Skip to content

Finds optimal strategies for sequential symmetric games using a genetic algorithm

License

Notifications You must be signed in to change notification settings

ConFooBio/gamesGA

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

59 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

gamesGA

A genetic algorithm for finding adaptive strategies in sequential games characterised by two by two symmetric payoff matrices. For example, in the classic case of the Prisoner's dilemma, the payoff matrix can be defined below for the scenario in which both players cooperate (CC, upper left), the focal player cooperates but the opponent defects (CD, upper right), the focal player defects but the opponent cooperates (DC, lower left), and both players defect (DD, lower right).

Opponent cooperates Opponent defects
Focal player cooperates 3, 3 0, 5
Focal player defects 5, 0 1, 1

Note that any symmetrical payoff combination is possible in games_ga(), as specified by CC, DC, CD, and DD (see below).

Installation and use

Install from CRAN

To install this package from CRAN.

install.packages("gamesGA")

Install with GitHub

To install this package from GitHub, make sure that the devtools library is installed.

install.packages("devtools")
library(devtools)

Use install_github to install using devtools.

install_github("bradduthie/gamesGA")

Example usage:

To run the genetic algorithm and find strategies that evolve in a classical Prisoner's dilemma scenario, run the following:

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

Run from your browser

The program gamesGA can also be run through a browser via shiny. To do this, run the games_ga_gui() function.

games_ga_gui()

Alternatively, the following link redirects to a page that runs the application from the web.

https://bradduthie.shinyapps.io/gameGA_browser/

For further assistance, contact brad.duthie@gmail.com.

About

Finds optimal strategies for sequential symmetric games using a genetic algorithm

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • R 71.4%
  • C 28.6%