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vseCheck.R
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library(data.table)
library(scatterD3)
#
# vse = fread("full1.csv")
# vse = rbind(vse,fread("full2.csv"))
# vse[,mean(util-rand)/mean(best-rand),by=list(method,chooser)]
#
# vse[chooser=="honBallot",mean(util-rand)/mean(best-rand),by=list(method,chooser)]
#
# ovse = fread("lowDks2.csv")
# ovse[,mean(util-rand)/mean(best-rand),by=list(method,chooser)]
#
# ovse[chooser %in% c("honBallot","Oss.hon_strat.","Prob.strat50_hon50."),mean(util-rand)/mean(best-rand),by=list(method,chooser)]
#
# mvse = fread("media413.csv")
# mvse = rbind(mvse,fread("media41.csv"), fill=T)
# mvse[,mean(util-rand)/mean(best-rand),by=list(method,chooser)]
#
# mvse[chooser %in% c("honBallot","Oss.hon_strat.","Prob.strat50_hon50."),mean(util-rand)/mean(best-rand),by=list(method,chooser)]
#
#
#
#
#
#
#
#
#
#
#
# fvse = fread("fuzzy5.csv")
# fvse[,mean(util-rand)/mean(best-rand),by=list(method,chooser)]
#
# fvse = fread("wtf1.csv")
#
# fvse = rbind(fvse,fread("wtf2.csv"))
fvse = fread("candnumbertest1.csv")
numVoters = mean(fvse[,numVoters])
vses = fvse[method != "ApprovalPoll",list(VSE=mean((r1WinnerUtil - meanCandidateUtil) /
(magicBestUtil - meanCandidateUtil))),by=.(method,backgroundStrat)]
dcast(vses, method ~ backgroundStrat)
vses
fromhons = fvse[backgroundStrat=="honBallot",
list(
vse=mean((r1WinnerUtil - meanCandidateUtil) / (magicBestUtil - meanCandidateUtil)),
fgMatters=mean(fgUtilDiff != 0),
VSEDiff=mean((totalUtil - r1WinnerUtil) / (magicBestUtil - meanCandidateUtil)),
#margStrategicRegret=mean(margStrategicRegret),
pivotalUtilDiff=mean(pivotalUtilDiff),
deciderUtilDiffSum=mean(deciderUtilDiffSum),
avgStrategicRegret=mean((avgStrategicRegret) / (magicBestUtil - meanCandidateUtil) / numVoters),
fgHelpedUtilDiff=mean((fgHelpedUtilDiff) / (magicBestUtil - meanCandidateUtil) / numVoters),
fgHarmedUtilDiff=mean((fgHarmedUtilDiff) / (magicBestUtil - meanCandidateUtil) / numVoters)
),
by=.(method,backgroundStrat, fgStrat, fgArgs, fgTargets)]
fromhons
fromawares = fvse[((backgroundStrat=="vaBallot" & method != "Minimax") | (backgroundStrat=="honBallot" &method == "Minimax")), #& !fgStrat %in% c("", "vaBallot"),
list(
vse=mean((r1WinnerUtil - meanCandidateUtil) / (magicBestUtil - meanCandidateUtil)),
fgMatters=mean(fgUtilDiff != 0),
VSEDiff=mean((totalUtil - r1WinnerUtil) / (magicBestUtil - meanCandidateUtil)),
#margStrategicRegret=mean(margStrategicRegret),
firstDeciderUtilDiff=mean(firstDeciderUtilDiff),
deciderUtilDiffSum=mean(deciderUtilDiffSum),
avgStrategicRegret=mean(avgStrategicRegret / (magicBestUtil - meanCandidateUtil) / numVoters),
fgHelpedUtilDiff=mean(fgHelpedUtilDiff / (magicBestUtil - meanCandidateUtil) / numVoters),
fgHarmedUtilDiff=mean(fgHarmedUtilDiff / (magicBestUtil - meanCandidateUtil) / numVoters)),
by=.(method,backgroundStrat, fgStrat, fgArgs, fgTargets)]
fromawares
fromall = fvse[method !="ApprovalPoll",
list(
vse=mean((r1WinnerUtil - meanCandidateUtil) / (magicBestUtil - meanCandidateUtil)),
fgMatters=mean(fgUtilDiff != 0),
VSEDiff=mean((totalUtil - r1WinnerUtil) / (magicBestUtil - meanCandidateUtil)),
#margStrategicRegret=mean(margStrategicRegret),
firstDeciderUtilDiff=mean(firstDeciderUtilDiff),
deciderUtilDiffSum=mean(deciderUtilDiffSum),
avgStrategicRegret=mean((avgStrategicRegret) / (magicBestUtil - meanCandidateUtil) / numVoters),
fgHelpedUtilDiff=mean((fgHelpedUtilDiff) / (magicBestUtil - meanCandidateUtil) / numVoters),
fgHarmedUtilDiff=mean((fgHarmedUtilDiff) / (magicBestUtil - meanCandidateUtil) / numVoters)
),
by=.(method,backgroundStrat, bgArgs, fgStrat, fgArgs, fgTargets)]
fromall
besttargets = function(grouped) {
grouped[,target:=fgTargets[order(-avgStrategicRegret)[1]], by=.(method,backgroundStrat, fgStrat, fgArgs)]
