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Bimatrix game
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\mathbf{A} = \begin{bmatrix} c & d & e \\ f & g & h \\ \end{bmatrix}\mathbf{B} = \begin{bmatrix} p & q & r \\ s & t & u \\ \end{bmatrix}
\begin{array}{cc} & X & Y & Z \\ \hline V & c,p & d,q & e,r \\ W & f,s & g,t & h,u \\ \end{array}
A payoff matrix converted from A and B where player 1 has two possible actions V and W and player 2 has actions X, Y and Z

In , a bimatrix game is a simultaneous game for two players in which each player has a finite number of possible actions. The name comes from the fact that the of such a game can be described by two matrices - matrix A describing the payoffs of player 1 and matrix B describing the payoffs of player 2.

Player 1 is often called the "row player" and player 2 the "column player". If player 1 has m possible actions and player 2 has n possible actions, then each of the two matrices has m rows by n columns. When the row player selects the i-th action and the column player selects the j-th action, the payoff to the row player is Ai,j and the payoff to the column player is Bi,j.

The players can also play . A mixed strategy for the row player is a non-negative vector x of length m such that: \sum_{i=1}^m x_i = 1. Similarly, a mixed strategy for the column player is a non-negative vector y of length n such that: \sum_{j=1}^n y_j = 1. When the players play mixed strategies with vectors x and y, the expected payoff of the row player is: x^\mathsf{T} A y and of the column player: x^\mathsf{T} B y.


Nash equilibrium in bimatrix games
Every bimatrix game has a in (possibly) mixed strategies. Finding such a Nash equilibrium is a special case of the Linear complementarity problem and can be done in finite time by the Lemke–Howson algorithm.

There is a reduction from the problem of finding a Nash equilibrium in a bimatrix game to the problem of finding a competitive equilibrium in an economy with Leontief utilities.

(2025). 9780898716054


Related terms
A is a special case of a bimatrix game in which A+B = 0.

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