The Black Swan: The Impact of the Highly Improbable
by Nassim Nicholas Taleb
Random House, 2007
480 pp., $17.00
Outliers: The Story of Success
by Malcolm Gladwell
Little, Brown, 2008
336 pp., $16.99
Nassim Taleb is not asking himself what went wrong. In that, he is unlike those of us who work in or write about the financial sector. To judge from the conversations I’ve had in the last few years, a lot of us are pondering similar questions: Why did we pay bankers so much to take such huge risks with our money? How come neither we, nor they, realized that the rising incomes at the top were so often based on gambling rather than real improvements? How could we all have gotten everything so wrong?
Taleb is not asking those questions because he has long believed that the financial world pays too little attention to unlikely but catastrophic risks. He had the great good fortune to say so in The Black Swan, which was published the year before financial markets melted down. He took a fair amount of grief in the years before the crisis, but now he’s having the last laugh—a very grim one. For it now seems impossible to deny that human beings often do not actually get smarter about managing risk. We just think we do. It’s a lesson as important for philanthropy as it is for finance.
On the African plains where we evolved, ignoring very unlikely events—what statisticians call “outliers”—was probably a sound strategy. Humans became a remarkably successful species while relying mostly on a combination of learning heuristics. If fire has burned you the last 17 times you touched it, it will probably burn you the next time, too. On the other hand, you should pay more attention to recent events than to ones that happened a while ago. No matter how many times you went into the cave last year, if you found a hungry lion in there yesterday, he’s probably still there. Reasoning something out from first principles is an expensive modern luxury.
But where financial markets are concerned, argues Taleb, it’s often our crude heuristics that we can’t afford. Trading strategies that seem like brilliant ways to make huge sums of money generally turn out to have drastically underestimated the probability of a critical risk. Witness the Russian bond default in 1998, which wiped out a lot of people who had assumed the Russian government couldn’t afford to default, or at any rate wouldn’t dare to.
Gambling against an unlikely but potentially catastrophic risk can be a winning strategy for a long time. Eventually, of course, the law of large numbers will probably prevail, and you will lose everything. But humans seem to be wired to take exactly the wrong lesson from early success. The more often their gamble succeeds, the less risky they perceive it to be. Meanwhile, in many cases, the risks are actually mounting—especially if the outsized profits tempt people to double or triple down on their original bets.
These days, Taleb’s book looks stunningly prescient. Take the models used to price mortgage bonds, which virtually ignored default risk. Analysts focused their energy on trying to model when the borrowers would pre-pay, suddenly leaving investors with an unwanted pile of cash rather than a stream of interest payments. Worrying about pre-payment now seems quaint and faintly ludicrous, but since people tend to refinance when interest rates drop, this was a big deal for investors. Over a decade of rising house prices, the new generation of mortgage analysts had forgotten what their predecessors knew: house prices can fall, and when they do, defaults tend to spike.
This sort of thinking becomes especially problematic when you aren’t betting your own money. Institutional investors—including those entrusted with charitable assets—eagerly piled into investments that they didn’t really understand. To take an extreme example, J. Ezra Merkin devastated the endowments of Yeshiva University and more than 30 other charities by funneling them into Bernard Madoff’s Ponzi scheme. It is not clear that Merkin was aware that Madoff was a fraud. Rather, he seems to have been lulled into a sense of safety by the regularity of Madoff’s returns.
But when the money you are investing is not your own, those warning signals may not sound as loudly. People investing their own money risk losing everything they have saved. Managers of mutual funds or philanthropic trusts risk losing their jobs, of course. But they don’t necessarily lose their jobs when their investments have performed badly. Rather, they lose their jobs when their investments underperform the market, whether that market is booming or busting. There is therefore great incentive to jump into risky investments that offer outsized returns, as long as everyone else is doing the same thing. This is dangerous on both an individual and a social scale.
Donors have to weigh these risks particularly carefully. It is at Taleb’s “tails,” when the economy is in crisis, that charitable funds are in greatest demand. Yet organizations that structured their portfolios for outsized growth rather than consistent returns, as has become common, are now seeing their assets plummet. As a result, when their help is most needed, they are least able to offer it. There is no doubt that the growth during the boom years enabled many organizations to do a lot of good. But responsible philanthropy needs to balance the benefits of growth against the risk of failing to provide help during the times of direst want. When times are good, it is all too easy to forget the potential downside.
