games_ga: Games genetic algorithm:

In gamesGA: Genetic Algorithm for Sequential Symmetric Games

Description

This function runs a genetic algorithm that identifies sequential strategies for maximising payoffs given any two by two symmetrical payoff matrix. Simulated players remember three rounds into the past.

Usage

games_ga(CC = 3, CD = 0, DC = 5, DD = 1, callC = TRUE,
  generations = 250, rounds = 100, num_opponents = 100,
  cross_prob = 0.05, mutation_prob = 0.05)

Arguments

CC	The number of points awarded to a focal agent when the focal agent and its opponent both cooperate
CD	The number of points awarded to a focal agent when the focal agent cooperates and its opponent defects
DC	The number of points awarded to a focal agent when the focal agent defects and its opponent cooperates
DD	The number of points awarded to a focal agent when the focal agent and its opponent both defect
callC	Whether or not the function calls c in the genetic algorithm. While not calling c is an option, the default value is TRUE because using the compiled c code greatly speeds up the genetic algorithm, making the whole program run much faster
generations	The number of generations the genetic algorithm will run before returning selected genotypes and fitness history. Each generation includes some number of rounds of the game that each strategy will play
rounds	The number of rounds of the game that a focal player will play against its opponent before moving on to the next opponent. Rounds are played iteratively against opponents, allowing the history of interactions to affect strategies and therefore total payoffs after all rounds are completed
num_opponents	The number of randomly selected opponents that a focal player will play during the course of one generation; the focal player will play in the same number of rounds with each opponent
cross_prob	A uniform probability of random crossing over event for a focal player's locus with the same locus from another randomly selected player.
mutation_prob	The probability that a given locus will mutate; mutation from 'C' to 'D' occurs with the same probability as 'D' to 'C' (no bias)

Value

A list, the elements of which include: 1. A table of the genomes of strategies and their frequencies in the population and 2. The mean fitness calculated over all players population in each generation. Fitness of one player is the number of points accrued over all rounds and opponents in a generation.

Examples

games_ga(CC = 3, CD = 0, DC = 5, DD = 1, generations = 100, rounds = 100)

gamesGA documentation built on March 26, 2020, 6:57 p.m.