alone in Napoli

A combinatorics puzzle from The Riddler about a Napoli solitaire where 4 x 10 cards numbered from 1 to 10 are shuffled and the game is lost when a number (1,2, or 3) is equal to its position modulo 3 (1,2 or 3). A simple R code shows that the probability of winning is around 0.00831:

N=40
for(t in 1:1e6)F=F+!sum(!(sample((1:N)%%10)-(1:N)%%3))

ChatGPT bends over backward to achieve this figure! Now, the exact probability can be found by combinatorics. While there are 40! ways of permuting the 40 cards, those missing the coincidences are

multiplied by 4!4!4!28! (which I initially forgot), resulting in 0.00831:

for(i in 0:4)for(j in 0:4)for(k in 0:4)
      F=F+exp(lchoose(13,i)+lchoose(13,4-i)+3*lfactorial(4)+
          lchoose(14,j)+lchoose(13-i,4-j)+lfactorial(28)+
          lchoose(14-j,k)+lchoose(9+i,4-k)-lfactorial(40))

another drawer of socks

A socks riddle from the Riddler but with no clear ABC connection! Twenty-eight socks from fourteen pairs of socks are taken from a drawer, one by one, and laid on a surface that only fit nine socks at a time, with complete pairs removed. What is the probability that all pairs are stored without running out of space? No orphan socks then!!

Writing an R code for this experiment is straightforward

for(v in 1:1e6){
 S=sample(rep(1:14,2))
 x=S[1]
 for(t in 2:18){
  if(S[t]%in%x){x=x[S[t]!=x]}else{x=c(x,S[t])}
  if(sum(!!x)>9){
    F=F+1;break()}}}

and it returns a value quite close to 0.7 for the probability of success. I was expecting a less brute-force resolution but the the Riddler only provided the answer of 70.049 based on the above tree of probabilities (which I was too lazy to code).

shelled and riddled

Consider a shell game with three shells and a ball with The Riddler constraint that the location of the shell with the ball is always exchanged with the location of an empty shell, randomly chosen. If one starts with the ball as rightmost, what is the distribution of the location of the ball after N steps?

Running an exploratory R code like

o=rep(0,3)
for(n in 1:1e6){
  b=c(0,0,1)
  for(t in 1:N){
    i=sample((1:3)[!b],1);b=0*b;b[i]=1}
  o=o+b}

shows that the difference in probability is between the rightmost position and both others, starting at zero, and evolving as p⁺=(1-p⁻)/2, with the successive values 0,1/2,1/4,3/8,5/15,11/32,… Very quickly converging to 1/3.

self-avoiding random angles

An apparently easy riddle from The Riddler this week: given N random half-lines starting from a N-S segment, what is the probability that none ever intersect? If m ½-lines are on the same side of the segment, they will not intersect when their angle with the segment is decreasing with the longitude of the endpoint of this ½-line on the segment. Assuming that drawing a ½-line at random is akin to uniformely drawing an angle on (0,π), this no X’ing event happens when the m angles are properly ordered, a 1/m! event. Independently, the probability for the segments on the other side is 1/(N-m)! The joint probability is thus

What are the chances of that?

What are the chances that I review a book with this title, a few months after reviewing a book called What is luck?! This one is written by Andrew Elliott, whose Is that a big number? I reviewed a wee bit earlier… And that the cover of this book involves a particularly unlucky sequence of die as in my much earlier review of Krysz Burdzy’s book? (About 10⁻⁶ less likely than the likeliest draw!)

The (relative) specificity of this book is to try to convey the notions of chance and uncertainty to the general public, more in demonstrating that our intuition is most often wrong by examples and simulations, than in delving into psychological reasons as in Barbara Blatchley’s book. The author advances five dualities that underly our (dysfunctional) relation to chance: individual vs. collective, randomness vs. meaning, foresight vs. insight, uniformity vs. variability, and disruption vs. opportunity.

“News programmes clearly understand that the testimonies of individuals draw better audiences than the summaries of statisticians.” (p. xvii)

Some of the nice features of the book  are (a) the description of a probabilistic problem at the beginning of each chapter, to be solved at the end, (b) the use of simulation experiments, represented by coloured pixels over a grey band crossing the page, including a section on pseudorandom generators [which is less confusing that the quote below may indicate!], (c) taking full advantage of the quincunx apparatus, and (d) very few apologies for getting into formulas. And even a relevant quote of Taleb’s Black Swan about the ludic fallacy. On the other hand, the author spends quite a large component of the book on chance games, exhibiting a ludic tendency! And contemplates biased coins, while he should know better! The historical sections may prove too much for both informed and uninformed readers. (However, I learned that the UK Government had used a form of lottery to pay interests on premium bonds.) And the later parts are less numerical and quantified, even though the author brings in the micromort measurement [invented by Ronald Howard and] favoured by David Spiegelhalter. Who actually appears to have inspired several other sections, like the one on coincidences (which remains quite light in its investigation!). I finished the book rather quickly by browsing though mostly anecdotes and a lesser feel of a unified discourse. I did not find the attempt to link with the COVID pandemic, which definitely resets our clocks on risk, particularly alluring…

“People go to a lot of trouble to generate truly random numbers—sequences that are impossible to predict.” (p.66)

The apparition of the Normal distribution is somewhat overdone and almost mystical, if the tone gets more reasonable by the end of the corresponding chapter.

“…combining random numbers from distributions that really have no business being added together (…) ends up with a statistic that actually fits the normal distribution quite well.” (p.83)

The part about Bayes and Bayesian reasoning does not include any inference, with a rather duh! criticism of prior modelling.

“If you are tempted to apply a group statistic derived from a broad analysis to a more narrow purpose, you run the risk of making an unfair judgement.” (p.263)

The section about Xenakis’ musical creations as a Markov process was most interesting (and novel to me). I also enjoyed the shared cultural entries, esp. literary ones. Like citing the recent Chernobyl TV drama. Or Philip K. Dick’s Do Androids Dream of Electric Sheep? Or yet Monty Python’s Life of Brian. Overall, there is enough trivia and engagement to keep reading the book till its end!