In skranz/StratTourn: Tournament of game theoretic strategies

library(StratTourn)
library(xtable)
library(ggplot2)
library(reshape2)
library(googleVis)
library(dplyr)
library(tidyr)

setwd("D:/libraries/StratTourn")

setwd("D:/libraries/StratTourn/studies")


tourn = load.tournament("Tourn_trade_20141123_082001.tou")
# Data for each match
md = tourn$dt
md = add.other.var(md,c("strat","u"))
md$delta.u = md$u - md$other.u
# Names of all strategies
strats = unique(md$strat)


rank.dt = strat.rank.from.matches(md)

# Data for each round
file = tourn$rs.file
rd = fread(file)
rd = as.tbl(as.data.frame(rd))
rd = add.other.var(rd,c("strat","u"))

# Names of all strategies
strats = unique(rd$strat)

# Perhaps select a subset of strategies
used.strats = strats
ard = rd

if (!identical(used.strats,strats)) {
  rows = rd$strat %in% used.strats & rd$other.strat %in% used.strats
  rd = ard[rows,]
}

rd = add.other.var(rd,c("cost","type"))
rd$cost.pair = paste0(rd$type,"_",rd$other.type)
sd <- summarise(group_by(rd,strat,other.strat, cost.pair), diff=mean(u-other.u), mean=mean(u))
rank.strats = rank.dt$strat
sd$strat = factor(sd$strat, rank.strats, ordered=TRUE)
sd$other.strat = factor(sd$other.strat, rank.strats, ordered=TRUE)

Mean Utility by Cost Combination

ggplot(data=sd, aes(cost.pair, fill=strat, y=mean)) + geom_bar(stat="identity",position="identity") +facet_grid(other.strat~strat) + geom_hline(yintercept=0, size=0.5, col="black",alpha=0.5)
cat("\n")

The plot shows the total mean utility for the cost distributions High/High, High/Low, Low/High, Low/Low respectively for each strategy pair.

Utility Difference by Cost Combination

ggplot(data=sd, aes(cost.pair, fill=strat, y=diff)) + geom_bar(stat="identity",position="identity") +facet_grid(other.strat~strat) + geom_hline(yintercept=0, size=0.5, col="black",alpha=0.5)
cat("\n")