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.

Attributes:
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’