egttools.games.CRDGame

class CRDGame(self: egttools.numerical.numerical_.games.CRDGame, endowment: SupportsInt | SupportsIndex, threshold: SupportsInt | SupportsIndex, nb_rounds: SupportsInt | SupportsIndex, group_size: SupportsInt | SupportsIndex, risk: SupportsFloat | SupportsIndex, enhancement_factor: SupportsFloat | SupportsIndex, strategies: list)

Bases: AbstractGame

Collective risk dilemma game.

Parameters:

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

• threshold (int) – Collective target the group must achieve to avoid risk.

• nb_rounds (int) – Number of rounds in the game.

• group_size (int) – Number of players in each group.

• risk (float) – Probability of losing remaining endowment if the target is not met.

• enhancement_factor (float) – Multiplier for successful cooperation.

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

Methods

calculate_fitness	Calculates the fitness of a strategy in a given population state.
calculate_group_achievement	Calculates group achievement given a stationary distribution.
calculate_payoffs	Computes the expected payoffs for each strategy under all group configurations.
calculate_polarization	Computes contribution polarization relative to the fair contribution.
calculate_polarization_success	Computes contribution polarization among successful groups.
calculate_population_group_achievement	Calculates group achievement for the population at a given state.
nb_strategies
payoff	Returns the payoff of a strategy in a given group composition.
payoffs	Returns the payoff matrix for all strategies and group configurations.
play	Plays a single round of the CRD game for the specified group composition.
save_payoffs	Saves the payoff matrix to a file.
type

Attributes

endowment
enhancement_factor
group_size
nb_rounds
nb_states
risk
strategies	List of strategy instances in the game.
target

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

Collective risk dilemma game.

Parameters:

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

• threshold (int) – Collective target the group must achieve to avoid risk.

• nb_rounds (int) – Number of rounds in the game.

• group_size (int) – Number of players in each group.

• risk (float) – Probability of losing remaining endowment if the target is not met.

• enhancement_factor (float) – Multiplier for successful cooperation.

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

__new__(**kwargs)

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

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

Calculates 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.CRDGame, population_size: SupportsInt | SupportsIndex, stationary_distribution: Annotated[numpy.typing.NDArray[numpy.float64], '[m, 1]']) → float

Calculates group achievement given a stationary distribution.

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

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

Return type:

numpy.ndarray

calculate_polarization(self: egttools.numerical.numerical_.games.CRDGame, 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 relative to the fair contribution.

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

Calculates group achievement for the population at a given state.

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

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

Returns the payoff of a strategy in a given group composition.

Parameters:

• strategy (int) – Index of the strategy.

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

Return type:

float

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

Returns the payoff matrix for all strategies and group configurations.

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

Plays a single round of the CRD game for the specified group composition.

Parameters:

• group_composition (numpy.ndarray) – Number of players using each strategy.

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

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

Saves the payoff matrix to a file.

Parameters:

file_name (str) – Output file path.

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

__annotations__ = {}

property endowment

property enhancement_factor

property group_size

property nb_rounds

property nb_states

property risk

property strategies

List of strategy instances in the game.

property target