The Gaussian curve, born from the dramatic life of Johann Carl Friedrich Gauss, gets applied across many fields.
"Born in Braunschweig into a poor family where his father worked as a brickmaker. Gauss's father didn't support him studying math and science, because he wanted Gauss to follow in his footsteps as a brick worker." (source: Wikipedia)
The graph of the Gaussian function is a symmetric bell-shaped curve that drops sharply toward +/- infinity. Parameter a sets the peak height, b the center position of the peak, and c the width of the bell. The Gaussian function is the derivative of the error function. It is also the density function of the normal distribution, and it's used widely in the natural sciences and statistics.
The basic idea behind applications is this: by looking at the expected value of a random variable, you can get a feel for the overall distribution.
"In probability theory and statistics, the variance of a random variable is a number that measures how far the random variable is distributed from the expected value. The expected value indicates the location of the random variable and the variance indicates how widely it is spread." (source: Wikipedia)
Related case 01)
Purple Cow. (Seth Godin)
Image source: http://novision.tistory.com/161
On this curve, Godin says to segment the innovation-stage buyers and the so-called early adopters, and advertise to them. In short, "create a remarkable product and target the minority who crave it."
Find the potential consumer group that will play the role of fervent evangelists, and give them a remarkable product—something worth talking about and recommending. Then equip them with incentives and communication tools to effectively spread it to friends and colleagues.
"Probably the biggest success story here is Facebook. It started at his school, stepped beyond the Ivy League, and gradually expanded worldwide, balancing different needs and wants along the way. A good example."
Related case 02)
'AIDS, the culprit behind management failure' (Samsung Economic Research Institute report)
Image source: http://grad.egloos.com/4180730
At first, those keenly interested in new tech—innovators and early adopters—are the first to encounter it. Unlike them, the majority who don't easily change their perception take time to accept the technology. The gap between these two groups is called a chasm.
Only when you cross the chasm and the majority also accepts the technology can you say that technology has succeeded in the market. So just because innovators and early adopters are excited about a new product, don't confidently declare success. Their nature is different from the majority. (source: don't mistake yourself for someone who can change customers' lives)
"This isn't a full rejection of Seth Godin's view, but it points to the risk around the probabilities. Honestly, many startups build user research and BM around Godin's framing, which leads to the kind of worrying situation described in the Samsung report. As this argues, the key question for service operators is how to preempt the chasm and manage the risk."
Related case 03)
Meanwhile, there's a somewhat dissenting take on Gaussian theory.
Taleb, the Wall Street sage, in The Black Swan exposed how inaccurate statistics based on Gaussian theory can be.
"Even if we assume there's a connection between probability and math, small numerical shifts in the real world aren't captured by the mild randomness represented by the normal distribution curve, but rather by a self-amplifying, wild randomness. What can be formalized isn't generally a Gaussian normal curve—it's Mandelbrotian."
He also says that if you look at the Matthew effect, the Pareto principle, and long-tail economics—all of which clearly describe the rich-get-richer/poor-get-poorer dynamic—you can see the current state of the economy.
"Since the 2008 financial crisis that started in the U.S. swept the world, the gap between rich and poor has only widened. People say the world has split into 1% rich and 99% poor."
Taleb, who became famous for predicting the financial crisis in The Black Swan, explained in detail through the 2008 market collapse how much reality gets distorted when statistics are built on the Gaussian normal curve.
According to him, the conventional statistics based on linear methods can't reflect all of the real variables, so confirmation bias is inevitable and it only reinforces whichever side's view you want.
That's why mainstream economists who clung to statistics based on the Gaussian normal curve (a bell curve with a thin top and wide bottom) failed to predict the 2008 market collapse, and keep silent on the polarization of wealth. (source: Matthew effect, Pareto principle, long-tail economics and wealth polarization)
Related case 04)
An example that extends Gaussian theory into ecosystem theory and the real economy.
# The more similar and the closer you are, the fiercer the fight gets
The Red Queen effect also applies to the real economy. A firm that once falls behind in tech competition struggles to catch up again. A new firm entering the race finds it hard to join the leading pack.
There are many examples where ecosystem theory expands into the real economy. One representative case is Gauss's theorem of competition strategy. The Russian scientist Gause ran two kinds of experiments. First he put two organisms of the same family but different species into the same space with limited food and watched what happened. They bickered now and then, but they divided the food and survived. Then he ran the same experiment with two organisms of the same family and species. They fought fiercely and both died. Gause explained this as 'the principle of survival through differentiation.' The more similar and the closer you are, the fiercer the fight and the harder survival becomes—an ironic truth of competition.
* 'Red Queen Effect'
A biological theory. The Red Queen appears in Lewis Carroll's (1832-1898) sequel, Through the Looking-Glass. In the story, the Queen grabs Alice's hand and they run through the forest, but Alice feels she can't move forward at all. When she asks why, the Queen says, "It takes all the running you can do just to stay in the same place. If you want to go anywhere, you must run at least twice as fast as that."
(source:Professor Park Yongtae's techno-management)
Additional info)
There's another interesting debate I want to add.
Lately I've been reading a book called Mood Matters, whose author is close to Taleb (author of The Black Swan) and whose ideas influence each other heavily. What's interesting is that he holds a different view and interpretation of Keynesian theory compared to Professor Park Yongtae referenced earlier, so I want to relay it here.
