Uses of Interface
learningRules.LearningRule

Packages that use LearningRule
connectionRules   
learningRules   
models   
utilities   
 

Uses of LearningRule in connectionRules
 

Classes in connectionRules that implement LearningRule
 class NeighborReinforce
          A strategy learning rule that is like reinforcement dynamics.
 

Uses of LearningRule in learningRules
 

Classes in learningRules that implement LearningRule
 class BgLearning
          This is a strategy learning model for the Bala Goyal Model.
 class CondorcetLearning
          This class is a Condorcet Learning Rule.
 class HybridLearning
          This class implements the Hybrid learning rule.
 class ImitateBestLearning
          This is a learning rule where an individual adopts the strategy that did best on the previous round from among those strategies choosen by a neighbor.
 class MyopicBRLearning
          This class implements myopic best response learning.
 class PayoffLearning
          A learning rule where individuals learn the payoffs in a n-strategy game.
 class SmoothBGLearning
          This is a strategy learning model for the Bala Goyal Model.
 class SmoothPayoffLearning
          A learning rule where individuals learn the payoffs in a n-strategy game.
 class SmoothReinforcement
          This implements a modification of the standard reinforcement dynamics.
 class StratReinforce
          Simple reinforcement learning for strategies
 

Uses of LearningRule in models
 

Methods in models with parameters of type LearningRule
 void StandardModel.createPlayersHigh(Individual[] inds, int s, ConnectionRule c, LearningRule l, double m, double z)
          Forms a new network of specified size; expects random to be set.
 void StandardModel.createPlayersHigh(Individual[] inds, int s, ConnectionRule c, LearningRule l, Game g, double m, double z)
          Forms a new network of specified size; expects random to be set.
 void SimpleCondorcetModel.createPlayers(Individual[] is, MutationRule mr, LearningRule lr, ConnectionRule cr)
          Sets up the individuals
 void HybridModel.createPlayersHigh(Individual[] inds, int s, ConnectionRule c, LearningRule l, double m, double z)
          Forms a new network of specified size; expects random to be set.
 void HybridModel.createPlayersHigh(Individual[] inds, int s, ConnectionRule c, LearningRule l, Game g, double m, double z)
          Forms a new network of specified size; expects random to be set.
 void BgModel.createPlayersHigh(Individual[] inds, int s, ConnectionRule c, LearningRule l, double m, double z)
          Forms a new network of specified size; expects random to be set.
 void BgModel.createPlayersHigh(Individual[] inds, int s, ConnectionRule c, LearningRule l, Game g, double m, double z)
          Forms a new network of specified size; expects random to be set.
 

Constructors in models with parameters of type LearningRule
RandomNetworkModel(Individual[] i, Game g, LearningRule l, double m, double p)
           
PayoffLearningModel(Individual[] i, Game g, ConnectionRule c, LearningRule l, double m)
           
BiasedNetworkModel(Individual[] i, Game g, LearningRule l, double m, double min, double max)
           
 

Uses of LearningRule in utilities
 

Methods in utilities that return LearningRule
 LearningRule Individual.getLearningRule()
          Returns the learning rule
 

Methods in utilities with parameters of type LearningRule
 void Individual.setLearningRule(LearningRule l)
          Sets the learning rule for updating strategies
 

Constructors in utilities with parameters of type LearningRule
Individual(int i, int s, MersenneTwister r, Individual[] nw, ConnectionRule c, LearningRule l)
          Returns and individual object, but with more initialized.