games_ga {gamesGA}

Games genetic algorithm:

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)