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We're now going to look at a different
category of games, games that are called

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Bayesian games. Sometimes called games of
incomplete information, not to be confused

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with games of imperfect information. So
far what we have seen are games in which

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all agents know what the basic setting is.
That is, they know who the players are.

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They know the actions available to the
players. They know that payoffs associated

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with each strategy profile or each action
profile, depending on what everybody does.

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this is true in all games, including games
of imperfect information. That is games

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of, in which our information is such where
agents don't know. Exactly in which state

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they are, nonetheless they know what would
happen given what the strategy of all the

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agents. So we're going to relax that.
We're going to assume that What you said

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isn't necessarily always common knowledge.
Now in principle you can imagine relaxing

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the various assumptions. You don't know
the number of the players. You don't know

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maybe the action, how many actions are
available to them. >> But, in some sense,

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some informal sense, all of those forms of
uncertainties can be reduced to one type

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of uncertainty, that is about the payoffs
in the game. And, so we will assume that

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agents have perfect common knowledge of
everything, except what the payoffs of the

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game are. And, furthermore, that there is
some. Prior knowledge, prior belief that

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is common to all the agents about those
payoffs, and simply agents have different

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signals that lead to different posteriors
based on those common prior This may sound

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very vague. Let me make it precise. Let me
first give the formal definition. and then

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just give an example which will make
everything clear. So we have a set of

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games, that is a Bayesian game is defined
by first of all a set of games that are

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identical except in their payoffs. So
let's start going over the formal

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definitions again. So we have A tuple that
defined the game. You have a set of agents

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N, and we have G as a set of regul ar kind
of games. Think of these as normal for

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games for example. each game is a consists
of N A agent play the game, and they all

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have the same strategy space. That is,
they're 2 games in the set that have the

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same strategy space. As I said, the payoff
will be generally different. We have a

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prior that is a distribution over those
set of games. That's a sum prior. Nature

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will decide which game is actually played
based on this prior. And then there's

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private signals as defined by our
partition structure. that is each agent

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for each agent, we find some equivalent
relation on the games. And agent will be,

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sort of, told which in which equivalent's
class they are. And based on that, they'll

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need to play the game. Now this is a
mouth, mouthful, I know but hopefully the

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following example will make it clear.
Let's assume that we have 4 possible

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games. And here the games that are
familiar, we have Matching Pennies, we

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have Prisoners Dilemma, we have the game
of pure Coordination, and we have Bat,

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Battle of teh Sexes. Each of those defined
simply by their, by their payoffs. Now,

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nature is going to decide which of those
games actually is being played. And we've

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decided, based on the probabilities as
listed here. We have a probability of 0.3

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here, 0.1 here, 0.2 here, and Point 4
here. Now once nature makes its choice,

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agent will play. But the question is, what
will they know? They will know the prior,

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but they will know something in addition.
And what they will know will be defined by

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this partition. So here we have the 2
agents playing, and for each of these

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agents there is a, a, an equivalent to
find.

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So, for example, think about the role
player. For the role player there are 2

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equivalence classes, denoted by the bold
Partition.

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And I'll make it green now. This is the
equivalent relation defined for the row

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agent. So, for example, suppose that,
nature decided to, in fact. Playing

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matching pennies. The agents will know
the, that is the row agent, will know that

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he's either in this game, or in this game.
He'll know that he's not in any of the

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other games. So that will be his private
signal. He'll now have posterior belief.

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What will he believe? Well he will believe
that with probability point a, point 75 he

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is playing this game at 0.25 he is playing
this game and why is that because this is

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the ratio of 3 to 1 as defined between
these 2 games for him. What will the

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column player Now well a column player
lets pick a different color for her, she

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has a different equal ventilation, this
one. And now if matter again chose

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matching penny what will she know? Well
she'll know that she is either. In this

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game or in this game, and in this case she
will need to update her prior to reflect

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this information and the perceiver for the
a,h column agent will be that she is

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playing this game's probability .6 and
this Proba, probability 0.4. Again,

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maintaining the ratios between these 2
games. And then we'll know more,

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intuitively. Because the, when the agent
knows, The ag, for example, the row agent

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knows that she's someplace in this class.
She will not know exactly what information

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the common player has, but she knows what
the possible information is it might have.

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She knows that, that the role player knows
that Either she is in this game, in which

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case she knows that this would be the
information that the common player has or

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that she is in this game in which case
she, the role player knows that the role

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player knows that she's someplace here.
And so it's a complicated story because

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you can keep going. They have some beliefs
about what the other player believe about

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what they know so on and so forth. But
this is the structure of Bayesian games

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and based on this, you can start modeling
and what it will do. But since this is

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complicated there's an alternative
Perspective on, on, on beige in game that

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is different, but in some sense easier to
work with.
