Why Business Defies Logic

There seems to be more pressure than ever before to eliminate uncertainty. The financial markets demand it. Corporate compensation systems reward it. The use of technology to 'perfect' algorithmic financial modeling seeks to offer reassurance that the goal is attainable.

But, as always, we invite disaster when we ignore history. It is, as Oscar Wilde once said in a similar context, 'the triumph of hope over experience.'

We are philosophically inclined to believe in certainty because of the power computers and data have provided us. As the following article explains, though, we simply make the uncertainty more certain when we attempt to eliminate the possibility that it exists - and that we have to learn to deal with it. JL

Greg Satell comments in Digital Tonto:

While philosophers and scientists saw the flaws in reductionism, business leaders spawn(ed) scientific marketing, financial engineering and the six sigma movement.By reducing factors to measurable units and creating a compelling logic for effects, they sought to wring uncertainty out of the decision making process.  It was thought that what could be measured could be predicted and therefore controlled. (But) marketers still saw their campaigns flop. Markets displayed more volatility than financial engineering models said they were supposed to. In fact, an excessive focus on mathematical models can blind us to the real dangers that lie ahead.
In the early 20th century science and technology emerged as a rising force in western society.  The new wonders of electricity, automobiles and telecommunication were quickly shaping how people lived, worked and thought.  Empirical verification, rather than theoretical musings, rose to the fore.
It was against this backdrop that Moritz Schlick formed the Vienna Circle, which became the center of the logical positivist movement and aimed to bring a more scientific approach to human thought. Throughout the 20’s and  and 30’s, the movement spread and became a symbol of the new technological age.
In time, the positivist movement came to be widely recognized as a failure, yet still it has inspired no shortage of imitators.  There seems to be an endless stream of thought leaders and consultants who claim to have engineered a more “scientific” approach to business.  Yet they, just like the positivists, always seem to fall short.  Unfortunately, the real world defies logic.
A World Of Atomic Facts
The inspiration for the Vienna Circle was a young Austrian wunderkind named Ludwig Wittgenstein and their bible was his Tractatus Logico-Philosophicus. Wittgenstein, at the time, believed that much of the confusion surrounding problems stemmed from a failure to state them clearly.  He thought that if we could be more precise, many difficulties would simply melt away.
So he set out to create a perfectly logical language.  Statements would be reduced to atomic facts that could be verified as being true or false—no opinions or speculation allowed.  Those statements, in turn, would be governed by a set of logical algorithms which would determine the validity of any argument or set of arguments.
The Tractatus is, even today, considered a masterwork and one of the most influential books ever written.  Still, the idea of a perfectly logical language quickly ran into problems.  First, it became clear that, in the real world, the set of verifiable facts is far too small to be useful. Second, Kurt Gödel, a member of the Vienna Circle, would later show that logic itself is flawed.
In time, even Wittgenstein himself would renounce many of the ideas in the Tractatus and the positivist insistence on verifiability would give way to Karl Popper’s less stringent standard of falsifiability.  Still, the positivist idea would live on, not least of all among business leaders and economists, with sometimes disastrous consequences.
The Rise And Fall Of Business Engineering
Reductionism in business, which can be traced to the scientific management ideas of Frederick Winslow Taylor, actually predates the Vienna Circle.  Yet while philosophers and scientists soon saw the flaws in reductionism, business leaders pressed forward, spawning offshoots such as scientific marketing, financial engineering and the six sigma movement.
Much like Wittgenstein’s Tractatus, each of these promised a more rational approach.  By reducing factors to small, measurable units and creating a compelling logic for evaluating their effects, they sought to wring uncertainty out of the decision making process.  In effect, it was thought that what could be measured could be predicted and therefore controlled.
In each case, the approach was found wanting.  Sophisticated marketers, investing heavily in research and verification, still saw their campaigns flop.  Markets consistently displayed far more volatility than the financial engineering models said they were supposed to.  One study found that the vast majority of companies employing six sigma actually trailed the market.
The problem is that any engineering approach has to be applied to a theoretical model and models, no matter how cleverly constructed, are not perfect representations of the real world.  Numbers, despite what many say, can lie.  In fact, as we saw in the 2008 financial crises, an excessive focus on mathematical models can blind us to the real dangers that lie ahead.
Noah Effects And Joseph Effects
In the 1960’s, a young mathematician at IBM Research named Benoit Mandelbrot began to study the strange phenomenon of noise in communication lines.  It was a maddening problem, because data transmissions would tend to be smooth for long periods and then, for no discernable reason, go haywire.  No one could seem to figure out why.
Yet Mandelbrot, using mathematical techniques that were obscure at the time, identified the pattern, which he referred to as “Noah effects” and “Joseph effects.”  Joseph effects, as in the biblical story, supported long periods of continuity.  Noah effects, on the other hand, were like a big storm creating a massive flood of discontinuity, washing away the previous order.
Sometime later a professor at Harvard invited Mandelbrot to give a talk about his findings.  When he arrived, he was startled to see one of his diagrams on the professor’s wall.  Asking how the professor managed to get his hands on the material before he gave his talk, Mandelbrot was even more shocked to find that he was looking not at his own work, but cotton prices.
That began Mandelbrot’s long feud with financial models.  The idea, more recently popularized in Nassim Taleb’s The Black Swan, was that the effort to predict real world events is doomed to fail, because there will always be Noah effects—large unforeseen events— that will render those models irrelevant.  Engineering certainty, in other words, is a pipe dream.
Science, Not Pseudoscience
The problem with so-called scientific approaches to business models is that they are not scientific at all, but pseudoscientific.  That is, they borrow certain vocabulary and techniques from science, but apply none of its wisdom.  Science, after all, is not about certainty, but uncertainty. It is a method of inquiry, not an engineered solution.
The problem is that an idea, when accompanied by sophisticated charts and mathematical formulas, can seem like a sure thing.  We become so enthralled with the engineering of a solution that when something doesn’t fit it is dismissed as an “outlier.”  Yet it is these outliers— financial crises, iPhones and phenomenons like Justin Bieber—that create and destroy markets.
Still, while Noah effects are more consequential, we generally experience the world through Joseph effects, with a continuous stream of events highly dependent on that which preceded them.  Managers grow their business by doing more of what was successful in the past and less of what failed.  Most of the time, that’s a pretty safe bet.
Yet we know from Gödel that all systems fail and we know from Mandelbrot that extreme events will always thwart our best laid plans. So a truly scientific approach to business would not be based on certainty, but inquiry.  As I’ve argued before, we need to take a more Bayesian approach to strategy.  Getting it “right” isn’t as important as becoming less wrong over time.
Or, as the physicist Richard Feynman put it, “The first principle is that you must not fool yourself and you are the easiest person to fool.”