model-initiate: Processes a model for use in the algorithm
In squipbar/abSan: Computes the Abreu-Sannikov repeated game algorithm

Description Usage Arguments Value See Also Examples

Processes a model definition function in two ways. First, it repackages the payoffs and actions for easier use in the rest of the code. Second, it computes the other properties of a game, such as the marginal gain from deviating, which the Abreu-Sannikov code uses. These are then returned as a list for use in other functions, such as, abSan.eqm.

1	model.initiate(defn.fn, opts)

`defn.fn`	the model definition function. This must return a list including the following four entries: `iActions`, the 2-vector of the number actions for each player; `payoffs1`, the normal-form matrix of payoffs of the stage game for player 1; `payoffs2`, the normal-form matrix of payoffs of the stage game for player 1; and `delta` the discount factor. See, for instance, `examples.PD` in this package.
`opts`	a list of options to pass to the model definition function `defn.fn`.

Returns a list of components used in the Abreu-Sannikov algorithm:

`iActions`	2-vector of the number actions for each player.
`mA`	a (2,m)-matrix, where m is the number of joint actions. Each row contains the action indices for the two players associated with that joint action.
`mF`	payoffs associated with the joint actions listed in mA.
`mH`	the gain from deviating from each joint action for the two players. Abreu-Sannikov's h(a) function.
`delta`	the discount factor.
`iJointActs`	the number of joint actions.
`minmax`	the minmax values of the stage game.