Package org.evoludo.simulator.modules

Class RSP

Object

Module

Discrete

RSP

All Implemented Interfaces:

Runnable, IBS.HasIBS, IBS.HasIBS.DPairs, MilestoneListener, ODE.HasDE, ODE.HasODE, PDE.HasPDE, SDE.HasSDE, Features, Features.Pairs, HasHistogram, HasHistogram.Degree, HasHistogram.Fitness, HasHistogram.StatisticsStationary, HasMean, HasMean.Fitness, HasMean.Strategy, HasPop2D, HasPop2D.Fitness, HasPop2D.Strategy, HasPop3D, HasPop3D.Fitness, HasPop3D.Strategy, HasS3, CLOProvider

public class RSP
extends Discrete
implements IBS.HasIBS.DPairs, ODE.HasODE, SDE.HasSDE, PDE.HasPDE, HasPop2D.Strategy, HasPop3D.Strategy, HasMean.Strategy, HasS3, HasPop2D.Fitness, HasPop3D.Fitness, HasMean.Fitness, HasHistogram.Fitness, HasHistogram.Degree, HasHistogram.StatisticsStationary

Cyclical dynamics of the rock-scissors-paper game. A game that children, lizards and even bacteria enjoy playing!

Author:

Christoph Hauert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
class	RSP.IBSPop	The extension for IBS simulations specific to 3×3 games.

Nested classes/interfaces inherited from interface Features

Features.Groups, Features.Pairs, Features.Static

Nested classes/interfaces inherited from interface HasHistogram

HasHistogram.Degree, HasHistogram.Fitness, HasHistogram.StatisticsProbability, HasHistogram.StatisticsStationary, HasHistogram.StatisticsTime, HasHistogram.Strategy

Nested classes/interfaces inherited from interface HasMean

HasMean.Fitness, HasMean.Strategy

Nested classes/interfaces inherited from interface HasPop2D

HasPop2D.Fitness, HasPop2D.Strategy

Nested classes/interfaces inherited from interface HasPop3D

HasPop3D.Fitness, HasPop3D.Strategy

Nested classes/interfaces inherited from interface IBS.HasIBS

IBS.HasIBS.CGroups, IBS.HasIBS.CPairs, IBS.HasIBS.DGroups, IBS.HasIBS.DPairs, IBS.HasIBS.MCGroups, IBS.HasIBS.MCPairs

Field Summary

Fields

Modifier and Type	Field	Description
final CLOption	cloPayoff	Command line option to set the payoff matrix.
static final int	PAPER	The trait (and index) value of the strategy 'paper'.
(package private) double[][]	payoff	The 3×3 payoff matrix for interactions between the three types.
static final int	ROCK	The trait (and index) value of the strategy 'rock'.
static final int	SCISSORS	The trait (and index) value of the strategy 'scissors'.

Fields inherited from class Discrete

cloMonoStop, monoStop, mutation, species

Fields inherited from class Module

active, cloDeathRate, cloGeometry, cloNGroup, cloNPopulation, cloPhase2DAxis, cloSpeciesUpdateRate, cloTraitColors, cloTraitDisable, cloTraitNames, competition, deathRate, defaultColor, engine, ibs, ID, interaction, logger, map2fitness, markers, model, nActive, name, nGroup, nPopulation, nTraits, opponent, playerUpdate, speciesUpdateRate, structure, traitColor, traitName, trajectoryColor, VACANT

Fields inherited from interface HasS3

CORNER_LEFT, CORNER_RIGHT, CORNER_TOP, EDGE_BOTTOM, EDGE_LEFT, EDGE_RIGHT

Constructor Summary

Constructors

Constructor	Description
RSP(EvoLudo engine)	Create a new instance of the module for rock-scissors-paper games.

Method Summary

Modifier and Type	Method	Description
void	adjustCLO(CLOParser parser)	Providers of command line options may want to remove certain options that other providers provided by overriding this method.
void	avgScores(double[] density, int n, double[] avgscores)	Calculate the average payoff/score for the frequency of traits/strategies specified in the array density for interactions in groups of size n.
void	collectCLO(CLOParser parser)	All providers of command line options must implement this method to collect their options.
RSP.IBSPop	createIBSPop()	Opportunity to supply custom individual based simulations.
String	getAuthors()	Returns a string with information about the authors of the module.
int	getDependent()	Get the index of dependent type or -1 if Module does not have an dependent type.
double	getMaxGameScore()	Calculates and returns the maximum payoff/score of an individual.
double	getMinGameScore()	Calculates and returns the minimum payoff/score of an individual.
double	getMonoGameScore(int type)	Calculate and return the payoff/score of individuals in monomorphic populations with trait/strategy type.
double[][]	getPayoffs()	Get the payoff matrix.
String	getTitle()	Returns title of active module, e.g.
void	load()	Load new module and perform basic initializations.
void	mixedScores(int[] traitCount, double[] traitScore)	Calculate the average payoff/score in a finite population with the number of each trait/strategy provided in count for pairwise interactions.
double	pairScores(int me, int[] traitCount, double[] traitScore)	Calculate and return total (accumulated) payoff/score for pairwise interactions of the focal individual with trait/strategy me against opponents with different traits/strategies.
void	setPayoffs(double[][] payMatrix)	Set the payoff matrix to payMatrix.
void	unload()	Unload module and free all resources.

