egttools.games.PGG¶
- class PGG(group_size, cost, multiplying_factor, strategies)[source]¶
Bases:
AbstractNPlayerGame
Classical Public Goods game with only 2 possible contributions (o or cost).
- Parameters
group_size (int) – Size of the group playing the game.
cost (float) – Cost of cooperation.
multiplying_factor (float) – The sum of contributions to the public good is multiplied by this factor before being divided equally among all players.
strategies (List[egttools.behaviors.pgg_behaviors.PGGOneShotStrategy]) – A list of strategies that will play the game.
Methods
Estimates the fitness for a player_type in the population with state :param strategies.
Estimates the payoffs for each strategy and returns the values in a matrix.
Size of the group.
Number of different group configurations.
Number of different strategies playing the game.
Returns the payoff of a strategy given a group composition.
Returns the payoff matrix of the game.
Updates the vector of payoffs with the payoffs of each player after playing the game.
Stores the payoff matrix in a txt file.
returns the type of game.
update an entry of the payoff matrix
- __init__(group_size, cost, multiplying_factor, strategies)[source]¶
Classical Public Goods game with only 2 possible contributions (o or cost).
- Parameters
group_size (int) – Size of the group playing the game.
cost (float) – Cost of cooperation.
multiplying_factor (float) – The sum of contributions to the public good is multiplied by this factor before being divided equally among all players.
strategies (List[egttools.behaviors.pgg_behaviors.PGGOneShotStrategy]) – A list of strategies that will play the game.
- __new__(**kwargs)¶
- calculate_fitness(self: egttools.numerical.numerical.games.AbstractNPlayerGame, strategy_index: int, pop_size: int, strategies: numpy.ndarray[numpy.uint64[m, 1]]) float ¶
Estimates the fitness for a player_type in the population with state :param strategies.
This function assumes that the player with strategy player_type is not included in the vector of strategy counts strategies.
- Parameters
strategy_index (int) – The index of the strategy used by the player.
pop_size (int) – The size of the population.
strategies (numpy.ndarray[numpy.uint64[m, 1]]) – A vector of counts of each strategy. The current state of the population.
- Returns
The fitness of the strategy in the population state given by strategies.
- Return type
- calculate_payoffs()[source]¶
Estimates the payoffs for each strategy and returns the values in a matrix.
Each row of the matrix represents a strategy and each column a game state. E.g., in case of a 2 player game, each entry a_ij gives the payoff for strategy i against strategy j. In case of a group game, each entry a_ij gives the payoff of strategy i for game state j, which represents the group composition.
- Returns
A matrix with the expected payoffs for each strategy given each possible game state.
- Return type
numpy.ndarray[numpy.float64[m, n]]
- group_size(self: egttools.numerical.numerical.games.AbstractNPlayerGame) int ¶
Size of the group.
- nb_group_configurations(self: egttools.numerical.numerical.games.AbstractNPlayerGame) int ¶
Number of different group configurations.
- nb_strategies(self: egttools.numerical.numerical.games.AbstractNPlayerGame) int ¶
Number of different strategies playing the game.
- payoff(self: egttools.numerical.numerical.games.AbstractNPlayerGame, strategy: int, group_composition: List[int]) float ¶
Returns the payoff of a strategy given a group composition.
If the group composition does not include the strategy, the payoff should be zero.
- payoffs(self: egttools.numerical.numerical.games.AbstractNPlayerGame) numpy.ndarray[numpy.float64[m, n]] ¶
Returns the payoff matrix of the game.
- Returns
The payoff matrix.
- Return type
- play(group_composition, game_payoffs)[source]¶
Updates the vector of payoffs with the payoffs of each player after playing the game.
This method will run the game using the players and player types defined in :param group_composition, and will update the vector :param game_payoffs with the resulting payoff of each player.