Interface Model.HasIBS.DPairs

All Superinterfaces:

Features, Features.Pairs, Model.HasIBS

All Known Subinterfaces:

Model.HasIBS.DGroups

All Known Implementing Classes:

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

Enclosing interface:

Model.HasIBS

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

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

Author:

Christoph Hauert

Nested Class Summary

Nested classes/interfaces inherited from interface Features

Features.Groups, Features.Multispecies, Features.Pairs, Features.Payoffs, Features.Static

Nested classes/interfaces inherited from interface Model.HasIBS

Model.HasIBS.CGroups, Model.HasIBS.CPairs, Model.HasIBS.DGroups, Model.HasIBS.DPairs, Model.HasIBS.MCGroups, Model.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 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 me against opponents with different traits.

Methods inherited from interface Features

getTitle, isMultispecies, isStatic

Methods inherited from interface Features.Pairs

isPairwise

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 me against opponents with different traits. The respective numbers of each of the nTraits opponent traits are provided in the array tCount. The payoffs/scores for each of the nTraits opponent traits 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 Model.HasIBS.DGroups.groupScores(int[], double[]).

Parameters:

me - the trait index of the focal individual

traitCount - number of opponents with each trait

traitScore - array for returning the scores of each opponent trait

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 provided in count for pairwise interactions. The payoffs/scores for each of the nTraits traits 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 Model.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 Model.check() (see CXPopulation for an example).

Parameters:

traitCount - number of individuals for each trait

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