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model8.R
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# Model 8 ####
# Advice is disconnected from initial decision
parallel <- T
# Agents have direct access to one another's confidence.
ARC <- Sys.info()[[1]] != 'Windows'
if(parallel)
ARC <- T
# Storage path for results
resultsPath <- ifelse(ARC,'results/','results/')
time <- format(Sys.time(), "%F_%H-%M-%S")
resultsPath <- paste0(resultsPath,time)
# Libraries
if(!require('parallel')) {
install.packages(repos="http://cran.r-project.org",'parallel')
library(parallel)
}
if(ARC) {
# sink(paste(resultsPath, 'log.txt'))
print(Sys.info())
# Set up the parallel execution capabilities
nCores <- detectCores()
cl <- makeCluster(nCores)
print(paste('Running in parallel on', nCores, 'cores.'))
reps <- nCores
} else {
reps <- 1
}
# Clear the result storage variables
suppressWarnings(rm('rawdata'))
suppressWarnings(rm('results'))
# Define the function
runModel <- function(spec, .wd) {
setwd(.wd)
source('evoSim/evoSim/R/evoSim.R')
data <- evoSim(agentCount = spec$agents,
agentDegree = spec$degree,
decisionCount = spec$decisions,
generationCount = 2500,
mutationChance = 0.01,
other = list(sensitivity = spec$sensitivity,
sensitivitySD = spec$sensitivitySD,
startingEgoBias = spec$startingEgoBias,
adviceNoise = spec$adviceNoise),
makeAgentFun = function(modelParams, parents = NULL) {
# Inherit egoBias if there's a previous generation and we're not mutating
if(!is.null(parents)) {
# print(paste0('id: ', parents$id,
# '; egoBias: ', round(parents$egoBias,3),
# '; fitness: ', round(parents$fitness,3)))
if(runif(1) < modelParams$mutationChance) {
# mutate
egoBias <- rnorm(1, parents$egoBias, 0.1)
} else {
egoBias <- parents$egoBias
}
}
else {
# print(paste('novelty',parents$generation))
egoBias <- modelParams$other$startingEgoBias#rnorm(1, .5, 1)
}
sensitivity <- abs(rnorm(1, mean = modelParams$other$sensitivity,
sd = modelParams$other$sensitivitySD))
# Keep egoBias to within [0-1]
egoBias <- clamp(egoBias, maxVal = 1, minVal = 0)
return(data.frame(sensitivity, egoBias))
},
selectParentsFun = function(modelParams, agents, world, ties) {
tmp <- agents[which(agents$generation == world$generation),]
tmp <- tmp[order(tmp$fitness, decreasing = T),]
# drop the worst half of the population
# tmp <- tmp[1:2,]#(floor(nrow(tmp)/2)), ]
# the others get weighted by relative fitness which are transformed to +ve values
tmp$fitness <- tmp$fitness - min(tmp$fitness) + 1
# scale appropriately
while(any(tmp$fitness < 10))
tmp$fitness <- tmp$fitness * 10
# and round off
tmp$fitness <- round(tmp$fitness)
tickets <- vector(length = sum(tmp$fitness)) # each success buys a ticket in the draw
i <- 0
for(a in 1:nrow(tmp)) {
tickets[(i+1):(i+1+tmp$fitness[a])] <- a
i <- i + 1 + tmp$fitness[a]
}
winners <- sample(tickets, modelParams$agentCount, replace = T)
# The winners clone their egocentric discounting
winners <- tmp[winners,'id']
#print(cor(agents$egoBias, abs(agents$fitness-.5)))
# print(summary(agents[agents$id %in% winners, c('egoBias', 'fitness')]))
# print(tmp[tmp$id %in% winners,])
print(paste('Selected parents for generation',world$generation))
return(winners)
},
getAdviceFun = function(modelParams, agents, world, ties) {
mask <- which(agents$generation == world$generation)
agents$advisor[mask] <- apply(ties, 1, function(x) sample(which(x != 0),1))
# Fetch advice as a vector
n <- length(mask)
agents$advice[mask] <- rnorm(n,
rep(world$state, n),
clamp(agents$sensitivity[agents$advisor[mask]]
+ modelParams$other$adviceNoise,
Inf))
return(agents)
},
getWorldStateFun = function(modelParams, world) {
return(50)
})
# save results
n <- length(unique(data$agents$generation))
results <- data.frame(generation = unique(data$agents$generation),
modelDuration = rep(data$duration, n),
sensitivity = rep(spec$sensitivity, n),
sensitivitySD = rep(spec$sensitivitySD, n))
# bind in the stats of interest aggregated by the generation
results <- cbind(results,
aggregate(data$agents,
list(data$agents$generation),
mean)[ ,c('fitness',
'degree',
'sensitivity',
'egoBias',
'initialDecision',
'finalDecision')])
rawdata <- data
rawdata$rep <- 1
return(list(rawdata = rawdata, results = results))
}
# Parameter space to explore
specs <- list()
for(x in c(1000))
for(y in c(10))
for(z in c(30))
for(s in c(10,100))
for(sSD in c(10))
for(sEB in c(0.01, 0.99))
for(aN in c(1,100))
specs[[length(specs)+1]] <- list(agents=x,degree=y,decisions=z,
sensitivity=s,sensitivitySD=sSD,
startingEgoBias=sEB,
adviceNoise = aN)
# Run the models
# make sure the children can see the degree variable
# Run parallel repetitions of the model with these settings
.wd <- getwd()
startTime <- Sys.time()
if(!ARC) {
degreeResults <- lapply(specs, runModel, .wd)
} else {
print('Executing parallel operations...')
degreeResults <- parLapply(cl, specs, runModel, .wd)
}
print('...combining results...')
# Join up results
for(res in degreeResults) {
if(!exists('results'))
results <- res$results
else
results <- rbind(results, res$results)
if(!exists('rawdata'))
rawdata <- list(res$rawdata)
else
rawdata[[length(rawdata)+1]] <- res$rawdata
}
print(paste0('...complete.'))
print(Sys.time() - startTime)
# Cleanup
if(ARC)
stopCluster(cl)
print('Saving data...')
# Save data
write.csv(results, paste(resultsPath, 'results.csv'))
save(rawdata, file = paste(resultsPath, 'rawdata.Rdata'))
print('...complete.')