Correlated Equilibrium and Mixed Nash Equilibrium with python
You can see the statements of this question here.
I tried to find the mixed nash equilibrium with simplex algorithm.
I applied two different kind of LP for this question.
Second one is an easier implementation using M + N variables. You can see more details here.
Input:
3 2
8 -5
-3 4
-5 -4
8 9
1 -2
8 5
Output:
0.750000 0.250000 0.000000
0.450000 0.550000
Suppose two players wants to play a game. In this example, player 1 has 3 actions and player2 has 2 actions. After N lines we get the utilities of player1 and after N lines we get utilities of player2. The table of utilities is equal to:
(8, 8) | (-5, 9)
-----------------
(-3, 1) | (4, -2)
-----------------
(-5, 8) | (-4, 5)
The mixed nash eqilibrium here shows the strategy of each player:
player1 -> (0.75, 0.25, 0)
player2 -> (0.45, 0.55)
You can see the statements of this question here.
I tried to find the correlated equilibrium with simplex algorithm.
For finding correlated equilibrium, you should check this constraint and with solving the LP, you will find the answer.
Input:
1 1
3 3
6 6 -2 0 0 7
2 2 2 2 0 0
0 0 0 0 3 3
Output:
8.000000
0.500000 0.000000 0.000000
0.250000 0.250000 0.000000
0.000000 0.000000 0.000000
Suppose two players want to play a game. In this example, player 1 has 3 actions and player2 has 3 actions. We should find the probability of playing each strategy profile and maximum optimal social benefit. For this example, the table of utilities is:
(6, 6) | (-2, 0) | (0, 7)
--------------------------
(2, 2) | (2, 2) | (0, 0)
--------------------------
(0, 0) | (0, 0) | (3, 3)
After solving LP, for finding maximum optimal social benefit we have:
0.5 * (6 + 6) + 0.25 * (2 + 2) + 0.25 * (2 + 2) = 8
For more questions or any problem, feel free to contact me.