egttools.games.abstract_games.AbstractNPlayerGameExpectedPayoff¶

class AbstractNPlayerGameExpectedPayoff(self: egttools.numerical.numerical.games.AbstractNPlayerGame, nb_strategies: int, group_size: int)[source]¶

Bases: AbstractNPlayerGame

This abstract Game class can be used in most scenarios where the fitness of a strategy is calculated as its expected payoff given the population state.

It assumes that the game is N player, since the fitness of a strategy given a population state is calculated as the expected payoff of that strategy over all possible group combinations in the given state.

Notes

It might be a good idea to overwrite the methods __str__, type, and save_payoffs to adapt to your given game implementation

It assumes that you have at least the following attributes:

1. And an attribute self.nb_strategies_ which contains the number of strategies that you are going to analyse for the given game. 2. self.payoffs() returns a numpy.ndarray and contain the payoff matrix of the game. This array is of shape (self.nb_strategies(), self.nb_group_configurations()), where self.nb_group_configurations() is the number of possible combinations of strategies in the group. Thus, each row should give the (expected) payoff of the row strategy when playing in a group with the column configuration. The payoff method provides an easy way to access the payoffs for any group composition, by taking as arguments the index of the row strategy and a List with the count of each possible strategy in the group.

You must still implement the methods play which should define how the game assigns payoffs to each strategy for a given game context. In particular, calculate_payoffs should fill the array self.payoffs_ with the correct values as explained above. We recommend that you run this method in the __init__ (initialization of the object) since, these values must be set before passing the game object to the numerical simulator (e.g., egttools.numerical.PairwiseComparisonNumerical).

Abstract N-Player Game.

This abstract Game class can be used in most scenarios where the fitness of a strategy is calculated as its expected payoff given the population state.

It assumes that the game is N player, since the fitness of a strategy given a population state is calculated as the expected payoff of that strategy over all possible group combinations in the given state.

Notes

It might be a good idea to overwrite the methods __str__, type, and save_payoffs to adapt to your given game implementation

It assumes that you have at least the following attributes: 1. And an attribute self.nb_strategies_ which contains the number of strategies that you are going to analyse for the given game. 2. self.payoffs_ which must be a numpy.ndarray and contain the payoff matrix of the game. This array must be of shape (self.nb_strategies_, nb_group_configurations), where nb_group_configurations is the number of possible combinations of strategies in the group. Thus, each row should give the (expected) payoff of the row strategy when playing in a group with the column configuration. The payoff method provides an easy way to access the payoffs for any group composition, by taking as arguments the index of the row strategy and a List with the count of each possible strategy in the group.

You must still implement the methods play and calculate_payoffs which should define how the game assigns payoffs to each strategy for each possible game context. In particular, calculate_payoffs should fill the array self.payoffs_ with the correct values as explained above. We recommend that you run this method in the __init__ (initialization of the object) since, these values must be set before passing the game object to the numerical simulator (e.g., egttools.numerical.PairwiseComparisonNumerical).

Parameters

nb_strategies (int) – total number of possible strategies.
group_size (int) – size of the group in which the game will take place.

See also

egttools.games.AbstractGame

Methods

`calculate_fitness`	Estimates the fitness for a player_type in the population with state :param strategies.
`calculate_payoffs`	This method calculates the payoffs for each strategy in each possible group configuration.
`group_size`	Size of the group.
`nb_group_configurations`	Number of different group configurations.
`nb_strategies`	Number of different strategies playing the game.
`payoff`	Returns the payoff of a strategy given a group composition.
`payoffs`	Returns the payoff matrix of the game.
`play`	This method fills the `game_payoffs` container with the payoff of each strategy given the `group_composition`.
`save_payoffs`	Stores the payoff matrix in a txt file.
`type`	returns the type of game.
`update_payoff`	update an entry of the payoff matrix

__init__(self: egttools.numerical.numerical.games.AbstractNPlayerGame, nb_strategies: int, group_size: int) → None¶

Abstract N-Player Game.

This abstract Game class can be used in most scenarios where the fitness of a strategy is calculated as its expected payoff given the population state.

It assumes that the game is N player, since the fitness of a strategy given a population state is calculated as the expected payoff of that strategy over all possible group combinations in the given state.

Notes

It might be a good idea to overwrite the methods __str__, type, and save_payoffs to adapt to your given game implementation

It assumes that you have at least the following attributes: 1. And an attribute self.nb_strategies_ which contains the number of strategies that you are going to analyse for the given game. 2. self.payoffs_ which must be a numpy.ndarray and contain the payoff matrix of the game. This array must be of shape (self.nb_strategies_, nb_group_configurations), where nb_group_configurations is the number of possible combinations of strategies in the group. Thus, each row should give the (expected) payoff of the row strategy when playing in a group with the column configuration. The payoff method provides an easy way to access the payoffs for any group composition, by taking as arguments the index of the row strategy and a List with the count of each possible strategy in the group.

You must still implement the methods play and calculate_payoffs which should define how the game assigns payoffs to each strategy for each possible game context. In particular, calculate_payoffs should fill the array self.payoffs_ with the correct values as explained above. We recommend that you run this method in the __init__ (initialization of the object) since, these values must be set before passing the game object to the numerical simulator (e.g., egttools.numerical.PairwiseComparisonNumerical).

Parameters

nb_strategies (int) – total number of possible strategies.
group_size (int) – size of the group in which the game will take place.