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normal accident theory: explaining outliers

Leonard Mlodinow explains outliers better than Malcolm Gladwell ever could in The Drunkard’s Walk: How Randomness Rules Our Lives. For example:

That string of events spurred Yale sociologist Charles Perrow to create a new theory of accidents, in which is codified the central argument of this chapter: in complex systems (among which I count our lives) we should expect that minor factors we can usually ignore will by chance sometimes cause major incidents. In his theory Perrow recognized that modern systems are made up of thousands of parts, including fallible human decision makers, which interrelate in ways that are, like Laplace’s atoms, impossible to track and anticipate individually.Yet one can bet on the fact that just as atoms executing a drunkard’s walk will eventually get somewhere, so too will accidents eventually occur. Called normal accident theory, Perrow’s doctrine describes how that happens—how accidents can occur without clear causes, without those glaring errors and incompetent villains sought by corporate or government commissions. But although normal accident theory is a theory of why, inevitably, things sometimes go wrong, it could also be flipped around to explain why, inevitably, they sometimes go right.

For in a complex undertaking, no matter how many times we fail, if we keep trying, there is often a good chance we will eventually succeed. In fact, economists like W. Brian Arthur argue that a concurrence of minor factors can even lead companies with no particular edge to come to dominate their competitors. “In the real world,” he wrote, “if several similar-sized firms entered a market together, small fortuitous events—unexpected orders, chance meetings with buyers, managerial whims—would help determine which ones received early sales and, over time, which came to dominate. Economic activity is…[determined] by individual transactions that are too small to foresee, and these small ‘random’ events could [ac]cumulate and become magnified by positive feedbacks over time.”

Thanks to the Internet, this idea has been tested. The researchers who tested it focused on the music market, in which Internet sales are coming to dominate. For their study they recruited 14,341 participants who were asked to listen to, rate, and if they desired, download 48 songs by bands they had not heard of. Some of the participants were also allowed to view data on the popularity of each song—that is, on how many fellow participants had downloaded it. These participants were divided into eight separate “worlds” and could see only the data on downloads of people in their own world.

All the artists in all the worlds began with zero downloads, after which each world evolved independently. There was also a ninth group of participants, who were not shown any data. The researchers employed the popularity of the songs in this latter group of insulated listeners to define the “intrinsic quality” of each song—that is, its appeal in the absence of external influence. If the deterministic view of the world were true, the same songs ought to have dominated in each of the eight worlds, and the popularity rankings in those worlds ought to have agreed with the intrinsic quality as determined by the isolated individuals. But the researchers found exactly the opposite: the popularity of individual songs varied widely among the different worlds, and different songs of similar intrinsic quality also varied widely in their popularity.

For example, a song called “Lockdown” by a band called 52metro ranked twenty-six out of forty-eight in intrinsic quality but was the number-1 song in one world and the number-40 song in another. In this experiment, as one song or another by chance got an early edge in downloads, its seeming popularity influenced future shoppers. It’s a phenomenon that is well-known in the movie industry: moviegoers will report liking a movie more when they hear beforehand how good it is. In this example, small chance influences created a snowball effect and made a huge difference in the future of the song. Again, it’s the butterfly effect.

People systematically fail to see the role of chance in the success of ventures and in the success of people like the equity-fund manager Bill Miller. And we unreasonably believe that the mistakes of the past must be consequences of ignorance or incompetence and could have been remedied by further study and improved insight. That’s why, for example, in spring 2007, when the stock of Merrill Lynch was trading around $95 a share, its CEO E. Stanley O’Neal could be celebrated as the risk-taking genius responsible, and in the fall of 2007, after the credit market collapsed, derided as the risk-taking cowboy responsible—and promptly fired.

We afford automatic respect to superstar business moguls, politicians, and actors and to anyone flying around in a private jet, as if their accomplishments must reflect unique qualities not shared by those forced to eat commercial-airline food. And we place too much confidence in the overly precise predictions of people—political pundits, financial experts, business consultants—who claim a track record demonstrating expertise.

On an emotional level many people resist the idea that random influences are important even if, on an intellectual level, they understand that they are. If people underestimate the role of chance in the careers of moguls, do they also downplay its role in the lives of the least successful? In the 1960s that question inspired the social psychologist Melvin Lerner to look into society’s negative attitudes toward the poor. Realizing that “few people would engage in extended activity if they believed that there were a random connection between what they did and the rewards they received,” Lerner concluded that “for the sake of their own sanity,” people overestimate the degree to which ability can be inferred from success. We are inclined, that is, to see movie stars as more talented than aspiring movie stars and to think that the richest people in the world must also be the smartest.

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Filed under: Cognition,

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