egttools.games.CommonPoolResourceDilemmaCommitment

class CommonPoolResourceDilemmaCommitment(group_size, a, b, cost, fine, strategies)

Bases: AbstractNPlayerGame

Common Pool resource game with commitment, but the threshold is set as another strategy :type group_size: int :param group_size: Size of the group playing the game. :type group_size: int :param a and b: Both a and b are the parameters of the game, if a>>b, then it’s attractive to invest in the CPR :type a and b: flaot :type cost: float :param cost: cost of making the commitment for commitment proposing strategy :type fine: float :param fine: fine for defection when accepting the commitment :type fine: float :type strategies: List[AbstractCPRStrategy] :param strategies: List of strategies which will play the game :type strategies: Lit[int]

Methods

add_group_extraction
calculate_expected_consumption
calculate_fitness	Computes the fitness of a given strategy in a population state.
calculate_payoffs	Computes and returns the full payoff matrix.
calculate_total_extraction
check_if_commitment_validated
get_nb_committed
group_size
nb_group_configurations
nb_strategies
payoff	Returns the payoff of a strategy in a given group context.
payoffs	Returns the payoff matrix.
play	Executes the game for a given group composition and fills the payoff vector.
save_payoffs	Saves the payoff matrix to a text file.
type
update_payoff	Updates an entry in the payoff matrix.

__init__(group_size, a, b, cost, fine, strategies)

Common Pool resource game with commitment, but the threshold is set as another strategy :type group_size: int :param group_size: Size of the group playing the game. :type group_size: int :param a and b: Both a and b are the parameters of the game, if a>>b, then it’s attractive to invest in the CPR :type a and b: flaot :type cost: float :param cost: cost of making the commitment for commitment proposing strategy :type fine: float :param fine: fine for defection when accepting the commitment :type fine: float :type strategies: List[AbstractCPRStrategy] :param strategies: List of strategies which will play the game :type strategies: Lit[int]

__new__(**kwargs)

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

add_group_extraction(group_extraction, group_composition)

calculate_expected_consumption(population_state)

Return type:

float

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

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

Parameters:

• strategy_index (int) – The strategy of the focal player.

• pop_size (int) – Total population size.

• strategies (numpy.ndarray) – Population state as a strategy count vector.

Returns:

Fitness of the focal strategy in the given state.

Return type:

float

calculate_payoffs()

Computes and returns the full payoff matrix.

Returns:

A matrix with expected payoffs. Each row represents a strategy, and each column a group configuration.

Return type:

numpy.ndarray

calculate_total_extraction(commitment_accepted, group_composition)

Return type:

float

check_if_commitment_validated(nb_committed, group_composition, commitment_accepted)

Return type:

None

get_nb_committed(group_composition)

Return type:

int

group_size(self: egttools.numerical.numerical_.games.AbstractNPlayerGame) → int

nb_group_configurations(self: egttools.numerical.numerical_.games.AbstractNPlayerGame) → int

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

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

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

Parameters:

• strategy (int) – The strategy index.

• group_composition (numpy.ndarray) – The group configuration.

Returns:

The corresponding payoff.

Return type:

float

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

Returns the payoff matrix.

Returns:

Matrix of shape (nb_strategies, nb_group_configurations).

Return type:

numpy.ndarray

play(group_composition, game_payoffs)

Executes the game for a given group composition and fills the payoff vector.

Parameters:

• group_composition (numpy.ndarray) – One-dimensional array containing the number of players of each strategy in the group.

• game_payoffs (numpy.ndarray) – Output container where the payoff of each strategy will be written.

Return type:

None

save_payoffs(self: egttools.numerical.numerical_.games.AbstractNPlayerGame, file_name: str) → None

Saves the payoff matrix to a text file.

Parameters:

file_name (str) – Destination file path.

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

update_payoff(self: egttools.numerical.numerical_.games.AbstractNPlayerGame, strategy_index: SupportsInt | SupportsIndex, group_configuration_index: SupportsInt | SupportsIndex, value: SupportsFloat | SupportsIndex) → None

Updates an entry in the payoff matrix.

Parameters:

• strategy_index (int) – Index of the strategy.

• group_configuration_index (int) – Index of the group composition.

• value (float) – The new payoff value.

__annotations__ = {}