After all, if you try to see what the bankers saw during the housing bubble, their decisions seem almost sensible, especially in the really frothy years before subprime began its downward spiral. House prices had been rising for a long, long time, and as long as house prices rise, defaults are a minimal risk. A homeowner who can’t afford the payments simply sells the house. The analysts were working from scads of empirical data. In retrospect, of course, it is obvious that house prices couldn’t outstrip incomes forever. In fact, a lot of people, including me, said so at the time. But it wasn’t necessarily obvious how all the feedback loops would magnify unwise home loans into something dreadful: foreclosures damaging banks, banks withdrawing credit, businesses contracting, and unemployment further pushing up foreclosures. Such events happen perhaps once every 50 years, so few people were looking for them . . . except Nassim Taleb.
This should sound a klaxon warning to everyone who tries to wring the uncertainty out of the world by relying on “hard numbers.” There are any number of simple questions that could have led analysts to question their models, like “What happens if housing prices fall?” But no one asked that question, because they had such lovely data—70 years worth—showing that house prices didn’t fall, at least not all at once across the whole country. The clarity of numbers can be dangerously seductive, especially to people who are tackling hard problems.
Donors may be especially vulnerable to this problem in their grantmaking, unless they are very careful to review their quantitative data against qualitative experience in the real world. After all, the stupidest mortgage bankers and most feckless investors mostly lost their jobs or went out of business. But with no real market discipline (other than the prospect of donor fatigue), charitable funds can continue down the wrong road for decades. This is precisely the phenomenon that economist William Easterly has so ably chronicled in the developing world: over and over, NGOs and multilateral institutions hit the numeric metrics they have set for themselves, while making no actual improvement in the lives of the people they are trying to help.
The great benefit of the market is that it provides negative, as well as positive, feedback loops. For instance, Coca-Cola spent record sums on market research before they launched New Coke, which may have been the most spectacularly failed product launch in history. Company executives called the launch a smart gamble. They may be right. But it’s crucial that Coke was forced to change when people decided they liked the old Coke better; the company went back to the old formula after less than three months. If they’d been measuring their success by metrics like taste tests, rather than the market, New Coke might still be with us. It’s a problem that will be familiar to anyone running a charitable organizations, and perhaps also to donors, who are apt to be applauded for the size of the checks they write, rather than any particular outcome.
Where Taleb’s exegesis of the perils of positive feedback loops is bitter and biting (and not a little exaggerated on the topic of Taleb’s lone genius), Malcolm Gladwell’s new book, Outliers, offers a critique that is almost genial. Taleb says people are just lucky, because the disaster hasn’t happened yet. Gladwell says people are just lucky, because the disaster already happened to someone else.
If you want your child to be a star baseball player, when should you start preparing him? Ac-cording to Gladwell, before he’s so much as a twinkle in his father’s eye. To give your children the best shot at athletic success, you should try very hard to conceive them in January or February. This makes it likely that they will be born in August or September. How much of an advantage is this? According to Gladwell, almost twice as many baseball players are born in August as in July—the latter being the cutoff for junior league cohorts. Not only that, but the effect grows stronger the closer the birthday is to August 1—more major league players are born in August than in any other month.
Now, even most hard-nosed conservatives would admit that it is harder for the child of a single welfare mom to succeed than the child of two professionals. The disadvantages of poverty are obvious. But . . . birthday discrimination? What possible difference could it make?
For a child entering junior sports, the answer is “Quite a lot.” Sports segregate children by age, for understandable reasons, but in order to do so, they have to use an arbitrary cutoff. For many sports, that’s August 1. A child born July 31 will be allowed to play that year. A child born two days later will have to wait.
In that year, the child will grow. They will tend to be larger and more developed than their younger teammates: stronger, faster, with better reflexes and better ability to take instruction. In short, they have a much better shot at being the stars of their team.
Gladwell thinks this creates a positive feedback loop. The star players get more time playing, and may be singled out for special coaching. If they are early successes, their parents may be more willing to spend time driving them to practices and games, or send them to an expensive sports camp.
