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Packages that use Individual | |
connectionRules | |
learningRules | |
models | |
mutationRules | |
utilities |
Uses of Individual in connectionRules |
Methods in connectionRules with parameters of type Individual | |
void |
NeighborReinforce.newGeneration(Individual i)
Prepares the individual for a new round of play. |
void |
NeighborReinforce.processPayoff(Individual i)
This function is run after the payoffs are updated for all individuals. |
int |
NeighborReinforce.pickStrat(Individual i)
Choose a strategy for the individual based on the wieghts by the standard reinforcement method. |
java.util.HashSet |
ConnectNothing.formConnection(Individual i)
|
java.util.HashSet |
ConnectionRule.formConnection(Individual i)
The function to form connections |
Uses of Individual in learningRules |
Methods in learningRules with parameters of type Individual | |
void |
StratReinforce.newGeneration(Individual i)
Prepares the individual for a new generation. |
void |
StratReinforce.processPayoff(Individual i)
Processes the payoff by adding the payoff to the weight of the strategy choosen on this round |
int |
StratReinforce.pickStrat(Individual i)
Chooses a strategy based on standard reinforcement method |
void |
SmoothReinforcement.newGeneration(Individual i)
Prepares the individual for a new generation. |
void |
SmoothReinforcement.processPayoff(Individual i)
Processes the payoff by adding the payoff to the weight of the strategy choosen on this round |
void |
SmoothReinforcement.processPayoff(Individual i,
int d)
Processes the payoff by adding the payoff to the weight of the strategy choosen on this round. |
int |
SmoothReinforcement.pickStrat(Individual i)
Picks a strategy based on a logisitic response rule similar to smoothed fictitious play this is for multi domain models |
int |
SmoothReinforcement.pickStrat(Individual i,
int d)
Picks a strategy based on a logisitic response rule similar to smoothed fictitious play this is for signaling games |
void |
SmoothPayoffLearning.newGeneration(Individual i)
This does nothing since there is no discounting or anything. |
void |
SmoothPayoffLearning.processPayoff(Individual i)
This processes the payoff by updating the agents beliefs about each payoff based on the information received this round. |
int |
SmoothPayoffLearning.pickStrat(Individual i)
This function chooses a strategy for the player. |
void |
SmoothBGLearning.newGeneration(Individual i)
This does nothing since there is no discounting or anything. |
void |
SmoothBGLearning.processPayoff(Individual i)
This processes a payoff for an individual. |
int |
SmoothBGLearning.pickStrat(Individual i)
This function chooses a strategy for the player. |
void |
PayoffLearning.newGeneration(Individual i)
This does nothing since there is no discounting or anything. |
void |
PayoffLearning.processPayoff(Individual i)
This processes the payoff by updating the agents beliefs about each payoff based on the information received this round. |
int |
PayoffLearning.pickStrat(Individual i)
Chooses the strategy with the highest expected payoff. |
void |
MyopicBRLearning.newGeneration(Individual i)
This does nothing since there is no discounting or anything. |
void |
MyopicBRLearning.processPayoff(Individual i)
Processing a payoff does nothing, since there are no beliefs to be updated. |
int |
MyopicBRLearning.pickStrat(Individual i)
This function chooses a strategy which is a best response to the collective action of the players last round. |
void |
LearningRule.newGeneration(Individual i)
A function that resets the learning rule for a new generation |
void |
LearningRule.processPayoff(Individual i)
Process the payoffs once they have played the game fully |
int |
LearningRule.pickStrat(Individual i)
Pick a strategy to play in a new generation |
void |
ImitateBestLearning.newGeneration(Individual i)
This does nothing since there is no discounting or anything. |
void |
ImitateBestLearning.processPayoff(Individual i)
Processing a payoff does nothing, since there are no beliefs to be updated. |
int |
ImitateBestLearning.pickStrat(Individual i)
This function chooses the strategy which did best on the previous round. |
void |
HybridLearning.newGeneration(Individual i)
The new generation method implements the averaging. |
int |
HybridLearning.pickStrat(Individual i)
Chooses the strategy with the highest expectation |
void |
HybridLearning.processPayoff(Individual i)
Updates beliefs based on the payoffs of neighbors. |
void |
CondorcetLearning.newGeneration(Individual i)
There is no updating so this function does nothing |
int |
CondorcetLearning.pickStrat(Individual i)
This function censuses individual in the neighborhood and adopts the strategy used by the majority last round. |
