What are the meaning(s) and context(s) of intellectual capital (IC) numbers? Specifically the number 42? The number which, as those familiar with The Hitchhiker’s Guide to the Galaxywill know, was the (eventual) answer given by the gargantuan computer Deep Thought in response to “The Ultimate Question of Life, the Universe, and Everything”.
John Dumay PhD (Economics) Sydney, EMBA AGSM, MA (Bus. Research) MGSM, GC (Higher Ed.) Sydney, CPA, who is Associate Professor in Accounting at Macquarie University, Australia, chose the number in order to examine what he calls the “Accountingisation” of intellectual capital. His paper, succinctly and precisely entitled “42” is published in the journal SAGE Open, January-March 2015.
Bonus amusement. How many other standard English words can you think of which have a double ‘h’ as in Hitchhiker?
…“Many genes of small effect” became a sort of tepid curse. I myself prefer the stronger, more memorable phrase “Many Assorted Genes of Tiny Significance,” or MAGOTS — a mass of barely significant genes explaining little.
MAGOTS infest most GWA studies for a simple, brutal reason: If a gene variant reliably plays a large role in causing disease, both the variant and the disease it causes tend to be rare, because its carriers tend to die without leaving offspring. This is why the genetic contributions for common diseases and conditions usually come from MAGOTS — the effects of which, it bears repeating, are usually maddeningly obscure and unpredictable. This applies even to diseases and traits that run in families. Take height: Hundreds of genes of small effect, few clues to how they contribute, and no real target to tweak if, say, you want to make someone tall. The best way to engineer a tall person? Tell two tall people to tango.
Similarly, deep digs at cancer, schizophrenia, heart disease, hypertension, diabetes, intelligence, bipolar disorder, and height have found mostly MAGOTS….
So let me offer a hype filter. This one comes courtesy of the oceanographer Henry Bryant Bigelow, who helped found Woods Hole Oceanographic Institute. A century ago, Bigelow opened a letter his brother had written him from Cuba. His brother reported that while weathering a hurricane there, he had seen, flying by, what he was almost sure was a donkey.
With three words, Bigelow gently told his brother he didn’t quite believe him — and stated a maxim for maintaining the ever-curious but ever-skeptical stance that marks the good scientist.
“We investigate the time evolution of lead changes within individual games of competitive team sports. Exploiting ideas from the theory of random walks, the number of lead changes within a single game follows a Gaussian distribution. We show that the probability that the last lead change and the time of the largest lead size are governed by the same arcsine law, a bimodal distribution that diverges at the start and at the end of the game. We also determine the probability that a given lead is “safe” as a function of its size L and game time t. Our predictions generally agree with comprehensive data on more than 1.25 million scoring events in roughly 40,000 games across four professional or semi-professional team sports, and are more accurate than popular heuristics currently used in sports analytics….
The kicker is in their conclusion:
“Cynically, our results suggest that one should watch only the first few and last few minutes of a professional basketball game; the rest of the game is as predictable as watching repeated coin tossings. On the other hand, the high degree of unpredictability of events in the middle of a game may be precisely what makes these games so exciting for sports fans.”
Here’s further detail from the paper:
The paper also explores the same question as it plays out in other sports — American college football, American pro football, and professional hockey — where the answer is not so dismaying.
Are there things that you’re still trying to prove or haven’t been able to figure out? Can you be sure whether there’s really intention there?
Today in my lab, we are studying in two different, let’s say parallel, ways. One is the social relationship among plants. I think that would be incredibly interesting to try to explain how plants behave differently according to their neighbors. If the neighbors are relatives, there is a kind of behavior. If they are strangers, there is a completely new, completely different behavior. This is something that we are trying to find, how the plants are able to recognize the plants around them and how they can change their behavior accordingly.
The second point is memory and learning. This is the most important and the most fascinating. At the beginning of the past year, we published a paper where we were able to demonstrate that the plants were able to learn — for example, that a specific stimulus was not dangerous. They were able also to learn not to react, not to spend energy responding to a non-dangerous stimulus. What was incredibly surprising for us was that we left plants completely undisturbed for almost two months; at the end of the second month, the plants were all the time remembering that a specific stimulus was not dangerous. What we learned from that experiment was that plants had a memory. We don’t know how they can memorize, because they have no brain. There should be another, completely new system to store information that could be incredibly interesting to find out. The other thing we want to find out is how long this kind of memory is. Two months is a lot. Just to give you an example, in insects, the average length of the memory of information is 24 hours.
Despite the highly hazardous life-style led by comic book characters such as Tintin, we are unaware of any previous systematic description of the challenges and health impairments faced by Tintin in the course of his adventures.
Methods —We evaluated the spectrum of health impairments (HIs) that Tintin sustained in his 23 adventures as well as their causes, consequences, and relation to travel. We diagnosed Tintin’s HIs according to descriptive terms in the text. We then classified HIs as traumatic and non-traumatic, and distinguished between intentional (those perpetrated by others) and unintentional events.
Results — We found 236 events leading to 244 HIs, 13 kidnappings, six hospitalisations and two surgical procedures. There was a median of 8 HIs/adventure (range 1–30/adventure). The mean number of HIs per adventure was much greater before 1945 than subsequently (14.9 vs. 6.1; P = 0.002), which was also true of the number of kidnappings (11 vs. 2; P = 0.001). Of the 244 HIs, there were 191 cases of trauma (78.3%) and 53 non-traumatic problems (21.7%). The most common form of trauma was concussion (62%) whereas the most common forms of non-traumatic problems were sleep problems (15.1%), depression/anxiety (13%), and gas or chloroform poisoning (13%). Overall, we found 46 losses of consciousness (LoC), including 29 traumatic and 17 non-traumatic LoCs.