In Professor Park's piece referenced above, the following view on Keynes appears:
The anxiety that the economy can crash at any time, or eventually, is capitalism's Achilles' heel. Galbraith describes the psychological response to recession this way. "When large-scale downturns first came, people called them panics out of shock. Later, because the word 'panic' stirred fear, they called them 'depression.' 'Depression' sounded ominous, so they downgraded it to 'recession.' 'Recession' felt unpleasant, so these days they call it 'growth adjustment'—consoling themselves that it's a temporary tweak on the way to continued growth.
" So has our coping capability improved as much as our coping attitude has grown bolder? Unfortunately, it doesn't seem so. There was a time when people believed, through Keynesian policies that stimulated aggregate demand (New Deal-style), that recessions could be eliminated at the root. But as awareness spread in the 1970s that the global downturn was a supply-side issue from the oil shock rather than an aggregate-demand shortfall, faith in the Keynesian fix faded.
# Business cycles are driven by technological factors as well as economic ones
What replaced faith in Keynes was the confidence that by predicting the business cycle, we could prepare for downturns in advance. But today, even that confidence is gone. The traditional business-cycle models stopped working. The U.S. economy was predicted to pass its peak and head downhill, but instead had a 'ten-year boom.' Japan was supposed to pass its bottom and climb back up, but kept scraping bottom through its 'lost decade.' And as cycles diverge by sector, balance between industries and regions breaks, creating winners and losers.
What causes these 'irregularities' and 'imbalances'? Engineers argue that today's cycles are shaped by 'technological factors' as well as 'economic factors,' so the traditional models built around economic factors alone have become less accurate.
On the other hand, Mood Matters's author John L. Casti says:
People will agree that John Maynard Keynes was, if not the greatest economist of the 20th century, the most influential. In his groundbreaking The General Theory of Employment, Interest and Money, he talked about 'animal spirits,' which drive people to act outside the realm of deductive, rational thought—driven by feelings and beliefs rather than calculation. My book, likely to stir debate, tackles the heart of this very Keynesian idea, which can be summed up in three basic principles.
1. What determines the character and likelihood of actual events is the mood of a group or society—how the group feels about its future.
2. Actual events have no influence on the social mood. In other words, there's no flow from event back to mood. Put simply and briefly (at least as far as mood formation goes), events don't matter.
3. No social events occur outside of human social organization. So the notion of 'external' events that shape the mood of society members in one specific direction is entirely a fiction. There is no 'outside.'
(omitted...)
Back to Keynes, here's a quick look at the three core principles his economic theory rests on.
1. There are no external shocks: The storms battering today's economy arise from the financial system itself. These storms aren't external shocks that pummel markets; they're one normal mode of how the economic and financial system operates.
2. Networks: Keynes noted that society is divided into many groups. Economic efficiency isn't something to be pursued at all costs. There are many things in life worth pursuing besides pure profit.
3. Long-term stagnation: A market mired in internal collapse can stay stuck for a very long time. Keynes argued that the market needs intervention at a pace society can tolerate, to prevent a recession from sinking into a severe depression.
(omitted...)
I want to say a respectful farewell to Keynes by quoting the preface of The General Theory: "A monetary economy, as we shall see, is essentially one in which changing views of the future (emphasis by John L. Casti) are capable of influencing the quantity of employment and not merely its direction." His biographer Robert Skidelsky said this sentence is "at the heart of the Keynesian revolution." It's also at the heart of the ideas in this book.
From the standpoint of running a business—planning products or operating services—the most important piece is customer demand, acceptance, and their distribution. That's why theories like the Gaussian curve or Gaussian function can be a very attractive tool. But as the SERI report and The Black Swan show, the world has variables we can't perceive; even if we grasp them as objective figures, the emotions and states of each individual can lead to completely different outcomes. So we should pick the proper path over the royal road. For example: separate wants from needs and find alternatives for each.
Gauss's theory, and the Keynesian theory I added here, are refined and insightful. But the experts' views and insights I cited above differ widely or even interpret things entirely differently, while each holds clear and definite grounds for their own view.
The point is: let's not argue over which narrow opinion is right or wrong, or throw around jargon. Technical terms and theories don't matter. We can never decide what's good and bad, right or wrong—and we shouldn't try to. It's like one person looking at a kettle from above and another from below, each insisting their view is correct. Sometimes they systematize their views into technical terms like 'Top view' or 'Left view' (like the '○○ theory'...). Top view and Left view are each undeniably true without contradiction. How can anyone adjudicate right and wrong between left and up?
Every day new papers flood out; new theories, new disciplines, and new experts emerge. We learn and absorb various theories daily. But every time, I feel the same thing: 'It's not that we didn't know the new theory—we just didn't have the mental room to understand another person's situation, view, and stakes.'
What really matters to us isn't the view each of us sees, but "why he ended up above and I ended up to the left," or "why we ended up in different positions," or "given our different positions, what's good and bad, and weighing the opportunity cost of changing this," and preparing options for each case.
Analyzing and indexing the situation matters a great deal. It's a powerful weapon. And it will undoubtedly be a great tool for breaking through the current situation. But I hope we remember: just as people change, the situation shifts every moment, and no one's view or perception can ever be called wrong.
I'm reminded of what Song Kang-ho's character Nae-gyeong said at the end of the film The Face Reader:
"He only read the face, not the age. He only saw the waves, not the wind. It's the wind that makes the waves. You rode high on the wave, and we were below it. But one day, the wave will flip. (source: The Face Reader)
