Wind extinguishes a candle and energizes fire. Likewise with randomness, uncertainty, chaos: you want to use them, not hide from them. You want to be the fire and wish for the wind.

TL;DR

The fast probabilistic consensus (FPC), introduced recently by Serguei Popov (paper) terminates in most of the times in a minimal number of steps and is much faster than the theoretical upper bounds given by Popov. It is claimed that an additional layer of randomness allows the protocol to still work in critical situations where other protocols fail. These simulations show that the FPC is indeed very robust under various attack scenarios ->.

Introduction and set-up

We perform a first simulation-based study on the fast probabilistic consensus proposed by Serguei Popov (paper). Since the protocol is very safe and fast in non-critical situations, this study is mainly about attack strategies that try to reach disagreement of the honest nodes and considers mostly critical situations. The post is not intended to be a thorough scientific study of the FPC; it justs collects various observations in order to illustrate its functionalities.

Question

How can a distributed network find consensus on the value of a bit?

Donsker’s invariance principle is a classical extension of the central limit theorem. It says that the path of a discrete random walk converges, if properly rescaled, in distribution to a standard Brownian motion. More details can be found in your favorite text book or on wikipedia. Here just comes an animation:

library(dplyr)
library(gganimate)
library(transformr)
set.seed(42)
n <- 1000 # maximal steps of random walk
n_seq <- seq(from=10, 100, by = 10) # starting phase
n_seq <- c(n_seq, seq(from=100, n, by = 100))
number_iteration <-length(n_seq)
X <- sample(c(-1,1), n, replace=TRUE, prob=c(0.5, 0.5))
S <- cumsum(X) # random walk S_n
W <- numeric(number_iteration*n)
id <- numeric(number_iteration*n)
time <- numeric(number_iteration*n)
for (i in 1:number_iteration){
for (j in (1:n)){
index <- floor((j-1)/n * n_seq[i])
if (index==0) {
W[j+(i-1)*n] <-((((j-1)/n * n_seq[i]) -(index)) *( S[index+1]))/ sqrt(n_seq[i])
}
else {
W[j+(i-1)*n] <- (S[index] + (((j-1)/n * n_seq[i]) -(index)) *( S[index+1]-S[index]))/ sqrt(n_seq[i])
}
}
id[(1+n*(i-1)):(n*i)] <- rep(n_seq[i],n)
time[(1+n*(i-1)):(n*i)] <- (1:n)/n
}
rw <- data.frame(x = time,
y = W,
id =id)
p <- ggplot(rw, aes(x=x, y=y)) +
geom_line()+
ylab(expression(W^{(n)}~ (x)))+
transition_states(
id,
transition_length = 2,
state_length = 1) +
enter_fade() +
exit_shrink() +
ease_aes('sine-in-out')+
labs(title = "Donsker's invariance principle. n = {closest_state}")
animate(p, nframes=number_iteration*2)

Brownian motion is a central object of probability theory. One perspective to look at its construction uses Hilbert spaces. Let be a Hilbert basis equipped with standard scalar product . Hence, every can be written in a unique way as

where and . Let be an i.i.d. sequeence of standard gaussian random variables and consider

Using the independence of we obtain

Brownian motion is then given by (a continuous modification of) , . Hence, “all” we need to do is to find a Hilbert basis. The most known is the following.

haar_mother <- function(x){
(x >0 & x <= 0.5) - (x >0.5 & x <= 1)
}
haar <- function(x,j,k){
(2^(j/2) * haar_mother(2^j*x-k))
}

set.seed(42)
j_max <- 10
n_max <- 11 # maximal resolution
delta <- 2^{-n_max}
x <- (0: 2^n_max)/2^n_max
xi_0 <- rnorm(1) # xi corresponding to constant function
xi <- list() # list of random variables xi
for (j in 0:j_max){
xi[[j+1]] <- rep(0, 2^j)
for (k in 0:(2^j-1))
{
xi[[j+1]][k+1] <-rnorm(1)
}
}
# data.frame containing approximation of the Brownian motion
df <- data.frame(x=numeric(),
y=numeric(),
id=numeric())
for (i in x){
y <- c(1,rep(1, i*2^n_max), rep(0, 2^n_max-i*2^n_max))
alpha <- list() # wavelet coefficients for y
bm <- sum(y)/(length(y)+1) * xi_0
for (j in 0:j_max){
alpha[[j+1]] <- rep(0, 2^j)
for (k in 0:(2^j-1))
{
alpha[[j+1]][k+1] <-sum(haar(x,j,k)*y)*delta
bm <- bm + alpha[[j+1]][k+1]*xi[[j+1]][k+1]
}
df_new <- data.frame(x=i,
y=bm,
id=j)
df <- bind_rows(df, df_new)
}
}
#### Animation using gganimate
ggplot(df, aes(x=x, y=y)) +
geom_line()+
transition_states(
id,
transition_length = 2,
state_length = 1
) +
labs(title = "Construction of Brownian motion using Haar basis. Step: {closest_state}") +
ease_aes('sine-in-out')

