detachment

One of the movies I watched during my hospitalisation is detachment, by Tony Kaye, with Adrian Brody as the lead actor. My daughter brought it to me as she remembered I was interested in it. detachment is a strong and highly original movie about the U.S. school system and the complete lack of prospects for the students in deprived suburbs. I have seen several movies of that kind in the past, some of them rather good and keeping away from the fairy tale that an exceptional teacher is enough to rescue a class cohort or even a single student from a bleak future. This one is however the most pessimistic of all, with no happy ending of any sort (except for the last minute that should have been cut). The plot is not flawless, e.g. the main teacher redemption of the young prostitute being just too unrealistic, but the burnout of the teachers, the newspeak preaching of the administration, the nihilism of the high school students, the bullying of unusual students, and the complete absolute absence of the parents (unless I am confused we only see one [screaming] mother once, no parent shows up at parents’ night and the bullying father is only a voice…) make up for those flaws. Adrian Brody is delivering a superb performance in a great movie, sadly about a terrible issue with our educational system(s)…

micro

“Indoctrinating children in proper environmental thought was a hallmark of the green movement.” M. Crichton, micro, p. ix

I believe I read most of Michael Crichton‘s novels and this posthumous version (completed by Richard Preston) is not very different in its style and pattern from the previous ones. micro delivers an efficient fast-paced techno-thriller that filled most of one afternoon when convalescing at home.  In that respect, it fills its intended role. I however feel this is one of the weakest novels in that the technological and scientific background is very poor. (The best Crichton’s novels are in my opinion The Andromeda Strain and Airframe. One of the last novels, State of Fear, carries a very anti-environmentalist and climatoskeptic  message similar to the above quote.)

“Perhaps the most important lesson to be learned by direct experience is that the natural world (…) represents a complex system and therefore we cannot understand it and we cannot predict its behavior. “ M. Crichton, micro, p. x

Indeed, the plot of micro is based on the assumption that there exists a technology that can miniaturise living and non-living objects to 1/100^th of their original size without any short-term impact. I remember watching as a child Fantastic Voyage, where a miniaturised submarine goes inside a blood vessel to remove a tumor, and I sat in front of a neighbour’s TV, mesmerised by the idea more than by the (weak) plot. This was in the laste 60′s. I also remember a sci’fi’ book I read when a pre-teen, with a great cover, called The Forgotten Planet: nothing truly memorable, apart from the cover, but hey this was a 1954 book. Now, micro does not use a deeper theory to justify this miniaturisation and the remainder of the plot is just as weak: I cannot imagine  1/100^th humans surviving more than a few minutes in a rain forest environment! The place is crawling with insects, all way faster and far more deadly than tiny humans with a pocket knife, but the heroes conveniently meet only one dangerous insect at a time, loosing only at most one member of the group each time (sorry for the spoiler!). (In fact, the earlier Prey was much better at involving nanotechnologies. ) The grad students are very charicaturesque as well, providing biological infodump at times when they should be frozen solid with fright. Provided they had not been eaten already. The final resolution of the thriller is just… grotesque! So wait until you are sick or recovering from being sick before embarking upon this micro and no so fantastic trip!

the cartoon introduction to statistics

A few weeks ago, I received a copy of The Cartoon Introduction to Statistics by Grady Klein and Alan Dabney, send by their publisher, Farrar, Staus and Giroux from New York City.  (Never heard of this publisher previously, but I must admit the aggregation of those three names sounds great!) As this was an unpublished version of the book, to appear in July 2013, I first assumed my copy was a draft version, with black and white drawings using limited precision graphics.. However, when checking the already published Cartoon Introduction to Economics, I realised this was the style of Grady Klein (as reflected below).

