logitdyn¶
-
class
quantecon.game_theory.logitdyn.LogitDynamics(data, beta=1.0)[source]¶ Bases:
objectClass representing the logit-response dynamics model.
Parameters: - dataNormalFormGame or array_like
The game played in the logit-response dynamics model.
- betascalar(float)
The level of noise in player’s decision.
Attributes: - Nscalar(int)
The number of players in the game.
- playerslist(Player)
The list consisting of all players with the given payoff matrix.
- nums_actionstuple(int)
Tuple of the number of actions, one for each player.
- betascalar(float)
See parameters.
- player.logit_choice_cdfsarray_like(float)
The choice probability of each actions given opponents’ actions.
Methods
logit_choice_cdfs()Return the tuple of choice probabilities. play([init_actions, player_ind_seq, ...])Return a new action profile which is updated num_reps times. time_series(ts_length[, init_actions, ...])Return the array representing time series of action profiles. -
play(init_actions=None, player_ind_seq=None, num_reps=1, random_state=None)[source]¶ Return a new action profile which is updated num_reps times.
Parameters: - init_actionstuple(int), optional(default=None)
The action profile in the initial period. If None, selected randomly.
- player_ind_seqlist(int), optional(default=None)
The sequence of player indices. If None, selected randomly.
- num_repsscalar(int), optional(default=1)
The number of iterations.
- random_stateint or np.random.RandomState/Generator, optional
Random number generator used.
Returns: - tuple(int)
The action profile after iterations.
-
time_series(ts_length, init_actions=None, random_state=None)[source]¶ Return the array representing time series of action profiles.
Parameters: - ts_lengthscalar(int)
The number of iterations.
- init_actionstuple(int), optional(default=None)
The action profile in the initial period. If None, selected randomly.
- random_stateint or np.random.RandomState/Generator, optional
Random number generator used.
Returns: - ndarray(int)
The array representing the time series of action profiles.