Utility routines for the game_theory submodule

class quantecon.game_theory.utilities.NashResult[source]

Bases: dict

Contain the information about the result of Nash equilibrium computation.


This is sourced from sicpy.optimize.OptimizeResult.

There may be additional attributes not listed above depending of the routine.

NE : tuple(ndarray(float, ndim=1))

Computed Nash equilibrium.

converged : bool

Whether the routine has converged.

num_iter : int

Number of iterations.

max_iter : int

Maximum number of iterations.

init : scalar or array_like

Initial condition used.


fromkeys(iterable[, value]) Create a new dictionary with keys from iterable and values set to value.
get(key[, default]) Return the value for key if key is in the dictionary, else default.
pop(key[, default]) If key is not found, default is returned if given, otherwise KeyError is raised
popitem(/) Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default]) Insert key with a value of default if key is not in the dictionary.
update([E, ]**F) If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]