egttools.numerical.structure.factories.NetworkMCEstimatorPC¶

class NetworkMCEstimatorPC(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC, game: egttools.numerical.numerical_.games.AbstractSpatialGame, topology: collections.abc.Mapping[SupportsInt | SupportsIndex, collections.abc.Sequence[SupportsInt | SupportsIndex]], nb_strategies: SupportsInt | SupportsIndex, beta: SupportsFloat | SupportsIndex, mu: SupportsFloat | SupportsIndex, cache_size: SupportsInt | SupportsIndex = 100000)¶

Bases: pybind11_object

Monte Carlo estimator for evolutionary games on networks using the Pairwise Comparison update rule.

Provides gradient of selection, fixation probability, strategy distribution estimation, and trajectory generation. Uses OpenMP for parallel runs and an LRU cache for fitness memoization.

The network topology is stored as a contiguous adjacency list for O(1) neighbour lookup.

Construct a NetworkMCEstimator.

Parameters:

game (egttools.games.AbstractSpatialGame) – Spatial game used to evaluate node fitness.
topology (dict[int, list[int]]) – Network adjacency dictionary (e.g. from NetworkX G.adjacency()).
nb_strategies (int) – Number of distinct strategies.
beta (float) – Selection intensity.
mu (float) – Mutation probability per time step.
cache_size (int, optional) – Per-thread LRU fitness cache size (default 100000).

Methods

`beta`
`calculate_gradient_of_selection`	Numerically exact gradient of selection for the given per-node strategy assignment.
`estimate_agos`	Estimate the time-independent Average Gradient of Selection G^A(j).
`estimate_agos_time_dependent`	Estimate the time-dependent Average Gradient of Selection G^A(j, t).
`estimate_fixation_probability`	Estimate the fixation probability of a single invader in an otherwise resident population.
`estimate_strategy_distribution`	Estimate time-averaged strategy frequencies after the transitory period.
`initialize`	Overloaded function.
`mean_population_state`	Return strategy count vector (length = nb_strategies).
`mu`
`nb_strategies`
`population_size`
`population_strategies`	Return per-node strategy assignments as a list of integers (length = population_size).
`run`	Run a single trajectory and return aggregate strategy counts per generation.
`run_snapshots`	Run a trajectory and call `callback(generation, population)` at each snapshot.
`set_beta`
`set_mu`
`step`	Advance the session by one generation.
`topology`

__init__(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC, game: egttools.numerical.numerical_.games.AbstractSpatialGame, topology: collections.abc.Mapping[SupportsInt | SupportsIndex, collections.abc.Sequence[SupportsInt | SupportsIndex]], nb_strategies: SupportsInt | SupportsIndex, beta: SupportsFloat | SupportsIndex, mu: SupportsFloat | SupportsIndex, cache_size: SupportsInt | SupportsIndex = 100000) → None¶

Construct a NetworkMCEstimator.

Parameters:

game (egttools.games.AbstractSpatialGame) – Spatial game used to evaluate node fitness.
topology (dict[int, list[int]]) – Network adjacency dictionary (e.g. from NetworkX G.adjacency()).
nb_strategies (int) – Number of distinct strategies.
beta (float) – Selection intensity.
mu (float) – Mutation probability per time step.
cache_size (int, optional) – Per-thread LRU fitness cache size (default 100000).

__new__(**kwargs)¶

beta(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC) → float¶

calculate_gradient_of_selection(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC, population: collections.abc.Sequence[SupportsInt | SupportsIndex]) → Annotated[numpy.typing.NDArray[numpy.float64], '[m, 1]']¶

Numerically exact gradient of selection for the given per-node strategy assignment.

Parameters:: population (list[int]) – Strategy index for each node (length = population_size).
Returns:: Gradient vector of length nb_strategies.
Return type:: numpy.ndarray

estimate_agos(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC, nb_runs: SupportsInt | SupportsIndex, nb_generations: SupportsInt | SupportsIndex, transitory: SupportsInt | SupportsIndex = 0, runs_per_j: SupportsInt | SupportsIndex = 0) → tuple[Annotated[numpy.typing.NDArray[numpy.float64], '[m, n]'], Annotated[numpy.typing.NDArray[numpy.float64], '[m, n]']]¶

Estimate the time-independent Average Gradient of Selection G^A(j).

Runs independent trajectories; at each post-transitory generation computes the numerically exact gradient and bins the result by cooperator count j. OpenMP-parallelised over runs; per-thread caches prevent contention.

Parameters:

nb_runs (int) – Total trajectories when runs_per_j == 0 (random starts).
nb_generations (int) – Generations (each = N elementary steps) per trajectory.
transitory (int, optional) – Burn-in generations not counted (default 0).
runs_per_j (int, optional) – When > 0, uses the paper’s sampling scheme: for each initial cooperator count j0 ∈ {1, …, N-1} exactly runs_per_j trajectories are started with j0 cooperators on random nodes. Total runs = runs_per_j × (N-1); nb_runs is ignored. This gives uniform coverage of all j values.

