# Continuing Sync

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I am continuing in Sync: How Order Emerges from Chaos in the Universe, Nature, and Daily Lifeby Steven Strogatz. To get a feeling on it, I was building a group of things which have only a minute influence on each other are able to synchronize their behavior. I got some more explanations how things fit together and adapted my calculations. Two things are changed.**Wiekvoet**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

You can actually use one of the items as a kind of clock, only register status when it goes to zero. This makes it possible to plot behavior over much longer times. Then, I found my function of last week a bit primitive, by going step-wise through time. I was thinking at first to reduce the step-size why not do the more exact thing and follow a function? So, I scaled the logit function a bit to provide behavior of an item and build some functions to determine when an item goes over the threshold. Code is at the bottom.

Even with a small additive effect they synchronize, as shown below. They are not completely synchronized, but close enough for me.

#### R code

library(ggplot2)library(arm)

plotpart <- function(xmat,fname='een.png',thin=1) {

nstep <- ncol(xmat)

coluse <- seq(1,nstep,by=thin)

xmat <- xmat[,coluse]

fin <- length(coluse)

nitems <- nrow(xmat)

df <- data.frame(score=as.numeric(xmat),

cycle=rep((coluse),each=nitems),

item =rep(1:nitems,fin))

g<- ggplot(df,aes(x=cycle,y=score,group=item,alpha=score)) +

geom_line() +

scale_alpha_continuous(range=c(.02,.0201) )+

theme(legend.position=’none’) +

xlab(‘Iteration’) + theme(panel.background = element_rect(fill=’white’))

png(fname)

print(g)

dev.off()

}

score2time <- function(x) logit((1+x)/2)

time_delta_score <- function(score1,score2=.99) {

score2time(score2)-score2time(score1)

}

score_delta_time <- function(scorenow,tdelta) {

tnow <- score2time(scorenow)

time2score(tnow+tdelta)

}

onestep <- function(score,limit=0.99,spil=1e-4) {

mscore <- max(score)

dtnew <- time_delta_score(mscore,limit)

scorenew <- score_delta_time(score,dtnew)

maxed1 <- maxed <- scorenew==max(scorenew)#>limit*.99999999

while(sum(maxed)>0) {

scorenew[!maxed] <- scorenew[!maxed] + sum(maxed)*spil

scorenew[maxed] <- 0

maxed <- scorenew>limit

maxed1 <- maxed | maxed1

}

scorenew[maxed1] <- 0

scorenew

}

nitems <- 500

niter <- 10000

xmat <- matrix(0,nrow=nitems,ncol=niter)

score <- time2score(runif(nitems,0,score2time(.99)))

n <- 0

while (n < niter) {

score <- onestep(score,spil=1e-7)

if (score[1]==0) {

n<- n+1

xmat[,n] <- score

}

}

plotpart(xmat[1:250,],’een.png’,thin=20)

To

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