# repeated_game¶

Tools for repeated game.

class quantecon.game_theory.repeated_game.RepeatedGame(stage_game, delta)[source]

Bases: object

Class representing an N-player repeated game.

Parameters: stage_game : NormalFormGame The stage game used to create the repeated game. delta : scalar(float) The common discount rate at which all players discount the future. sg : NormalFormGame The stage game. See Parameters. delta : scalar(float) See Parameters. N : scalar(int) The number of players. nums_actions : tuple(int) Tuple of the numbers of actions, one for each player.

Methods

 equilibrium_payoffs([method, options]) Compute the set of payoff pairs of all pure-strategy subgame-perfect equilibria with public randomization for any repeated two-player games with perfect monitoring and discounting.
equilibrium_payoffs(method=None, options=None)[source]

Compute the set of payoff pairs of all pure-strategy subgame-perfect equilibria with public randomization for any repeated two-player games with perfect monitoring and discounting.

Parameters: method : str, optional The method for solving the equilibrium payoff set. options : dict, optional A dictionary of method options. For example, ‘abreu_sannikov’ method accepts the following options: tol : scalar(float) Tolerance for convergence checking. max_iter : scalar(int) Maximum number of iterations. u_init : ndarray(float, ndim=1) The initial guess of threat points.

Notes

Here lists all the implemented methods. The default method is ‘abreu_sannikov’.

1. ‘abreu_sannikov’