Thus, I have to assume this is how The Cartoon Introduction to Statistics will look like  when published in July… I am quite perplexed by the whole project. First, I do not see how a newcomer to the field can learn better from a cartoon with an average four sentences per page than from a regular introductory textbook. Cartoons introduce an element of fun into the explanation, with jokes and (irrelevant) side stories, but they are also distracting as readers are not always in  a position to know what matters and what does not. Second, as the drawings are done in a rough style, I find this increases the potential for confusion. For instance, the above cover reproduces an example linking the histogram of a sample of averages and the normal distribution. If a reader has never heard of histograms, I do not see how he or she could gather how they are constructed in practice. The width of the bags is related to the number of persons in each bag (50 random Americans) in the story, while it should be related to the inverse of the square root of this number in the theory.  Similarly, I find the explanation about confidence intervals lacking: when trying to reassure the readers about the fact that any given random sample from a population might be misleading, the authors state that “in the long run most cans [of worms] have averages in the clump under the hump [of the normal pdf]“. This is not reassuring at all: when using confidence intervals based on 10 or on 10⁵ normal observations, the corresponding 95% confidence intervals on their mean both have 95% chances to contain the true mean. The long run aspect refers to the repeated use of those intervals. (I am not even mentioning the classical fallacy of stating that “we are 99.7% confident that the population average is somewhere between -1.73 and -0.27″…)

In conclusion, I remember buying an illustrated entry to Marx’ Das Kapital when I started economics in graduate school (as a minor). This gave me a very quick idea of the purpose of the book. However, I read through the whole book to understand (or try to understand) Marx’ analysis of the economy. And the introduction did not help much in this regard. In the present setting, we are dealing with statistics, not economics, not philosophy. Having read a cartoon about the average length of worms within a can of worms is not going to help much in understanding the Central Limit Theorem and the subsequent derivation of confidence intervals. The validation of statistical methods is done through mathematics, which provides a formal language cartoons cannot reproduce.

Le Monde puzzle [#820]

The current puzzle is… puzzling:

Given the set {1,…,N} with N<61, one iterates the following procedure: take (x,y) within the set and replace the pair with the smallest divider of x+y (bar 1). What are the values of N such that the final value in the set is 61?

I find it puzzling because the way the pairs are selected impacts the final value. Or not, depending upon N. Using the following code (with factors() from the pracma package):

library(pracma)
endof=function(N){
  coll=1:N
  for (t in 1:(N-1)){

    pair=sample(1:length(coll),2)
    dive=min(factors(sum(coll[pair])))
    coll=coll[-pair]
    coll=c(coll,dive)
    }
  print(dive)
  }

I got:

> for (t in 1:10) endof(10)
[1] 5
[1] 3
[1] 3
[1] 5
[1] 7
[1] 5
[1] 5
[1] 7
[1] 3
[1] 3> for (t in 1:10) endof(16)
[1] 2
[1] 2
[1] 2
[1] 2
[1] 2
[1] 2
[1] 2
[1] 2
[1] 2
[1] 2

For N of the form 4k or 4k-1, the final number is always 2 while for N‘s of the form 4k-2 and 4k-3, the final number varies, sometimes producing 61′s. Although I could not find solutions for N less than 17…  Looking more closely into the sequence leading to 61, I could not see a pattern, apart from producing prime numbers as, in, e.g.

61 = 2 + [12 +  (4 + {14 + [13 + 16]})]

for N=17.  (Another puzzle is that 61 plays no particular role: a long run of random calls to endof() return all prime numbers up to 79…)

Udate: Looking at the solution in today’s edition, there exist a solution for N=13 and a solution for N=14. Even though my R code fails to spot it. Of course, an exhaustive search would be feasible in these two cases.  (I had also eliminated values below as not summing up to 61.) The argument for eliminating 4k and 4k-1 is that there must be an odd number of odd numbers in the collection, otherwise, the final number is always 2.

Ｒによるモンテカルロ法入門

Here is the cover of the Japanese translation of our Introducing Monte Carlo methods with R book.  A few year after the French translation. It actually appeared last year in August but I was not informed of this till a few weeks ago. The publisher is Maruzen, with an associated webpage if you want to order… Unless I am confused the translators are Hiro Ishida and Kazue Ishida; they deserve a major ありがとう ! And too bad George is no longer with us: this must have been the first translation of one of his books in Japanese..