egttools.games.CRDGameTU

class CRDGameTU(self: egttools.numerical.numerical_.games.CRDGameTU, endowment: SupportsInt | SupportsIndex, threshold: SupportsInt | SupportsIndex, nb_rounds: SupportsInt | SupportsIndex, group_size: SupportsInt | SupportsIndex, risk: SupportsFloat | SupportsIndex, tu: egttools.numerical.numerical_.distributions.TimingUncertainty, strategies: list)

Bases: AbstractGame

Collective risk dilemma with timing uncertainty.

Parameters:

• endowment (int) – Initial endowment of each player.

• threshold (int) – Collective target required to avoid risk.

• nb_rounds (int) – Maximum number of rounds.

• group_size (int) – Number of players per group.

• risk (float) – Probability of failure if the target is not met.

• tu (TimingUncertainty) – Object modeling timing uncertainty.

• strategies (list[AbstractCRDStrategy]) – List of strategy instances.

Methods

calculate_fitness	Computes the fitness of a strategy in a given population state.
calculate_group_achievement	Calculates group achievement based on a stationary distribution.
calculate_payoffs	Computes the expected payoffs for each strategy across all group configurations.
calculate_polarization	Computes contribution polarization in a given population state.
calculate_polarization_success	Computes contribution polarization among successful groups.
calculate_population_group_achievement	Calculates group achievement for a given population state.
nb_strategies
payoff	Returns the payoff of a strategy given a group composition.
payoffs	Returns the matrix of expected payoffs.
play	Executes one iteration of the CRD game using a specific group composition.
save_payoffs	Saves the payoff matrix to a text file.
type

Attributes

endowment
group_size
min_rounds
nb_states
risk
strategies	List of strategy objects participating in the game.
target

__init__(self: egttools.numerical.numerical_.games.CRDGameTU, endowment: SupportsInt | SupportsIndex, threshold: SupportsInt | SupportsIndex, nb_rounds: SupportsInt | SupportsIndex, group_size: SupportsInt | SupportsIndex, risk: SupportsFloat | SupportsIndex, tu: egttools.numerical.numerical_.distributions.TimingUncertainty, strategies: list) → None

Collective risk dilemma with timing uncertainty.

Parameters:

• endowment (int) – Initial endowment of each player.

• threshold (int) – Collective target required to avoid risk.

• nb_rounds (int) – Maximum number of rounds.

• group_size (int) – Number of players per group.

• risk (float) – Probability of failure if the target is not met.

• tu (TimingUncertainty) – Object modeling timing uncertainty.

• strategies (list[AbstractCRDStrategy]) – List of strategy instances.

__new__(**kwargs)

__str__(self: egttools.numerical.numerical_.games.CRDGameTU) → str

calculate_fitness(self: egttools.numerical.numerical_.games.CRDGameTU, player_strategy: SupportsInt | SupportsIndex, pop_size: SupportsInt | SupportsIndex, population_state: Annotated[numpy.typing.NDArray[numpy.uint64], '[m, 1]']) → float

Computes the fitness of a strategy in a given population state.

Parameters:

• player_strategy (int) – Index of the focal strategy.

• pop_size (int) – Total population size.

• population_state (numpy.ndarray) – Vector of strategy counts.

Return type:

float

calculate_group_achievement(self: egttools.numerical.numerical_.games.CRDGameTU, population_size: SupportsInt | SupportsIndex, stationary_distribution: Annotated[numpy.typing.NDArray[numpy.float64], '[m, 1]']) → float

Calculates group achievement based on a stationary distribution.

calculate_payoffs(self: egttools.numerical.numerical_.games.CRDGameTU) → Annotated[numpy.typing.NDArray[numpy.float64], '[m, n]']

Computes the expected payoffs for each strategy across all group configurations.

Returns:

Matrix of expected payoffs.

Return type:

numpy.ndarray

calculate_polarization(self: egttools.numerical.numerical_.games.CRDGameTU, population_size: SupportsInt | SupportsIndex, population_state: Annotated[numpy.typing.NDArray[numpy.float64], '[m, 1]']) → Annotated[numpy.typing.NDArray[numpy.float64], '[3, 1]']

Computes contribution polarization in a given population state.

calculate_polarization_success(self: egttools.numerical.numerical_.games.CRDGameTU, population_size: SupportsInt | SupportsIndex, population_state: Annotated[numpy.typing.NDArray[numpy.float64], '[m, 1]']) → Annotated[numpy.typing.NDArray[numpy.float64], '[3, 1]']

Computes contribution polarization among successful groups.

calculate_population_group_achievement(self: egttools.numerical.numerical_.games.CRDGameTU, population_size: SupportsInt | SupportsIndex, population_state: Annotated[numpy.typing.NDArray[numpy.uint64], '[m, 1]']) → float

Calculates group achievement for a given population state.

nb_strategies(self: egttools.numerical.numerical_.games.CRDGameTU) → int

payoff(self: egttools.numerical.numerical_.games.CRDGameTU, strategy: SupportsInt | SupportsIndex, group_composition: collections.abc.Sequence[SupportsInt | SupportsIndex]) → float

Returns the payoff of a strategy given a group composition.

Parameters:

• strategy (int) – Strategy index.

• group_composition (list[int]) – Group composition vector.

Return type:

float

payoffs(self: egttools.numerical.numerical_.games.CRDGameTU) → Annotated[numpy.typing.NDArray[numpy.float64], '[m, n]']

Returns the matrix of expected payoffs.

play(self: egttools.numerical.numerical_.games.CRDGameTU, arg0: collections.abc.Sequence[SupportsInt | SupportsIndex], arg1: collections.abc.Sequence[SupportsFloat | SupportsIndex]) → None

Executes one iteration of the CRD game using a specific group composition.

Parameters:

• group_composition (numpy.ndarray) – Number of players per strategy in the group.

• game_payoffs (numpy.ndarray) – Output vector for player payoffs.

save_payoffs(self: egttools.numerical.numerical_.games.CRDGameTU, arg0: str) → None

Saves the payoff matrix to a text file.

Parameters:

file_name (str) – Path to the output file.

type(self: egttools.numerical.numerical_.games.CRDGameTU) → str

__annotations__ = {}

property endowment

property group_size

property min_rounds

property nb_states

property risk

property strategies

List of strategy objects participating in the game.

property target