# justBest = grouped[target,,]
chosen = grouped[grouped[, .I[avgStrategicRegret == max(avgStrategicRegret)], by = .(method,backgroundStrat, fgStrat, fgArgs)]$V1]
}
bestfromawares = besttargets(fromawares)
bestfromawares
library(ggplot2)
library(ggthemes)
library(dplyr)
library(forcats)
library(scales)
#vse as bars
(ggplot(data = vses, aes(x = VSE, y = method, group = method))
+ geom_line(size=3) + xlim(.65,1.00) + theme_gdocs()
+ theme(axis.title.y=element_blank()) + xlab("% Voter Satisfaction Efficiency (VSE)"))
#vse as dots
(
vses[!(method=="Minimax" & backgroundStrat=="vaBallot"),] %>%
mutate(method = method
%>% fct_reorder(VSE, .fun='mean')
%>% recode(`STAR`="STAR",
`PluralityTop2`="Plurality Top Two",
`Minimax`="Smith/Minimax",
`Irv`="IRV (RCV)",
`ApprovalTop2`="Approval Top Two",
),
#to=c("STAR", "Plurality/Runoff", "Plurality", "Smith/Minimax", irv="IRV (RCV)", "Approval/Runoff", "Approval")))
#VSE = VSE * 100
) %>%
ggplot(
aes(x = VSE, y = as.factor(method), color = backgroundStrat))
+ scale_x_continuous(labels=scales::percent_format(accuracy = 1))#, limits=c(.68, 1.0))
+ geom_point(size=3) #+ xlim(.65,1.00)
+ theme_gdocs()
+ theme(axis.title.y=element_blank()) + xlab("% Voter Satisfaction Efficiency (VSE)")
+ labs(color="Voter Behavior") + scale_colour_colorblind(labels = c("Honest / Naive", "Viability-aware"))
# + scale_y_discrete(breaks=c("STAR", "Plurality", "PluralityTop2", "Minimax", "Irv", "ApprovalTop2", "Approval"),
# labels=c("STAR", "Plurality/Runoff", "Plurality", "Smith/Minimax", irv="IRV (RCV)", "Approval/Runoff", "Approval"))
)
#ASR for viability-aware
(
fromhons %>%
mutate(method = method
%>% fct_reorder(avgStrategicRegret, .fun='mean')
%>% recode(`STAR`="STAR",
`PluralityTop2`="Plurality Top Two",
`Minimax`="Smith/Minimax",
`Irv`="IRV (RCV)",
`ApprovalTop2`="Approval Top Two",
),
#to=c("STAR", "Plurality/Runoff", "Plurality", "Smith/Minimax", irv="IRV (RCV)", "Approval/Runoff", "Approval")))
#VSE = VSE * 100
) %>%
ggplot(
aes(x = avgStrategicRegret, y = as.factor(method)))
+ scale_x_continuous(labels=scales::percent_format(accuracy = 1), limits = c(-.0,.38))
+ geom_point(size=3) #+ xlim(.65,1.00)
+ theme_gdocs()
+ theme(axis.title.y=element_blank()) + xlab("% Average Strategic Regret (ASR) for not casting a viability-aware ballot")
#+ labs(color="Voter Behavior")
#+ scale_colour_colorblind(labels = c("Honest / Naive", "Viability-aware"))
# + scale_y_discrete(breaks=c("STAR", "Plurality", "PluralityTop2", "Minimax", "Irv", "ApprovalTop2", "Approval"),
# labels=c("STAR", "Plurality/Runoff", "Plurality", "Smith/Minimax", irv="IRV (RCV)", "Approval/Runoff", "Approval"))
)
#ASR for targeted strategy
(
bestfromawares %>%
mutate(method = method
%>% fct_reorder(avgStrategicRegret, .fun='mean')
%>% recode(`STAR`="STAR",
`PluralityTop2`="Plurality Top Two",
`Minimax`="Smith/Minimax",
`Irv`="IRV (RCV)",
`ApprovalTop2`="Approval Top Two",
),
#to=c("STAR", "Plurality/Runoff", "Plurality", "Smith/Minimax", irv="IRV (RCV)", "Approval/Runoff", "Approval")))
#VSE = VSE * 100
) %>%
ggplot(
aes(x = avgStrategicRegret, y = as.factor(method), shape = as.factor(fgStrat), color = fgArgs))
+ scale_x_continuous(labels=scales::percent_format(accuracy = 1))
+ geom_point(size=3) #+ xlim(.65,1.00)
+ theme_gdocs()
+ theme(axis.title.y=element_blank()) + xlab("% Average Strategic Regret (ASR) for not using targeted strategy")
+ labs(color="Strategy level", shape="Strategy type")
+ scale_colour_colorblind(labels = c("Honest", "Semi-honest", "Dishonest", "Bullet"))
+ scale_shape(labels = c("Compromise", "Die-hard"))
# + scale_y_discrete(breaks=c("STAR", "Plurality", "PluralityTop2", "Minimax", "Irv", "ApprovalTop2", "Approval"),
# labels=c("STAR", "Plurality/Runoff", "Plurality", "Smith/Minimax", irv="IRV (RCV)", "Approval/Runoff", "Approval"))
)