egttools.numerical.MLSGarcia

class MLSGarcia(self: egttools.numerical.numerical_.MLSGarcia, nb_generations: SupportsInt | SupportsIndex, nb_strategies: SupportsInt | SupportsIndex, group_size: SupportsInt | SupportsIndex, nb_groups: SupportsInt | SupportsIndex)

Bases: pybind11_object

Multi-level selection following Garcia & van den Bergh (2011).

Extends the Traulsen & Nowak model with direct group conflict (kappa) and separate in-group / out-group payoff matrices (alpha weighting).

Parameters:

  • nb_generations (int) – Maximum number of Moran steps per run.

  • nb_strategies (int) – Number of distinct strategies.

  • group_size (int) – Maximum group capacity n (must be >= 4).

  • nb_groups (int) – Number of groups m.

Methods

fixation_probability

Estimate fixation probability of invader over resident.

Attributes

generations

group_size

max_pop_size

nb_groups

nb_strategies

__init__(self: egttools.numerical.numerical_.MLSGarcia, nb_generations: SupportsInt | SupportsIndex, nb_strategies: SupportsInt | SupportsIndex, group_size: SupportsInt | SupportsIndex, nb_groups: SupportsInt | SupportsIndex) → None
__new__(**kwargs)
__repr__(self: egttools.numerical.numerical_.MLSGarcia) → str
fixation_probability(self: egttools.numerical.numerical_.MLSGarcia, invader: typing.SupportsInt | typing.SupportsIndex, resident: typing.SupportsInt | typing.SupportsIndex, nb_runs: typing.SupportsInt | typing.SupportsIndex, q: typing.SupportsFloat | typing.SupportsIndex, lambda: typing.SupportsFloat | typing.SupportsIndex, w: typing.SupportsFloat | typing.SupportsIndex, alpha: typing.SupportsFloat | typing.SupportsIndex, kappa: typing.SupportsFloat | typing.SupportsIndex, z: typing.SupportsFloat | typing.SupportsIndex, payoff_matrix_in: typing.Annotated[numpy.typing.NDArray[numpy.float64], "[m, n]", "flags.c_contiguous"], payoff_matrix_out: typing.Annotated[numpy.typing.NDArray[numpy.float64], "[m, n]", "flags.c_contiguous"]) → float

Estimate fixation probability of invader over resident.

Parameters:

  • invader (int)

  • resident (int)

  • nb_runs (int)

  • q (float splitting probability)

  • lambda (float migration probability)

  • w (float intensity of selection)

  • alpha (float fraction of interactions within the group)

  • kappa (float average fraction of groups involved in conflict)

  • z (float importance of payoffs in conflict (0 = deterministic))

  • payoff_matrix_in (np.ndarray (nb_strategies x nb_strategies) in-group payoff)

  • payoff_matrix_out (np.ndarray (nb_strategies x nb_strategies) out-group payoff)

__annotations__ = {}
property generations
property group_size
property max_pop_size
property nb_groups
property nb_strategies