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Game Theory
Game theory is a mathematical theory that deals
with the general features of competitive situations.
The final outcome depends primarily upon the
combination of strategies selected by the
adversaries.
Two key Assumptions:
(a) Both players are rational
(b) Both players choose their strategies solely
to increase their own welfare.
Payoff Table
StrategyPlayer 2
1 2 31 2 4 1 0 5 0 1 -1
Player 1123
Each entry in the payoff table for player 1 represents the utility to player 1 (or the negative utility to player 2) of the outcome resulting from the corresponding strategies used by the two players.
A strategy is dominated by a second strategy if the second strategy is always at least as good regardless of what the opponent does. A dominated strategy can be eliminated immediately from further consideration.
StrategyPlayer 2
1 2 31 2 4 1 0 5 0 1 -1
Player 1123
For player 1,strategy 3 can be eliminated. ( 1 > 0, 2 > 1, 4 > -1)
1 2 31 2 4 1 0 5
12
For player 2,strategy 3 can be eliminated. ( 1 < 4, 1 < 5 )
1 21 2 1 0
12
For player 1,strategy 2 can be eliminated. ( 1 = 1, 2 < 0 )
1 21 2 1
For player 2,strategy 2 can be eliminated. ( 1 < 2 )
Consequently, both players should select their strategy 1.
A game that has a value of 0 is said to be
a fair game.
StrategyPlayer 2
1 2 3-3 -2 6 2 0 2 5 -2 -4
Player 1123
Minimum
Maximum: 5 0 6
-30-4
Minimax value Maximin value
Minimax criterion:
To minimize his maximum losses whenever resulting choice of strategy cannot be exploited by the opponent to then improve his position.
StrategyPlayer 2
1 2 3-3 -2 6 2 0 2 5 -2 -4
Player 1123
Minimum
Maximum: 5 0 6
-30-4
Saddle point
The value of the game is 0, so this is fair game
Saddle Point:
A Saddle point is an entry that is both the maximin and minimax.
StrategyPlayer 2
1 2 3 0 -2 2 5 4 -3 2 3 -4
Player 1123
Maximum: 5 4 2
-2-3-4
Minimum
There is no saddle point.
An unstable solution
Mixed Strategies
= probability that player 1 will use strategy i
( i = 1,2,…,m),
= probability that player 2 will use strategy j
( j = 1,2,…,n),
ix
jy
Expected payoff for player 1 =
m
i
n
jjiij yxp
1 1
,
Minimax theorem:
If mixed strategies are allowed, the pair of mixed strategies that is optimal according to the minimax criterion provides a stable solution with (the value of the game), so that neither player can do better by unilaterally changing her or his strategy.
vvv
v
v
= maximin value
= minimax value
Probability
Player 2
1 2 3 0 -2 2 5 4 -3 Player 1
12
1x11 x
PureStrategy
1y 2y 3yProbability
Graphical Solution Procedure
12 1 xx
),,( 321 yyy
)1,0,0(
)0,1,0(
)0,0,1(Expected Payoff
111
111
53)1(32
55)1(50
xxx
xxx
111 64)1(42 xxx
),,( 321 yyy
)1,0,0(
)0,1,0(
)0,0,1(Expected Payoff
111
111
53)1(32
55)1(50
xxx
xxx
111 64)1(42 xxx
Expected payoff for player 1 =
).53()64()55( 131211 xyxyxy
6543210
-1-2-3-4
2
1
4
3
4
1 1.0 1x
155 x
164 x
153 x
Exp
ecte
d pa
yoff
Maximin point
Player 1 wants to maximize the minimum expected payoff. Player 2 wants to minimize the expected payoff.
11 6453 xx
)11
4,
11
7(),( 21 xx
11
2
11
753
vv
The optimal mixed strategy for player 1 is
)11
4,
11
7(),( 21 xx
So the value of the game is
11
2)53()64()55( 1
*31
*21
*1 vvxyxyxy
)y,y,y( *3
*2
*1The optimal strategy
(1)
Because is a probability distribution,
.1*3
*2
*1 yyy
jy
11
2
11
2
11
2
11
20 *3
*2
*1 vyyy (2)
11
71 xWhen player 1 is playing optimally ( ),
this inequality will be an equality, so that
11
7
11
2
,1011
2
)53()64(
1
1
2*31
*2
xfor
xforxyxy
because would violate (2),0*1 y 0*
1 y
.10,11
2)53()64( 12
*31
*2 xforxyxy
Because the ordinate of this line must equal
at , and because it must never exceed ,11
71 x
11
2
11
2
11
222
,11
234
*3
*2
*3
*2
yy
yy
To solve for and , select two values of
(say, 0 and 1),
11
6,
11
5 *3
*2 yy
).11
6,
11
5,0(),,( *
3*2
*1 yyy
The optimal mixed strategy for player 2 is
*2y
*3y
Solving by Linear Programming
Expected payoff for player 1 =
m
i
n
jjiij yxp
1 1
The strategy is optimal if),,,( 21 mxxx
vvyxpm
i
n
jjiij
1 1
,,,2,11
njforvxpm
iiij
),,,( 21 nyyy For each of the strategies where one and the rest equal 0. Substituting these values into the inequality yields
121 mxxx
mforixi ,,2,1,0
ixBecause the are probabilities,
The two remaining difficulties are
(1) is unknown
(2) the linear programming problem has
no objective function.
Replacing the unknown constant by the variable and then maximizing ,
so that automatically will equal at the optimal solution for the LP problem.
1mx 1mx
1mx
v
v
v
.m,,2,1ifor,0x
1xxx
0xxpxpxp
0xxpxpxp
0xxpxpxp .t.s
,xMaximize
i
m21
1mmmn2n21n1
1mm2m222112
1mm1m221111
1m
.n,,2,1jfor,0y
1yyy
0yypypyp
0yypypyp
0yypypyp .t.s
,yMinimize
j
n21
1nnmn22m11m
1nnn2222121
1nnn1212111
1n
1xx
0xx3x2
0xx4x2
0xx5.t.s
,xMaximize
21
321
321
32
3
Probability
Player 2
1 2 3 0 -2 2 5 4 -3 Player 1
12
1x11 x
PureStrategy
1y 2y 3yProbabilityExample
.0x,0x,0x 321
)11
2,
11
4,
11
7(),,( *
3*2
*1 xxx
1yyy
0yy3y4y5
0yy2y2.t.s
,yMinimize
321
4321
432
4
Probability
Player 2
1 2 3 0 -2 2 5 4 -3 Player 1
12
1x11 x
PureStrategy
1y 2y 3yProbability
.0y,0y,0y,0y 4321 )
11
2,
11
6,
11
5,0(
)y,y,y,y( *4
*3
*2
*1
The dual
Question 1
Consider the game having the following payoff table.
(a) Formulate the problem of finding optimal mixed strategies according to the minimax criterion as a linear programming problem.
(b) Use the simplex method to find these optimal mixed strategies.
1 2 3 41 5 0 3 1
Player 1 2 2 4 3 23 3 2 0 4
Player 2Strategy