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P

PayoffLearning - class learningRules.PayoffLearning.
A learning rule where individuals learn the payoffs in a n-strategy game.
PayoffLearning(Game) - Constructor for class learningRules.PayoffLearning
 
PayoffLearningModel - class models.PayoffLearningModel.
 
PayoffLearningModel(Individual[], Game, ConnectionRule, LearningRule, double) - Constructor for class models.PayoffLearningModel
 
pickStrat(Individual) - Method in class connectionRules.NeighborReinforce
Choose a strategy for the individual based on the wieghts by the standard reinforcement method.
pickStrat(Individual) - Method in class learningRules.StratReinforce
Chooses a strategy based on standard reinforcement method
pickStrat(Individual) - Method in class learningRules.SmoothReinforcement
Picks a strategy based on a logisitic response rule similar to smoothed fictitious play this is for multi domain models
pickStrat(Individual) - Method in class learningRules.SmoothPayoffLearning
This function chooses a strategy for the player.
pickStrat(Individual) - Method in class learningRules.SmoothBGLearning
This function chooses a strategy for the player.
pickStrat(Individual) - Method in class learningRules.PayoffLearning
Chooses the strategy with the highest expected payoff.
pickStrat(Individual) - Method in class learningRules.MyopicBRLearning
This function chooses a strategy which is a best response to the collective action of the players last round.
pickStrat(Individual) - Method in interface learningRules.LearningRule
Pick a strategy to play in a new generation
pickStrat(Individual) - Method in class learningRules.ImitateBestLearning
This function chooses the strategy which did best on the previous round.
pickStrat(Individual) - Method in class learningRules.HybridLearning
Chooses the strategy with the highest expectation
pickStrat(Individual) - Method in class learningRules.CondorcetLearning
This function censuses individual in the neighborhood and adopts the strategy used by the majority last round.
pickStrat(Individual) - Method in class learningRules.BgLearning
This function chooses a strategy for the player.
pickStrat(Individual, int) - Method in class learningRules.SmoothReinforcement
Picks a strategy based on a logisitic response rule similar to smoothed fictitious play this is for signaling games
playGame() - Method in class utilities.Individual
Plays the game with each neighbor.
playGen() - Method in class models.StandardModel
Plays a generation and then resets.
playGen() - Method in class models.SingleDPL
Resets then plays a generation.
playGen() - Method in class models.RandomNetworkModel
Resets then plays a generation.
playGen() - Method in class models.PayoffLearningModel
Resets then plays a generation.
playGen() - Method in class models.HybridModel
Resets then plays a generation.
playGen() - Method in class models.BiasedNetworkModel
Resets then plays a generation.
playGen() - Method in class models.BgModel
Resets then plays a generation.
printResults() - Method in class models.SignalingGame
Outputs the probabilities that each strategy is played to StdOut
probs(double[]) - Static method in class learningRules.SmoothReinforcement
This calculates 1/(e^x[0]+e^x[1]+...+e^x[n]) It returns a double which is a rounded version of the result
probs(double[]) - Static method in class learningRules.SmoothBGLearning
This calculates 1/(e^x[0]+e^x[1]+...+e^x[n]) It returns a double which is a rounded version of the result
process() - Method in class utilities.NeuQuant
 
processPayoff(Individual) - Method in class connectionRules.NeighborReinforce
This function is run after the payoffs are updated for all individuals.
processPayoff(Individual) - Method in class learningRules.StratReinforce
Processes the payoff by adding the payoff to the weight of the strategy choosen on this round
processPayoff(Individual) - Method in class learningRules.SmoothReinforcement
Processes the payoff by adding the payoff to the weight of the strategy choosen on this round
processPayoff(Individual) - Method in class learningRules.SmoothPayoffLearning
This processes the payoff by updating the agents beliefs about each payoff based on the information received this round.
processPayoff(Individual) - Method in class learningRules.SmoothBGLearning
This processes a payoff for an individual.
processPayoff(Individual) - Method in class learningRules.PayoffLearning
This processes the payoff by updating the agents beliefs about each payoff based on the information received this round.
processPayoff(Individual) - Method in class learningRules.MyopicBRLearning
Processing a payoff does nothing, since there are no beliefs to be updated.
processPayoff(Individual) - Method in interface learningRules.LearningRule
Process the payoffs once they have played the game fully
processPayoff(Individual) - Method in class learningRules.ImitateBestLearning
Processing a payoff does nothing, since there are no beliefs to be updated.
processPayoff(Individual) - Method in class learningRules.HybridLearning
Updates beliefs based on the payoffs of neighbors.
processPayoff(Individual) - Method in class learningRules.CondorcetLearning
All this does is update LastStrat.
processPayoff(Individual) - Method in class learningRules.BgLearning
This processes a payoff for an individual.
processPayoff(Individual, int) - Method in class learningRules.SmoothReinforcement
Processes the payoff by adding the payoff to the weight of the strategy choosen on this round.

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