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gameGA_browser.Rmd
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gameGA_browser.Rmd
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---
title: "gameGA: Browser interface"
output:
html_document: default
header-includes:
linestretch: 1
runtime: shiny
---
```{r, echo=FALSE}
library(gamesGA)
library(shiny)
library(rhandsontable)
```
********************************************************************************
> **The [gameGA](https://github.com/bradduthie/gamesGA) R package simulates a genetic algorithm for finding adaptive strategies in sequentially played rounds of any two by two sequential payoff matrix game iterated between players. Below, the embedded [shiny](https://shiny.rstudio.com/) application recognises user inputs in the `games_ga()` function and returns a table showing surviving strategies and their corresponding frequencies.**
> [See here](#help) for instructions on [inputs](#help) and interpretion of [table](#out) and [figure](#out2) output. To install the `gamesGA` package, [see here](https://github.com/bradduthie/gamesGA/blob/master/README.md).
********************************************************************************
<br>
**Input variables into the model below**
```{r, echo=FALSE}
inputPanel(
numericInput("CC", label = "CC", 3, width="50%"),
numericInput("DC", label = "DC", 5, width="50%"),
numericInput("CD", label = "CD", 0, width="50%"),
numericInput("DD", label = "DD", 1, width="50%"),
numericInput("RnD", label = "Rounds", 100, width="50%"),
numericInput("Gen", label = "Generations", 250, width="50%"),
numericInput("Cro", label = "Pr(Crossover)", 0.05, width="50%"),
numericInput("mu", label = "Pr(Mutation)", 0.05, width="50%"),
style='width: 900px; height: 150px'
)
```
```{r, echo=FALSE}
gres <- reactive({
cc <- input$CC;
dc <- input$DC;
cd <- input$CD;
dd <- input$DD;
rd <- input$RnD;
gn <- input$Gen;
co <- input$Cro;
mu <- input$mu;
game_res <- games_ga(CC = cc, DC = dc, CD = cd, DD = dd, rounds = rd,
generations = gn, cross_prob = co,
mutation_prob = mu, num_opponents = 100);
all <- c(game_res$genos, "break_1",
rownames(game_res$genos), "break_2",
game_res$fitness);
})
```
<br><br>
**Table of evolved strategies**
```{r, echo=FALSE}
renderTable({
break1 <- which(gres()=="break_1")[1] - 1;
break2 <- which(gres()=="break_2")[1] - 1;
geno_vec <- gres()[1:break1];
geno_row <- gres()[(break1+2):break2];
genos <- matrix(data = geno_vec, ncol = 10, byrow = FALSE);
rownames(genos) <- geno_row;
colnames(genos) <- c("CCC", "CCD", "CDC", "CDD", "DCC", "DCD", "DDC",
"DDD", "1st", "Final %");
final_table <- genos;
}, include.rownames=TRUE)
```
<br><br>
**Mean per round fitness over generations**
```{r, echo = FALSE}
renderPlot({
break2 <- which(gres()=="break_2")[1]+1;
end <- length(gres());
fitnesses <- gres()[break2:end];
fitnesses <- as.numeric(fitnesses);
mean_fit <- fitnesses / (input$RnD * 100);
maxpt <- max(c(input$CC, input$CD, input$DC, input$DD));
par(mar=c(5,5,1,1));
plot(x = 1:length(mean_fit), y=mean_fit, type="l", ylim=c(0,maxpt), lwd = 3,
xlab="Generation", ylab = "Mean strategy fitness per round",
cex.axis=1.5, cex.lab=1.5);
})
```
<br>
********************************************************************************
<br>
<a name="help">Input variables</a>
--------------------------------------------------------------------------------
A list of all of the inputs that can be used in the embedded application above is presented below with explanation:
- `CC` The payoff that the focal player receives if the focal player and the opponent both cooperate
- `DC` The payoff that the focal player receives if the focal player defects but the opponent cooperates
- `CD` The payoff that the focal player receives if the focal player cooperates but the opponent defects
- `DD` The payoff that the focal player receives if the focal player and the opponent both defect
- `Rounds` The number of rounds a focal player plays against each opponent
- `Generations` The number of generations the genetic algorithm is allowed to run before producing the output of strategies. By default, **each strategy will compete against all 100 strategies in the population (including itself)**
- `Pr(Cossover)` The probability that a crossover event occurs at any given locus between a focal player and a randomly selected player in the population
- `Pr(Mutation)` For each locus of every player, the probability that a mutation occurs in a generation
The inputs `CC`, `DC`, `CD`, and `DD` can be visualised as the payoffs that a focal individual receives on a standard $2 \times 2$ payoff matrix, shown below.
| | Opponent cooperates | Opponent defects |
|-------------------------|---------------------|------------------|
| Focal player cooperates | **CC** | **CD** |
| Focal player defects | **DC** | **DD** |
Hence, `gamesGA` does not (yet) model non-symmetric payoffs.
<br>
<a name="out">Table output</a>
--------------------------------------------------------------------------------
In the above output table, each row corresponds to a unique strategy that is present in the population after `Generations` of simulated evolution. The first eight columns in the table correspond to the response of a focal strategy given the history of an opponent. For example, in the first column, **C C C** corresponds to a history of three previous rounds of an opponent playing **C** (cooperate). The row element in this column therefore indicates the focal strategy's response (**C** cooperation, or **D** defect) if its opponent has coopereated three times in a row. The nineth column indicates the evolved strategy that a focal player adopts for their first round move (for second and third round moves, see how the [code](https://github.com/bradduthie/gamesGA/blob/master/src/fitness.c) handles the reduced information). Finally, the **Final %** column indicates the perecentage of individuals in the population adopting the row strategy.
<a name="out2">Figure output</a>
--------------------------------------------------------------------------------
The figure above shows the mean number of points a the average strategy accrues *per round* in each generation. For example, if `CC = 3` in a simulation, and all strategies cooperate, then the mean per round fitness will always be 3.