Returns:

(mean_G, se_G) each of shape (N+1, nb_strategies). Row j holds the average/SE gradient at cooperator count j. Rows 0 and N are zero (absorbing states).

Return type:

tuple[numpy.ndarray, numpy.ndarray]

estimate_agos_time_dependent(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC, nb_runs: SupportsInt | SupportsIndex, nb_generations: SupportsInt | SupportsIndex) → tuple[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64]]¶

Estimate the time-dependent Average Gradient of Selection G^A(j, t).

Like estimate_agos but preserves the generation index, letting you study how the gradient landscape evolves from the initial transient to the stationary regime (e.g. Fig. 2 of Pinheiro et al. 2012).

Returns:: (mean_G_t, se_G_t) each of shape (nb_generations, N+1, nb_strategies). mean_G_t[t, j, k] is the mean gradient of strategy k at generation t and cooperator count j.
Return type:: tuple[numpy.ndarray, numpy.ndarray]

estimate_fixation_probability(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC, invader: SupportsInt | SupportsIndex, resident: SupportsInt | SupportsIndex, nb_runs: SupportsInt | SupportsIndex, nb_generations: SupportsInt | SupportsIndex) → float¶

Estimate the fixation probability of a single invader in an otherwise resident population.

Parameters:

invader (int)
resident (int)
nb_runs (int)
nb_generations (int) – Maximum time-steps per trial.

Returns:

Estimated fixation probability in [0, 1].

Return type:

float

estimate_strategy_distribution(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC, nb_runs: SupportsInt | SupportsIndex, nb_generations: SupportsInt | SupportsIndex, transitory: SupportsInt | SupportsIndex, tolerance: SupportsFloat | SupportsIndex = 0.0, check_every: SupportsInt | SupportsIndex = 0) → tuple[Annotated[numpy.typing.NDArray[numpy.float64], '[m, 1]'], Annotated[numpy.typing.NDArray[numpy.float64], '[m, 1]']]¶

Estimate time-averaged strategy frequencies after the transitory period.

Returns:: (mean_frequencies, standard_errors), each of length nb_strategies.
Return type:: tuple[numpy.ndarray, numpy.ndarray]

initialize(*args, **kwargs)¶

Overloaded function.

initialize(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC, init_state: typing.Annotated[numpy.typing.ArrayLike, numpy.uint64, “[m, 1]”]) -> None

Initialise a manual-stepping session with the given strategy counts.

Parameters:: init_state (numpy.ndarray) – Strategy count vector of length nb_strategies; sum must equal population_size.

initialize(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC) -> None

Initialise a manual-stepping session with a uniformly random strategy assignment.

mean_population_state(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC) → Annotated[numpy.typing.NDArray[numpy.uint64], '[m, 1]']¶: Return strategy count vector (length = nb_strategies).

mu(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC) → float¶

nb_strategies(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC) → int¶

population_size(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC) → int¶

population_strategies(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC) → list[int]¶: Return per-node strategy assignments as a list of integers (length = population_size).

run(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC, nb_generations: SupportsInt | SupportsIndex, transitory: SupportsInt | SupportsIndex, init_state: Annotated[numpy.typing.ArrayLike, numpy.uint64, '[m, 1]']) → Annotated[numpy.typing.NDArray[numpy.uint64], '[m, n]']¶

Run a single trajectory and return aggregate strategy counts per generation.

Returns:: Matrix of shape (nb_generations - transitory, nb_strategies).
Return type:: numpy.ndarray

run_snapshots(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC, nb_generations: SupportsInt | SupportsIndex, transitory: SupportsInt | SupportsIndex, snapshot_interval: SupportsInt | SupportsIndex, init_state: Annotated[numpy.typing.ArrayLike, numpy.uint64, '[m, 1]'], callback: object) → None¶

Run a trajectory and call callback(generation, population) at each snapshot.

Parameters:

snapshot_interval (int) – Call callback every this many generations after transitory.
callback (callable) – Called as callback(generation: int, population: list[int]).

set_beta(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC, beta: SupportsFloat | SupportsIndex) → None¶

set_mu(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC, mu: SupportsFloat | SupportsIndex) → None¶

step(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC) → None¶

Advance the session by one generation.

For asynchronous rules: N individual update steps (one per node on average). For synchronous rules (e.g. LinearProportional): one full simultaneous sweep. Raises RuntimeError if initialize() has not been called first.

topology(self: egttools.numerical.numerical_.structure.NetworkMCEstimatorPC) → list[list[int]]¶

__annotations__ = {}¶