Methods inherited from class Discrete

add, getMaxMonoGameScore, getMinMonoGameScore, getMonoStop, getMutation, setMonoStop

Methods inherited from class Module

check, createGeometry, getActiveTraits, getCompetitionGeometry, getDeathRate, getGeometry, getIBSPopulation, getID, getInteractionGeometry, getKey, getMapToFitness, getMarkers, getMeanColors, getModelTypes, getNActive, getName, getNGroup, getNPopulation, getNRoles, getNSpecies, getNTraits, getOpponent, getPlayerUpdate, getSpecies, getSpecies, getSpeciesUpdateRate, getTraitColor, getTraitColors, getTraitName, getTraitNames, getTrajectoryColor, getVacant, init, isNeutral, processColorMap, reset, run, setActiveTraits, setDeathRate, setGeometries, setIBSPopulation, setModel, setName, setNGroup, setNPopulation, setNTraits, setOpponent, setSpeciesUpdateRate, setTraitColors, setTraitNames, setTrajectoryColor

Methods inherited from class Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface Features

isStatic

Methods inherited from interface Features.Pairs

isPairwise

Methods inherited from interface HasHistogram

getCustomLevels, getNTraits, getTraitColors

Methods inherited from interface HasS3

getS3Map, setS3Map

Methods inherited from interface IBS.HasIBS

createIBS

Methods inherited from interface MilestoneListener

modelDidInit, modelDidReset, modelLoaded, modelRelaxed, modelRunning, modelSettings, modelStopped, modelUnloaded, moduleLoaded, moduleRestored, moduleUnloaded

Methods inherited from interface ODE.HasDE

avgScores

Methods inherited from interface ODE.HasODE

createODE

Methods inherited from interface PDE.HasPDE

createPDE

Methods inherited from interface SDE.HasSDE

createSDE

Field Details

ROCK

public static final int ROCK

The trait (and index) value of the strategy 'rock'.

See Also:

• Constant Field Values

SCISSORS

public static final int SCISSORS

The trait (and index) value of the strategy 'scissors'.

See Also:

• Constant Field Values

PAPER

public static final int PAPER

The trait (and index) value of the strategy 'paper'.

See Also:

• Constant Field Values

payoff

double[][] payoff

The 3×3 payoff matrix for interactions between the three types.

cloPayoff

public final CLOption cloPayoff

Command line option to set the payoff matrix.

Constructor Details

RSP

public RSP(EvoLudo engine)

Create a new instance of the module for rock-scissors-paper games.

Parameters:

engine - the manager of modules and pacemaker for running the model

Method Details

load

public void load()

Description copied from class: Module

Load new module and perform basic initializations.

Overrides:

load in class Discrete

See Also:

• EvoLudo.loadModule(String)
• MilestoneListener.moduleLoaded()

unload

public void unload()

Description copied from class: Module

Unload module and free all resources.

Overrides:

unload in class Discrete

See Also:

• EvoLudo.unloadModule()
• MilestoneListener.moduleUnloaded()

getAuthors

public String getAuthors()

Description copied from class: Module

Returns a string with information about the authors of the module.

Overrides:

getAuthors in class Module

Returns:

the names of the authors

getTitle

public String getTitle()

Description copied from class: Module

Returns title of active module, e.g. 2x2 games in TBT returns "2x2 Games".

Specified by:

getTitle in class Module

Returns:

the title of active module

getDependent

public int getDependent()

Description copied from class: Module

Get the index of dependent type or -1 if Module does not have an dependent type.

Notes:

• Dependent types are used by replicator type models where the frequencies of all types must sum up to one. Currently only used by Discrete modules.
• Density modules do not have dependent types.
• By default use vacant type as the dependent

Specified by:

getDependent in interface ODE.HasDE

Overrides:

getDependent in class Module

Returns:

the index of the vacant type

getMinGameScore

public double getMinGameScore()

Description copied from class: Module

Calculates and returns the minimum payoff/score of an individual. This value is important for converting payoffs/scores into probabilities, for scaling graphical output and some optimizations.

Specified by:

getMinGameScore in class Module

Returns:

the minimum payoff/score

See Also:

• Model.getMinScore(int)

getMaxGameScore

public double getMaxGameScore()

Description copied from class: Module

Calculates and returns the maximum payoff/score of an individual. This value is important for converting payoffs/scores into probabilities, for scaling graphical output and some optimizations.

Specified by:

getMaxGameScore in class Module

Returns:

the maximum payoff/score

See Also:

• Model.getMaxScore(int)

getMonoGameScore

public double getMonoGameScore(int type)

Description copied from class: Discrete

Calculate and return the payoff/score of individuals in monomorphic populations with trait/strategy type.