The book is a bit repetitious, since Gladwell basically only has the one point: innate talent doesn’t matter nearly as much as contingent factors. And the main reason the contingent factors matter so much is that they give people more opportunities to practice. Practice, he argues, is what makes a star. He claims that this is so universal that there’s a rule: it takes 10,000 hours of practice to become an expert. In fact, he seems to imply that anyone who does practice for 10,000 hours will become an expert near automatically. Even Bill Gates and Steve Jobs, in his account, succeeded largely because they had unique opportunities to practice on computers when they were young.
If you’re only going to make one point, it’s a good point to make, but Gladwell takes it far too far. Innate talent is not just contingent in his book; it practically disappears. And this is no more accurate than ascribing all success to the inner fabulousness of those who enjoy it.
After all, a lot of kids are born in the first half of the year. Just about half of them, in fact. Only a few of them make it to the major leagues. Bill Gates and Steve Jobs were not the only kids in America who had a lot of access to computers. But they were the only ones who grew up to be Bill Gates and Steve Jobs.
And what of the star athletes who didn’t have advantageous birthdays? If the players born in August have an unfair edge, aren’t those born in July then extra impressive? What unseen contingent factor is pushing them to greatness? Gladwell doesn’t spend a lot of time on them. He largely ignores the role of innate gifts. But while luck may make extensive practice possible, it often takes talent to make it palatable.
To hear Malcolm Gladwell tell it, I might have been a star ballerina, had I only done the requisite years of practice. But of course, I badgered my mother into withdrawing me from ballet class in fifth grade, because at 5-foot-6 and still growing, I was an uncoordinated, inflexible, oversized disaster. Similarly, slow, short kids don’t spend much time on the basketball court, and ham-handed, tone-deaf people don’t spend four hours a day practicing the cello on the off chance that they will suddenly metamorphose into Yo-Yo Ma. This cheerful suggestion that most of us could be most anything we wanted if we could just get enough practice is almost as irritating as Taleb’s insistence on the sterling rarity of his own genius. Positive feedback loops aren’t enough. Negative feedback is also critical.
But if both books give into the temptation to overstate their case, they nonetheless add up to a fairly powerful reason to revisit the idea of success in America—and, I think, of the way that the successful elite thinks about charity.
Over the last 50 years, we’ve replaced America’s old WASP elites with a new aristocracy of education. And in many ways, that aristocracy is more self-perpetuating than the old one. As a former colleague at the Economist, himself a product of a landed British family, once said to me, “I’m not so worried if the rich give their children money, because the undeserving children will squander it. But if the rich are giving their children the human capital to make themselves rich, it’s hard to see where it ends.”
The new elite, more so than the old one, is self-legitimating. Members of the new elite really have exerted themselves quite a lot to earn their position: excelled at good schools, worked obscene hours at difficult jobs. They tend to feel roughly entitled to what they get. Arguably, this has changed philanthropy, eroding the sense of noblesse oblige that used to drive the old elites to do what you might call society’s “maintenance work”: feeding the hungry, nursing the sick, sheltering the homeless.
By contrast, the modern philanthropist is someone who really likes to solve problems, which is how he got so successful, and there are a lot of really knotty problems to solve—as well as some warm fuzzy feelings, and considerable adulation for altruism. That may explain why so many of today’s efforts are aimed not at merely easing the afflictions of the least fortunate, but at being transformative: “ending the cycle of homelessness” or “eradicating malaria.”
In philanthropy as in life, people tell themselves stories when things are going well. Those stories do not weigh risks and probabilities. They’re rather like a good novel: all direct cause and inevitable effect. If your trading strategy is making a lot of money, that must mean that you are very smart, and possibly even that you are creating substantial value for your clients, and society. If you’ve worked long hours at a high-octane career, and you finally make partner, that must be because of your own talent and hard work. If your charitable giving is met with widespread applause, it must be doing real good.
Those stories are not entirely wrong. But our recent tribulations are a brutal reminder that they’re very incomplete. Neither a good education, nor a high IQ, nor even a solid track record of making money, are necessarily good measures of your value as a human being, or your contribution to society. Recognizing those factors also means recognizing a need for better strategies to give away our wealth—strategies that search for negative feedback, rather than the positive reinforcement we’d much rather enjoy. We need more books to remind us to look for the outliers, the contingencies, that shape our lives.
Megan McArdle is business and economics editor of The Atlantic.