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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.
Overview
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