Note: Optional implementation. Returns Double#NaN if not defined or not implemented.

Overrides:

getMonoGameScore in class Discrete

Parameters:

type - trait/strategy

Returns:

payoff/score in monomorphic population with trait/strategy type

pairScores

public double pairScores(int me,
 int[] traitCount,
 double[] traitScore)

Description copied from interface: IBS.HasIBS.DPairs

Calculate and return total (accumulated) payoff/score for pairwise interactions of the focal individual with trait/strategy me against opponents with different traits/strategies. The respective numbers of each of the nTraits opponent traits/strategies are provided in the array tCount. The payoffs/scores for each of the nTraits opponent traits/strategies must be stored and returned in the array tScore.

Important: must be overridden and implemented in subclasses that define game interactions between pairs of individuals (nGroup=2, pairwise=true), otherwise see IBS.HasIBS.DGroups.groupScores(int[], double[]).

Specified by:

pairScores in interface IBS.HasIBS.DPairs

Parameters:

me - the trait index of the focal individual

traitCount - number of opponents with each trait/strategy

traitScore - array for returning the scores of each opponent trait/strategy

Returns:

score of focal individual me accumulated over all interactions

avgScores

public void avgScores(double[] density,
 int n,
 double[] avgscores)

Description copied from interface: ODE.HasDE

Calculate the average payoff/score for the frequency of traits/strategies specified in the array density for interactions in groups of size n. The average payoffs/scores for each of the nTraits traits/strategies must be stored and returned in the array avgscores.

Note: needs to be thread safe for parallel processing of PDE's.

IMPORTANT: one of

• ODE.HasDE.avgScores(double[], int, double[]),
• ODE.HasDE.avgScores(double[], int, double[], int), or

should be implemented in modules that advertise the model types ODE, SDE or PDE.

Alternatively, the method ODE.getDerivatives(double, double[], double[], double[]) may be overridden in a subclass of ODE, which may prevent calls to avgScores(...) altogether.

Specified by:

avgScores in interface ODE.HasDE

Parameters:

density - the frequency/density of each trait/strategy

n - the size of interaction groups

avgscores - the array for storing the average payoffs/scores for each strategic type

mixedScores

public void mixedScores(int[] traitCount,
 double[] traitScore)

Description copied from interface: IBS.HasIBS.DPairs

Calculate the average payoff/score in a finite population with the number of each trait/strategy provided in count for pairwise interactions. The payoffs/scores for each of the nTraits traits/strategies must be stored and returned in the array traitScores.

Important: must be overridden and implemented in subclasses that define game interactions in well-mixed populations where individuals interact with everyone else. Computationally it is not feasible to cover this scenario with IBS.HasIBS.DPairs.pairScores(int, int[], double[]) or IBS.HasIBS.DGroups.groupScores(int[], double[]), respectively.

Note: If explicit calculations of the well-mixed scores are not available, interactions with everyone in well-mixed populations should checked for and excluded with a warning in IBS.check() (see CXPopulation for an example).

Specified by:

mixedScores in interface IBS.HasIBS.DPairs

Parameters:

traitCount - number of individuals for each trait/strategy

traitScore - array for returning the payoffs/scores of each trait/strategy

setPayoffs

public void setPayoffs(double[][] payMatrix)

Set the payoff matrix to payMatrix.

Parameters:

payMatrix - the new payoff matrix

getPayoffs

public double[][] getPayoffs()

Get the payoff matrix.

Returns:

the payoff matrix

collectCLO

public void collectCLO(CLOParser parser)

Description copied from interface: CLOProvider

All providers of command line options must implement this method to collect their options.

Each command line option is (uniquely) identified by it's name (see CLOption.getName()), which corresponds to the long version of the option. If an attempt is made to add an option with a name that already exists, the parser issues a warning and ignores the option. Thus, in general, implementing subclasses should first register their options and call super.collectCLO(CLOParser) at the end such that subclasses are able to override command line options specified in a parental class.

Override this method in subclasses to add further command line options. Subclasses must make sure that they include a call to super.

Specified by:

collectCLO in interface CLOProvider

Overrides:

collectCLO in class Discrete

Parameters:

parser - the reference to parser that manages command line options

See Also:

• CLOParser.addCLO(CLOption)

adjustCLO

public void adjustCLO(CLOParser parser)

Description copied from interface: CLOProvider

Providers of command line options may want to remove certain options that other providers provided by overriding this method. After all command line options are collected, all providers get a chance to adjust the collection. In particular, options should be removed that do not make sense in present context. Overriding methods usually call CLOParser.removeCLO(String[]) or variants thereof.

Specified by:

adjustCLO in interface CLOProvider

Parameters:

parser - the reference to parser that manages command line options

See Also:

• CLOParser.removeCLO(CLOption)

createIBSPop

public RSP.IBSPop createIBSPop()

Description copied from class: Module

Opportunity to supply custom individual based simulations.

Overrides:

createIBSPop in class Module

Returns:

the custom IBSPopulation or null to use default.