void |
CondorcetLearning.processPayoff(Individual i)
All this does is update LastStrat. |
void |
BgLearning.newGeneration(Individual i)
This does nothing since there is no discounting or anything. |
void |
BgLearning.processPayoff(Individual i)
This processes a payoff for an individual. |
int |
BgLearning.pickStrat(Individual i)
This function chooses a strategy for the player. |
Uses of Individual in models |
Methods in models that return Individual | |
Individual[] |
StandardModel.getIndividuals()
Returns the set of individuals for the model. |
Individual |
SingleDPL.getIndivids()
Returns the value of indivis. |
Individual |
SingleBGM.getIndividual()
Returns the set of individuals for the model. |
Individual[] |
SimpleCondorcetModel.getIndividuals()
Returns the set of individuals for the model. |
Individual[] |
RandomNetworkModel.getIndivids()
Returns the value of indivis. |
Individual[] |
PayoffLearningModel.getIndivids()
Returns the value of indivis. |
Individual[] |
HybridModel.getIndividuals()
Returns the set of individuals for the model. |
Individual[] |
BiasedNetworkModel.getIndivids()
Returns the value of indivis. |
Individual[] |
BgModel.getIndividuals()
Returns the set of individuals for the model. |
Methods in models with parameters of type Individual | |
void |
StandardModel.setIndividuals(Individual[] i)
Sets the list of individuals for the model. |
void |
StandardModel.createPlayersLow(Individual[] inds)
Forms a new network of specified size; doesn't expect much to be set. |
void |
StandardModel.createPlayersHigh(Individual[] inds,
int s)
Forms a new network of specified size; expects random to be set. |
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 |
SingleDPL.setIndividual(Individual individs)
Sets the value of indivis. |
void |
SingleBGM.setIndividual(Individual i)
Sets the list of individuals for the model. |
void |
SimpleCondorcetModel.setIndividuals(Individual[] i)
Sets the list of individuals for the model. |
void |
SimpleCondorcetModel.createPlayers(Individual[] is,
MutationRule mr,
LearningRule lr,
ConnectionRule cr)
Sets up the individuals |
void |
RandomNetworkModel.setIndivids(Individual[] individs)
Sets the value of indivis. |
void |
PayoffLearningModel.setIndivids(Individual[] individs)
Sets the value of indivis. |
void |
HybridModel.setIndividuals(Individual[] i)
Sets the list of individuals for the model. |
void |
HybridModel.createPlayersLow(Individual[] inds)
Forms a new network of specified size; doesn't expect much to be set. |
void |
HybridModel.createPlayersHigh(Individual[] inds,
int s)
Forms a new network of specified size; expects random to be set. |
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 |
BiasedNetworkModel.setIndivids(Individual[] individs)
Sets the value of indivis. |
void |
BgModel.setIndividuals(Individual[] i)
Sets the list of individuals for the model. |
void |
BgModel.createPlayersLow(Individual[] inds)
Forms a new network of specified size; doesn't expect much to be set. |
void |
BgModel.createPlayersHigh(Individual[] inds,
int s)
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 Individual | |
RandomNetworkModel(Individual[] i,
Game g,
LearningRule l,
double m,
double p)
|
|
PayoffLearningModel(Individual[] i,
Game g,
ConnectionRule c,
LearningRule l,
double m)
|
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BiasedNetworkModel(Individual[] i,
Game g,
LearningRule l,
double m,
double min,
double max)
|
Uses of Individual in mutationRules |
Methods in mutationRules with parameters of type Individual | |
void |
NoMutate.mutate(Individual i)
Does nothing |
void |
MutationRule.mutate(Individual i)
|
void |
MutateStrategy.mutate(Individual i)
This function mutates based on the mutation matrix already established by the constructor. |
void |
MutateBeliefs.mutate(Individual i)
Mutates weights, beliefs, and alpha/beta values. |
Uses of Individual in utilities |
Methods in utilities that return Individual | |
Individual[] |
Individual.getNetwork()
Returns the network (all other individuals in the model). |
static Individual[] |
GraphIO.adjacencyMatrix(int[][] m)
Turns an adjacency matrix into a collection of Individuals. |
Methods in utilities with parameters of type Individual | |
void |
Individual.setNetwork(Individual[] nw)
Sets the network (all other individuals in the model). |
void |
Individual.addConnected(Individual i)
Adds a neighbor to an individuals current neighbors. |
void |
BeliefAnimation.start(Individual ind,
java.lang.String fn)
|
Constructors in utilities with parameters of type Individual | |
Individual(int i,
int s,
MersenneTwister r,
Individual[] nw,
ConnectionRule c,
LearningRule l)
Returns and individual object, but with more initialized. |
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