localint

class quantecon.game_theory.localint.LocalInteraction(payoff_matrix, adj_matrix)

Bases: object

Class representing the local interaction model.

Parameters:

payoff_matrixarray_like(float, ndim=2)

The payoff matrix of the symmetric two-player game played in　each interaction.

adj_matrixarray_like(float, ndim=2)

The adjacency matrix of the network. Non constant weights and　asymmetry in interactions are allowed.

Attributes:

playerslist(Player)

The list consisting of all players with the given payoff matrix.

adj_matrixscipy.sparse.csr.csr_matrix(float, ndim=2)

See Parameters.

Nscalar(int)

The number of players.

num_actionsscalar(int)

The number of actions available to each player.

Methods

play([revision, actions, player_ind_seq, ...])	Return a new action profile which is updated by playing the game num_reps times.
time_series(ts_length[, revision, actions, ...])	Return an array representing time series of each player's actions.

play(revision='simultaneous', actions=None, player_ind_seq=None, num_reps=1, **options)

Return a new action profile which is updated by playing the game num_reps times.

Parameters:

revisionstr, optional(default=’simultaneous’)

The way to revise the action profile. If simulataneous, all players’ actions will be updated simultaneously. If asynchronous, only designated players’ actions will be updated. str in {‘simultaneous’, ‘asynchronous’}.

actionstuple(int) or list(int), optional(default=None)

The action profile in the first period. If None, selected randomly.

player_ind_seqarray_like(int), optional(default=None)

The index of players who take actions. If None, all players take actions.

num_repsscalar(int), optional(default=1)

The number of iterations.

**optionsKeyword arguments passed to the best response method and

other methods.

Returns:

tuple(int)

The action profile after iterations.

time_series(ts_length, revision='simultaneous', actions=None, player_ind_seq=None, **options)

Return an array representing time series of each player’s actions.

Parameters:

ts_lengthscalar(int)

The number of iterations.

revision{‘simultaneous’, ‘asynchronous’}(default=’simultaneous’)

The way to revise the action profile. If simulataneous, all players’ actions will be updated simultaneously. If asynchronous, only designated players’ actions will be updated. str in {‘simultaneous’, ‘asynchronous’}.

actionstuple(int), optional(default=None)

The action profile in the first period. If None, selected randomly.

player_ind_seqarray_like, optional(default=None)

The sequence of player_ind`(see `play Parameters) when the revision is ‘asynchronous’. If None, selected randomly.

**optionsKeyword arguments passed to the best response method and

other methods.

Returns:

Array_like(int)

The array representing time series of each player’s actions.