On 2007-05-27 19:38:00, Allan Engelhardt wrote in CYBAEA Journal:
We are keen on innovation here at CYBAEA, so I feel obliged to mention two articles on the subject that I noticed this week.
The New Scientist (whose web site today crashes on connections from the UK but works fine when connecting from the US) reports (subscription required) that innovation is the driver for growth in cities, and that innovation grows as a power law against the number of inhabitants in the city with a factor of 1.2. This factor is greater than one, which has two interesting consequences.
First it implies that there are no upper limits on city sizes. The largest megacity is Tokyo with 35 million people, followed by a handful of cities around the 20 million mark. There is no reason why these urban structures should not continue to grow in size. The only limit is the limit on human ingenuity and innovation, since that is the driver for growth.
![[Boom and bust - from Newscientist.com]](/images/boom_and_bust.png)
From New Scientist
Second, and slightly contradictory, the models suggests that we are approaching a kind of innovation horizon where infinite innovation in infinite short time is required to sustain human civilization. The structure of growth is on of an super-linear boom followed by a "bust" which resets the growth to one with a slightly longer time to infinity.
But only slightly longer. If you reset superlinear growth at the time of a bust, the duration until the population would grow infinite again gets shorter.
This agrees with our experience in innovation: new ideas and technologies do not last as long as they used to and certainly do not take as long to disseminate into the population.
But taken to the logical conclusion, this means that to sustain the city you need a new major innovation every year, month, week, day, hour, minute, second, .... This does not appear to be sustainable. Researcher Geoffry West says: What is the nature of the end stage? We certainly do not have an answer.
Richard Florida, an economic geographer at George Mason University in Fairfax, Virginia, echoes West's concerns. "I worry if we push the speed of the urban organism past that of the human organism," he says. "Already we're hiring personal trainers and coaches to keep us going, and boosting our memory with computers." He adds that as cities continue to get larger, the contrast between the "talent-attracting haves" and the "talent-exporting have-nots" will increase, and the fault lines will not run between the developed and developing world, as they used to, but will split countries internally.
The picture is not very encouraging for the have-nots either. Take the US cities of Buffalo, Pittsburgh and Cleveland, whose populations have been steadily diminishing since 1960. "These are cities that stopped growing because they haven't found the next innovation cycle, and were left with something stagnant or collapsing," Bettencourt says.
On a lighter note, this weekend's Financial Times has an article called Better Great Than Never which argues that old ages is no barrier to innovation. While acknowledging that innovation is associated with youth in the popular imagination, the article goes to great lengths to demonstrate that it does not have to be so. We copy just one:
William Herschel, for example, was a music teacher before he turned to astronomy, discovering the planet Uranus at 43, infrared radiation and the asteroids at 62, and continuing to make observations into his 70s.
So what can we learn about inventiveness?
So, what are the prerequisites for inventiveness? Youth is not one of them. An exceptional memory is helpful, but ... no guarantee. Scientists of great originality are often poor students, according to psychologist Professor Liam Hudson, writing in The Oxford Companion to the Mind. Fellows of The Royal Society generally have the same class of university degree as less successful researchers. Nobel prizewinners mostly have the IQ of the average undergraduate; and there is no correlation - above a certain minimum, of course - between IQ and achievement ”in any sphere of adult endeavour studied so far”.
Inventors do seem to belong to a certain type, however. They are self-propelling, questioning and persistent. They need, says The Royal Institution’s James, a ruthless objectivity which resists the pressures of received wisdom or religious dogma. A capacity for free association, even fantasy, is important: Einstein prescribed a lively imagination unfettered by reason or logic. In The Act of Creation, Arthur Koestler suggests the key is analogy, bringing unrelated ideas together such as a diver who follows an underwater (i.e. subconscious) chain of which only the two ends are visible above the surface.
Why, then, in spite of the evidence, does it seem strange to speak of old innovators? Because there are impediments to creativity in later life, though not the ones we suppose. They are not biological blocks, but psychological or even cultural ones. People get tired or run out of ideas. Or they fall out of fashion as public taste changes. Or, in a society where youth is prized above everything, they lose confidence in their work, feeling that younger people do not wish to hear what they have to say. ”Perhaps,” says Tallis, ”there is a feeling of moving towards closure, that your life is complete, the job is over, like those individuals who don’t go to the doctor because they feel old and undeserving of attention.”
So whatever your age: innovate today and help your city and human civilization grow. There really are no excuses.
On 2010-07-13 07:47:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
I am not sure apeescape’s ggplot2 area plot with intensity colouring is really the best way of presenting the information, but it had me intrigued enough to replicate it using base R graphics.
The key technique is to draw a gradient line which R does not support natively so we have to roll our own code for that. Unfortunately, lines(..., type="l") does not recycle the colour col= argument, so we end up with rather more loops than I thought would be necessary.
We also get a nice opportunity to use the under-appreciated read.fwf function.
Read more (~535 words).
On 2010-06-22 11:45:00, Allan Engelhardt wrote in CYBAEA Journal:
We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is scary.
We now re-do the analysis four years later and, just because we can, we are using the leading companies of the London stock exchange instead of the largest American companies.
The results still hold. We called it the 3/2 rule: treble the number of workers and you halve their individual productivity. Large companies with ten times the number of employees are ¼ as productive as their smaller competitors.
Employee productivity is a big issue. If all the FTSE-100 companies achieved their average profits per employee, then the index would generate almost £1 trn of additional net profits for the economy.
Read more (~245 words).
On 2010-06-22 11:20:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is mildly scary.
We revisit the analysis for the FTSE-100 constituent companies and find that the relation still holds four years later and across a continent.
Read more (~763 words, 5 comments).
On 2010-06-17 09:05:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Following on from my previous post about improving performance of R by linking with optimized linear algebra libraries, I thought it would be useful to try out the five benchmarks Revolutions Analytics have on their Revolutionary Performance pages.
Read more (~300 words, 2 comments).
On 2010-06-15 10:21:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Can we make our analysis using the R statistical computing and analysis platform run faster? Usually the answer is yes, and the best way is to improve your algorithm and variable selection.
But recently David Smith was suggesting that a big benefit of their (commercial) version of R was that it was linked to a to a better linear algebra library. So I decided to investigate.
The quick summary is that it only really makes a difference for fairly artificial benchmark tests. For “normal” work you are unlikely to see a difference most of the time.
Read more (~934 words, 1 comments).
Join the discussion
There are no comments yet. Be the first to comment.