Package org.evoludo.simulator.models

Interface IBS.HasIBS.DPairs

All Superinterfaces:

Features, Features.Pairs, IBS.HasIBS

All Known Subinterfaces:

IBS.HasIBS.DGroups

All Known Implementing Classes:

ATBT, CDL, CDLP, CDLPQ, Centipede, DemesTBT, EcoPGG, RSP, simCDL, simCDLP, simCDLPQ, simTBT, simTraits, TBT, Traits

Enclosing interface:

IBS.HasIBS

public static interface IBS.HasIBS.DPairs
extends IBS.HasIBS, Features.Pairs

Modules that offer individual based simulation models with discrete traits and pairwise interactions must implement this interface.

Nested Class Summary

Nested classes/interfaces inherited from interface Features

Features.Groups, Features.Pairs, Features.Static

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

Method Summary

Modifier and Type	Method	Description
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.

Methods inherited from interface Features

isStatic

Methods inherited from interface Features.Pairs

isPairwise

Methods inherited from interface IBS.HasIBS

createIBS

Method Details

pairScores

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. 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[]).

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

mixedScores

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. 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 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).

Parameters:

traitCount - number of individuals for each trait/strategy

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