The result looks the same as with Levy’s construction which is not astonishing. The implementation above is not optimal and is much slower than the previous one.

It might be worth to consider a different Hilbert basis.

Trigonometric Hilbert basis

The trigonometric basis is defined as

together with .

set.seed(42)
N_max <- 200 # number of trigonometric basis functions used
n_max <- 11 # maximal resolution
delta <- 2^{-n_max}
x <- (0: 2^n_max)/2^n_max
xi <- rnorm(N_max+1) # list of random variables xi
# data.frame containing approximation of the Brownian motion
df <- data.frame(x=numeric(),
y=numeric(),
id=numeric())
for (i in x){
y <- c(1,rep(1, i*2^n_max), rep(0, 2^n_max-i*2^n_max))
alpha <- numeric() # wavelet coefficients for y
alpha[1] <- mean(y)
bm <- alpha[1] * xi[1]
for (n in 1:N_max){
alpha[n] <-sum(sqrt(2)*cos(x*pi*n)*y)*delta
bm <- bm + alpha[n]*xi[n]
df_new <- data.frame(x=i,
y=bm,
id=n)
df <- bind_rows(df, df_new)}
}
#### Animation using gganimate
p <- ggplot(df, aes(x=x, y=y)) +
geom_line()+
transition_states(
id,
transition_length = 2,
state_length = 1
) +
labs(title = "Construction of Brownian motion using trigonometric basis. Step: {closest_state}") +
ease_aes('sine-in-out')
animate(p, nframes=1000)

The above convergence is very slow. The irregularity properties of the Brownian motion are not yet very visible.

Hilbert spaces play a prominent role in various fields of mathematics. An orthonormal basis of such a space is called a Hilbert basis. The purpose of this blog is to illustrate a very clasic and basic Hilbert basis – the Haar basis.

Let be a Hilbert basis of equipped with the standard scalar product . Hence, every can be written in a unique way as

where and .

A classic Hilbert basis consists of Haar functions that are supported on . They are defined using the Haar wavelet:

The Haar basis consists then of rescaled (by ) versions of shifted by ,

together with the constant function . (Note: the constant function has to be added since we consider the interval and not ). In R this looks like:

haar_mother <- function(x){
(x >0 & x <= 0.5) - (x >0.5 & x <= 1)
}
haar <- function(x,j,k){
2^(j/2) * haar_mother(2^j*x-k)
}

Animation of the Haar basis

j_max <- 3 # maximal depth
n_max <- 10 # resolution of grid
df <- data.frame(x=numeric(),
y=numeric(),
id=numeric())
x <- (1: 2^n_max)/2^n_max
id<-1
for (j in 0:j_max){
for (k in 0:(2^j-1))
{
df_new<- data.frame(x=x,
y=haar(x,j,k),
id=id)
df <- bind_rows(df, df_new)
id <- id+1
}
}
ggplot(df, aes(x=x, y=y)) +
geom_step()+
transition_states(
id,
transition_length = 2,
state_length = 5
) +
labs(title = 'Illustration of Haar basis') +
ease_aes('sine-in-out')

Approximation via Haar basis

Now, every function can be written as

The coefficients are called the Haar Wavelet coefficients. Let us calculate them in the discrete setting. We give us a mesh and values and some function :

j_max <- 12
n_max <- 13 # maximal resolution
delta <- 2^{-n_max}
x <- (1: 2^n_max)/2^n_max
y <- x* sin(1/x)
alpha <- list() # list of Haar coefficients
for (j in 0:j_max){
alpha[[j+1]] <- rep(0, 2^j)
for (k in 0:(2^j-1))
{
alpha[[j+1]][k+1] <-sum(haar(x,j,k)*y)*delta # approximation of scalar product
}
}
y_approx <- rep(0, length(y)) # approximated values of y
#### Calculating the approximations for different values of j
df <- data.frame(x=numeric(),
y=numeric(),
id=numeric())
y_approx <- mean(y) # this is the contribution of the constant function
for (j in 0:j_max){
for (k in 0:(2^j-1))
{
y_approx <- y_approx + alpha[[j+1]][k+1]*haar(x,j,k)
}
df_new <- data.frame(x=x,
y=y_approx,
id=j)
df <- bind_rows(df, df_new)
}

