1. Method & Example-1
Method
graphical method Steps (Rule)
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Step-1:
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This method can only be used in games with no saddle point, and having a pay-off matrix of type n`xx`2 or 2`xx`n.
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Step-2:
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The example is used to explain the procedure.
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Example-1
1. Find Solution of game theory problem using graphical method
Player A\Player B | B1 | B2 | A1 | 1 | -3 | A2 | 3 | 5 | A3 | -1 | 6 | A4 | 4 | 1 | A5 | 2 | 2 | A6 | -5 | 0 |
Solution: 1. Saddle point testing Players
| | | Player `B` | | | | | | `B_1` | `B_2` | | | Player `A` | `A_1` | | 1 | -3 | | `A_2` | | 3 | 5 | | `A_3` | | -1 | 6 | | `A_4` | | 4 | 1 | | `A_5` | | 2 | 2 | | `A_6` | | -5 | 0 | |
We apply the maximin (minimax) principle to analyze the game.
| | | Player `B` | | | | | | `B_1` | `B_2` | | Row Minimum | Player `A` | `A_1` | | 1 | -3 | | `-3` | `A_2` | | [3] | 5 | | `[3]` | `A_3` | | -1 | 6 | | `-1` | `A_4` | | (4) | 1 | | `1` | `A_5` | | 2 | 2 | | `2` | `A_6` | | -5 | 0 | | `-5` | | Column Maximum | | `(4)` | `6` | | |
Select minimum from the maximum of columns Column MiniMax = (4)
Select maximum from the minimum of rows Row MaxiMin = [3]
Here, Column MiniMax `!=` Row MaxiMin
`:.` This game has no saddle point.
Solution using graphical method First, we draw two parallel lines 1 unit distance apart and mark a scale on each. The two parallel lines represent strategies of player `B`.
If player `A` selects strategy `A_1`, player `B` can win 1 or -3 units depending on B's selection of strategies.
The value 1 is plotted along the vertical axis under strategy `B_1` and the value -3 is plotted along the vertical axis under strategy `B_2`.
A straight line joining the two points is then drawn. Similarly, we can plot strategies `A_2,A_3,A_4,A_5,A_6` also. The problem is graphed in the following figure.
![](data:image/png;base64,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)
The lowest point `V` in the shaded region indicates the value of game. From the above figure, the value of the game is `3.4` units.
1. The point of optimal solution occurs at the intersection of two lines `E_2 = 3p_1 +5p_2`
`E_4 = 4p_1 +p_2`
Comparing the above two equations, we have `3p_1 +5p_2 = 4p_1 +p_2`
Substituting `p_2 = 1 - p_1`
`3p_1 +5(1 - p_1) = 4p_1 +(1 - p_1)`
Solving `p_1=4/5`
`p_2=1-p_1=1-4/5=1/5`
Substituting the values of `p_1` and `p_2` in equation `E_2`
`V = 3(4/5) +5(1/5)=17/5`
2. The point of optimal solution occurs at the intersection of two lines `L_2 = 3q_1 +4q_2`
`L_4 = 5q_1 +q_2`
Comparing the above two equations, we have `3q_1 +4q_2 = 5q_1 +q_2`
Substituting `q_2 = 1 - q_1`
`3q_1 +4(1 - q_1) = 5q_1 +(1 - q_1)`
Solving `q_1=3/5`
`q_2=1-q_1=1-3/5=2/5`
Substituting the values of `q_1` and `q_2` in equation `L_2`
`V = 3(3/5) +4(2/5)=17/5`
This material is intended as a summary. Use your textbook for detail explanation. Any bug, improvement, feedback then
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