What is luck? [book review]

I was sent—by Columbia University Press—this book for a potential review in CHANCE: What are the chances? (Why we believe in luck?) was written by Barbara Blatchley, professor of Psychology and Neuroscience at Agnes Scott College in Decatur, Georgia. I have read rather quickly its 193 pages over the recent trips I made to Marseille and Warwick. The topic is truly about luck and the psychology of the feeling of being luck or unlucky. There is thus rather little to relate to as a statistician, as this is not a book about chance! (I always need to pay attention when using both words, since, in French chance primarily means luck, while malchance means bad luck. And the French term for chance and randomness is hasard…) The book is pleasant to read, even though the accumulation of reports about psychological studies may prove tiresome in the long run and, for a statistician, worrisome as to which percentage of such studies were properly validated by statistical arguments…

“…the famous quote by Louis Pasteur: “Dans les champs de l’observation, le hasard ne favorise que les esprits préparés”s (…) Pasteur never saw a challenge he couldn’t overcome with patience and preparation.” (p.19)

Even the part about randomness is a-statistical and mostly a-probabilist, rather focusing on our subjective and biased (un)ability to judge randomness. The author introduces us to the concepts of apophenia, which is “the unmotivated seeing of connections accompanied with a specific feeling of abnormal meaningfulness”, and of patternicity for the “tendency to find meaningful patterns in meaningless noise”. She also states that (Neyman-Pearson) Type I error is about seeing a pattern in random noise while Type II errors are for conclusion of meaningless when the data is meaningful (p.15). Which is reductive to say the least, but lead her to recall the four types of luck proposed by James Austin (which I first misread as Jane Austin).

“There is a long-standing and deeply intimate connection between luck, religion, and belief in the supernatural.” (p.28)

I enjoyed very much the sections on these connections between a belief in luck and religions, even though the anthropological references to ancient religions are not strongly connected to luck, but rather to the belief that gods and goddesses could modify one’s fate (and avoiding the most established religions). Still, I appreciate her stressing the fact that if one believes in luck (as opposed to sheer randomness), this expresses at the very least a form of irrational belief in higher powers that can bend randomness in one’s favour (or disfavour). Which is the seed for more elaborate if irrational beliefs. (For illustrations, Borgès’ stories come to mind.)

“B.F. Skinner believed that superstitious behaviour was a consequence of learning and reinforcement.” (p.85)

There are also parts where (a belief in) luck and (human) learning are connected, but, unfortunately, no mention is made of the (vaguely) Bayesian nature of the (plastic, p. 188) brain modus operandi. The large section on the brain found in the book is instead physiological, since concerned with finding regions where the belief in luck could be located. In relation with attention-deficit disorders. (Revealing the interesting existence (for me) of mirror neurons, dedicated to predicting what could happen! Described as “predictive coding”, p.153). The last chapter “How to get lucky” contains a rather lengthy account of “Clever Hans”, the 1990 German counting horse (!). Who, as well-known, reacted to subtle and possibly unconscious signals from his trainer rather than to an equine feeling for arithmetic…

One of the clearest conclusions of the book is (imho) that a belief in luck may improve the life of the believers, while a belief in being unlucky may deteriorate it. The Taoist tale finishing the book is a pure gem. But I am still in the dark as to whether or not my exceptional number of bike punctures in the past year qualifies as bad luck!

“Luck is the way you face the randomness of the world.” (p.191)

As an irrelevant aside, one anecdote at the beginning of the book brought back memories of the Wabash River flowing through Lafayette, IN, as it tells of the luck of two Purdue female rowers who attempted a transatlantic race and survived capsizing in the middle of the Atlantic. It also made me regret I had not realised at the time there was a rowing opportunity there!

In Bayesian statistics, data is considered nonrandom…

A rather weird question popped up on X validated, namely why does Bayesian analysis rely on a sampling distribution if the data is nonrandom. While a given sample is is indeed a deterministic object and hence nonrandom from this perspective!, I replied that on the opposite Bayesian analysis was setting the observed data as the realisation of a random variable in order to condition upon this realisation to construct a posterior distribution on the parameter. Which is quite different from calling it nonrandom! But, presumably putting too much meaning and spending too much time on this query, I remain somewhat bemused by what line of thought led to this question…

laser sharp random number generator

Caught the headline of Science News on a super-fast random number generator based on a dysfunctional laser! Producing “254 trillion random digits per second”.

“…when the laser is shined on a surface, its light contains a constantly changing pattern of tiny pinpricks that brighten and dim randomly. The brightness at each spot in the pattern over time can be translated by a computer into a random series of ones and zeros.”

I presume this is covered in the original Science paper [which I cannot access] but the parallel series of 0’s and 1’s should be checked to produce independent Bernoulli B(½) variates before being turned into a genuine random number generator.

QMC at CIRM

certified RNGs

A company called Gaming Laboratories International (GLI) is delivering certificates of randomness. Apparently using Marsaglia’s DieHard tests. Here are some unforgettable quotes from their webpage:

“…a Random Number Generator (RNG) is a key component that MUST be adequately and fully tested to ensure non-predictability and no biases exist towards certain game outcomes.”

“GLI has the most experienced and robust RNG testing methodologies in the world. This includes software-based (pseudo-algorithmic) RNG’s, Hardware RNG’s, and hybrid combinations of both.”

“GLI uses custom software written and validated through the collaborative effort of our in-house mathematicians and industry consultants since our inception in 1989. An RNG Test Suite is applied for randomness testing.”

“No lab in the world provides the level of iGaming RNG assurance that GLI does. Don’t take a chance with this most critical portion of your iGaming system.”