“Experts” vs. Common Sense

By | September 22, 2016 | 0 Comments

Years ago, when I was a medical resident, I knew a young doctor who was a wine aficionado. One day he told me this story.

A senior professor at our medical school was well past his prime when it came to medicine. His lectures were vacuous, and his recommendations on patient care were behind the times. However, he had written a book on wine, and we all thought of him as an expert on that subject.

My colleague was visiting a Napa Valley winery, and the chief vintner asked him if he knew the senior professor. When my colleague said he did, the vintner asked, “Tell me the truth − does he know any more about medicine than he does about wine?” My colleague tried unsuccessfully to smother a laugh.

We physicians knew about medicine and realized the professor was no longer competent in our field. But we knew little about wine, so we assumed he was an expert in that field. On the contrary, the vintner knew about wine and realized that the professor was ignorant there, but he knew little about medicine and assumed the professor was an expert in that field.

This experience led me to formulate Murphy’s Law of Expertise:

The less people understand what you are saying, the more likely they are to consider you brilliant.

As a recent example, consider David X. Li. He was born in China and got an economics degree there. He then immigrated to Canada, where he got an MBA and a PhD in statistics from Waterloo University. The irony is impossible to overstate.

Li’s brilliance soon became apparent, in both senses of that word. As “Wired” magazine states, “…legions of math and physics PhDs were required to create, price, and arbitrage Wall Street’s ever more complex investment structures.” Li moved from academia to Wall Street, where he was working for JPMorgan Chase when he published an article that gained wide attention.

The article was titled, “On Default Correlation: A Copula Function Approach.” Li purported to show a new method for predicting the default of securities, but without considering historical data. That is, he claimed to have an innovative way to evaluate the safety of investments, one that did not require the labor of analyzing what that investment had done in the past.

To me, that looks too good to be true. But Wall Street executives loved the idea. Like many university-educated people, they had been taught to worship abstract intelligence and academic achievement rather than practical experience and common sense. To them, the fact that they did not understand Li’s method was a plus, not a minus. To them, this proved how brilliant Li was, not how credulous they were.

Li devised an equation that might have been simple to him, but was too complex for Wall Street executives to understand:

Pr[TA<1,TB<1] = Φ21(FA(1)),Φ1(FB(1)),γ)

There, is that perfectly clear? It might be, if you had a PhD in mathematics from MIT or UCLA. And even then, you would not foresee the equation’s consequences in the real world of investments. But to an MBA, even from a prestigious business school, it might as well have been written in hieroglyphics. Nevertheless, the MBAs pretended to understand its implications.

And for a while, Li’s equation seemed to work. So long as the stock market and real-estate values continued to rise, it seemed that Li’s goose had laid a golden egg. But no one noticed − or at least admitted − that so long as the stock market and real-estate values continued to rise, almost any system would seem to work.

But then, despite Li’s assurances that historical market gyrations need not be considered, real-estate values and the stock market began to fall − as any reasonable person knew they eventually would. It became painfully obvious that Li’s goose had merely laid an egg.

The bottom dropped out in 2008. But Li claimed that he had told business executives of the drawbacks inherent in his method. He told the Wall Street Journal in 2005, “Very few people understand the essence of the model.”

But was this a warning, or just a boast about his mathematical skill? It reminds me of an old joke: “I must be smarter than Einstein − only five people in the world understand him, and nobody understands me.” In effect, this was similar to selling a medicine as a cure-all, while omitting the serious side-effects on the pretext that the average person couldn’t understand them. It was the worst kind of lie – a half truth.

Granted, Li’s equation was hardly the only cause of the economic collapse of 2008. Greed, incompetence, failed regulation, and lack of moral principles surely play major roles. But as I learned about Li’s equation, I was reminded of my old professor. The medical people thought he was a wine expert, and the wine people thought he was a medical expert − while in fact he was neither.

I believe that the same principle applies to Li. The financiers thought he was a mathematical genius, while the mathematicians thought he was a financial genius. Like my professor, he fell between two chairs. But unlike my professor, he nearly took the whole economy with him.

And I also believe that most of the executives knew they did not understand the implications of Li’s equation, but they were ashamed to admit it. I saw examples of this all through my medical training. Professors made statements that my colleagues and I did not understand, but none of us − including me − had the guts to say so at the time.

We were afraid that the others understood, and we didn’t want to be shown up as the only one who didn’t. And when an authority figure made a statement we considered wrong, few dared to challenge it. These are major pathologies of any bureaucracy.

Make no mistake − big business can be as bureaucratic as big government. The profit motive is no guarantee of anything except a desire to make a profit. It is surely no guarantee of competence. We see the results all around us.

Li is now back in China, where he reportedly holds a high position in Beijing. Not being a conspiracy theorist, I will not comment on this disturbing fact. But I will comment − as loudly as I can − on the danger of relying too heavily on people who appear to be experts in fields with which we are unfamiliar.

Computer systems that crash repeatedly?

Internet connections that fail frequently?

Shower heads that barely dribble?

Toilets that barely flush?

Detergents that barely wash dishes?

Self-driving cars that crash into large trucks and kill their drivers?

Major businesses whose main business is to absorb and sell off other businesses, rather than actually providing goods or services?

Huge banks on the verge of insolvency?

A military that spends time on transgender diversity while pilots cannibalize old planes for spare parts?

An immigration policy that excludes Western Europeans while admitting potential terrorists?

All these are, to a large extent, the result of depending on “experts.”

It may be time to redefine our terms:

● Perhaps it is time to redefine “intelligence” as the ability to foresee and solve actual problems, not a number on a piece of paper.

● Perhaps it is time to redefine “education” as learning actual facts, not regurgitating professors’ pet theories at a prestigious university.

● Perhaps it is time to redefine “authority” as one who learned a field from the bottom up, not one who parachuted onto third base and thinks he hit a triple.

● Perhaps it is time to redefine “the best and the brightest” as those who truly do good in the world, not those who devise arcane theories and then attempt to twist the facts to fit the theories.

● Perhaps it is time to redefine “teacher” as one who tries to make a subject easily understood, not someone who tries to impress listeners with how brilliant he must be to have mastered such indecipherable complexity.

● Perhaps it is time to redefine “expert” as someone who actually does something skillfully, not someone who uses complex computer models to predict events that don’t happen.

If we do that, perhaps we will again be served by business people whose businesses prosper, and by politicians whose policies succeed – rather than by clever people who fabricate clever theories to explain their clever failures.


Contact: dstol@prodigy.net. You are welcome to publish or post these articles, provided that you cite the author and website.

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