu:
>>:
So now all this parameters here is fixed in this
We're learning the [indiscernible] probability.
So how difficult it is?
>> K.J. Ray Liu: Given the simple example we have at
universities, it's not that difficult. Yeah. However, if we
want to apply this to this real problem, then we may have to take
into account, you know, to keep a model.
>>: So in order to estimate that, this is actually the key.
you don't have that information, nothing
>> K.J. Ray Liu:
>>:
If
Yes, we need to have that.
Then what kind of label you have in order to figure out this
>> K.J. Ray Liu: The label data in our experiment is very
simple, because this is basically to calculate two randomly
generated double digit number, right. We don't have to
[indiscernible]. We look at ourself.
>>:
Okay.
>> K.J. Ray Liu: Yeah, otherwise you need
or evaluate 30 percent, you know.
evaluate every one
>>: I got the impression that on set three, even if you had set
the probability of sampling much lower, you would still perform
very well.
>> K.J. Ray Liu:
You mean this one?
>>: Yeah, set three would be much better than two, even if the
probability of sampling was like 0.1.
>> K.J. Ray Liu:
This one.
>>: Because all you need is a small probability of sampling to
change the person's behavior. It's like the train ticket
problem, right. People go into the train. But if I and choose,
you know, it only needs a small probability to change the
behavior.
>> K.J. Ray Liu: Yeah. In fact, this, you can view this more
like, okay if you got call, this is your punishment, okay? 0.1
percent, if your punishment is very severe, where you cannot earn
money during a period of time, people may
so this is a more
deter, okay, so actually how often you may go here, you don't
know, because of what if you see this, you know what.
[indiscernible] translate into when the punishment is severe
enough.
>>: Then the probability could be very small and it still works
well, right?
>> K.J. Ray Liu:
Yes.
Yes.
>>: An interesting extension of this that would bring in
externalities, which would be the rest of the work requesters is
to look at throughput of jobs on crowd sources. Because it's
often the case that paying more actually doesn't change the
quality. This is totally bizarre, but I've measured it. And
other people have measured it. If you pay more, what I saw is
the quality went down slightly. What goes up is the speed. So
if you pay more, you can get the jobs done a lot faster. It
would be interesting to see what the interaction is between this
kind of mechanism design and throughput. Because if people
perceive this as something that's slowing them down, how does
that affect the throughput of your job?
>> K.J. Ray Liu:
Yes.
>>: And there, it depends on what all the other requesters are
doing. If you're the only person that's policing in this way,
you're the only requester policing this way, you might find that
no one wants to do your jobs.
>> K.J. Ray Liu:
>>:
Um hmm, yes, that's correct.
Yeah.
In this case, do you announce the mechanism to the worker?
>> K.J. Ray Liu: Yes, at the beginning they all know.
to know. So then they take it into account.
They have
>>: In practice, you don't need to know whether it's a 30
percent or 10 percent, just need to be checked at the
[indiscernible].
>> K.J. Ray Liu: No, we let people know, okay.
know all this parameter, okay.
We let people
[Multiple people speaking.]
>> K.J. Ray Liu: The message basically here is for something
like this, it's not just we keep learning, keep learning, keep
learning. We can incorporate some strategic decision making as a
tool to design some kinds of mechanism that doesn't incur much
cost. Also, it can really improve the system performance. It's
just a different perspective to look into that.
>>:
[indiscernible].
>> K.J. Ray Liu: This is simply for one to be [indiscernible]
probability one to [indiscernible] solution.
>>:
[indiscernible] and you have an answer for every visit.
>> K.J. Ray Liu:
>>:
I had to evaluate every answer.
Okay, okay.
>> K.J. Ray Liu: This is 30 percent chance I have
[indiscernible] probability. Maybe three out of ten answer I
will evaluate.
>>:
Okay.
>> K.J. Ray Liu: Because this is very low, so many people will
choose to do a lousy work and they get reward because of the
chance of being caught is small.
>>: So in some case, if a worker finish ten tasks, and if they
get [indiscernible].
>> K.J. Ray Liu: No, no. When they got called, they had to say
N, I don't
we can determine the N. Well, it's not here.
There. We said this N, right here, the training of N test. So
if you got called, you had to train by 20 problem, okay. And
that 20 problem cannot come into your reward, okay. And after
that you will continue to do that. So [indiscernible] mechanism
is that you can view this as a training process to increase your
workers' skill for them to improve their skill whenever they need
to improve.
>>: On the other hand, if they know that the chance of being
caught is high, they lose everything, they have [indiscernible]
may decide not to do this job but do something else.
>> K.J. Ray Liu:
>>:
Yes.
There's a [indiscernible].
>> K.J. Ray Liu:
Yes.
>>: This doesn't assume there's competing strategies.
worker can choose between the two.
>> K.J. Ray Liu:
>>:
Exactly, exactly.
There's different layers.
>> K.J. Ray Liu:
>>:
The same
They can choose which test, yes.
That's all strategic.
>> K.J. Ray Liu: It is also strategic. There's many different
ways just to show that we can develop some kind of mechanisms to
achieve some desirable goal. Certainly, there are many other
competing factors there.
>>: [indiscernible] the worker will have to
has to be trained
again because accuracy is under [indiscernible].
>> K.J. Ray Liu:
No, no, no.
>>: Or just any single mistake, they'll have to be trained
again?
>> K.J. Ray Liu: Okay. There is a [indiscernible], okay which I
didn't indicate here. In assigning them to
assigning the
trainer to worker when they perform poorly, we have to decide
what does that mean, poorly, okay. It could be when they miss
one problem or when they miss two consecutive problem, something
like that.
>>:
So [indiscernible].
>> K.J. Ray Liu:
>>:
Right.
You mean in this particular experiment?
For the students, right?
>> K.J. Ray Liu: In fact, I can tell you. I can tell you. We
don't actually, because this is the result, right? We don't
actually implement this punishment state, because we just collect
this result. This mean that when a student, under this scenario,
produce this high quality result, under this, we can say that if
you perform poorly, but what we collect, we may
I don't
remember actually we tell them it's one problem or two
consecutive problem, I don't recall that, but this is when we
collect from them. Once they understand there is such a
mechanism there, this is the quality over where they produce.
>>:
It's almost like a train ticket.
>> K.J. Ray Liu:
Yes.
>>: Once I know there is something there, I will try my best to
perform.
>> K.J. Ray Liu:
>> Li Deng:
Yes, exactly.
Thank you very much.
>> K.J. Ray Liu:
Okay.
Thank you.