Brownian motion is a central object of probability theory. The idea of Lévy’s construction is to construct the Brownian motion step by step on finite sets

of dyadic points. As is dense in the Brownian motion is then obtained as the uniform limit of linear interpolation on . It is pretty easy to illustrate this construction using R and the package gganimate. We use the same notation as in the proof of Wiener’s theorem given on page 23 in “Brownian motion” by Peter Mörters and Yuval Peres.

library(dplyr)
library(gganimate)
library(transformr)
set.seed(42)
n_max <- 14 # maximal number of steps
D <- (0: 2^n_max)/2^n_max # this is in fact D_n_max
B <- list()
Z <- rnorm(1)
B[[1]] <- c(0, Z/2 + rnorm(1), Z)
for (n in 2:n_max){
B[[n]] <- rep(0, 2^n+1)
index_known <- seq(1,2^n+1, by=2) # indices where values are known from previous steps
B[[n]][index_known] <- B[[n-1]]
index_unknown <- seq(2, 2^n, by=2) # indices where values are not yet defined
for (d in index_unknown){
B[[n]][d] <- 0.5*(B[[n]][d-1]+B[[n]][d+1])+ rnorm(1)*2^(-(n+1)/2)
}
}
## interpolation and transformation in a data.frame
df <- data.frame(time=numeric(),
value=numeric(),
id=numeric())
for (n in 1:n_max){
D_n<-(0: 2^n)/2^n
B_interpol<- approx(D_n, B[[n]], xout = D)$y # interpolation
df_new <- data.frame(time=D,
value=B_interpol,
id=n)
df <- bind_rows(df, df_new)
}
## animation
ggplot(df, aes(x=time, y=value)) +
geom_line()+
transition_states(
id,
transition_length = 2,
state_length = 1
) +
labs(title = 'Levy`s construction of Brownian motion. Step: {closest_state}', x = 'time', y = 'position') +
ease_aes('sine-in-out')

We continue What are typical football results? The notion of weaker and stronger has not been made precise. Is is true that a team that has say 5 Elo points more than another team is really stronger? What might be an appropriate threshold? A glance at the current Elo ranking might give an indication that teams in within 50 points may be considered as equally strong. But is this true? At which threshold the probabilities of win, draw, lose will change?

In this post we continue our investigation of Who wins the 2018 FIFA World Cup™? and take a first look on historical data of FIFA football matches. These are obtained from the site www.eloratings.net using the wayback machine and some copy-and-paste. Unfortunately, our data set obtained in this way is not complete and we did not obtain data on all FIFA matches in this millennium. However, we were able to retrieve all matches of the FIFA World cup 2018 participants plus the matches of Italy, the Netherlands, and Austria. Yes, Italy and the Netherlands are not qualified, but we still are convinced that these two teams are amongst the strongest teams in the world. We added Austria to pay homage to the country where we spent a lot of quality time.

In just a few weeks the 2018 FIFA World Cup™ starts. People already discuss passionately who is going to win and how the chances for their teams are. Almost everybody has an intuition, opinion, idea, feeling or whatsoever about the performances of the different nations. There might be a consensus among football experts and fans on the top favorites, e.g. Brazil, Germany, Spain, but more debate on possible underdogs. However, most of these predictions rely on subjective opinions and are very hard if not impossible to quantify. An additional difficulty is the complexity of the tournament, with billions of different outcomes, making it very difficult to obtain accurate guesses of the probabilities of certain events.

How can we make reasonable, objective and quantitative estimates of the outcomes? For example, what is the probability that Brazil, Germany or Spain will win the cup? What are the chances that England will make it to the Round of 16? What are the chances that Brazil beats Germany in the semifinals 7:1?

In this and the following posts, we give quantitative answers to all kind of these questions. This post will start with what we can learn by studying previous matches and tournaments. Once we found some appropriate data we will investigate which models are out there to model an event like the FIFA World Cup.