The Black Swan: The Impact of the Highly Improbable

It is easy to test the effect of reputation. One way would be to find papers that were written by famous scientists, had their authors’ identities changed by mistake, and got rejected. You could verify how many of these rejections were subsequently overturned after the true identities of the authors were established. Note that scholars are judged mostly on how many times their work is referenced in other people’s work, and thus cliques of people who quote one another are formed (it’s an “I quote you, you quote me” type of business).

Eventually, authors who are not often cited will drop out of the game by, say, going to work for the government (if they are of a gentle nature), or for the Mafia, or for a Wall Street firm (if they have a high level of hormones). Those who got a good push in the beginning of their scholarly careers will keep getting persistent cumulative advantages throughout life. It is easier for the rich to get richer, for the famous to become more famous.

In sociology, Matthew effects bear the less literary name “cumulative advantage.” This theory can easily apply to companies, businessmen, actors, writers, and anyone else who benefits from past success. If you get published in The New Yorker because the color of your letterhead attracted the attention of the editor, who was daydreaming of daisies, the resultant reward can follow you for life. More significantly, it will follow others for life. Failure is also cumulative; losers are likely to also lose in the future, even if we don’t take into account the mechanism of demoralization that might exacerbate it and cause additional failure.

Note that art, because of its dependence on word of mouth, is extremely prone to these cumulative-advantage effects. I mentioned clustering in Chapter 1, and how journalism helps perpetuate these clusters. Our opinions about artistic merit are the result of arbitrary contagion even more than our political ideas are. One person writes a book review; another person reads it and writes a commentary that uses the same arguments. Soon you have several hundred reviews that actually sum up in their contents to no more than two or three because there is so much overlap. For an anecdotal example read Fire the Bastards!, whose author, Jack Green, goes systematically through the reviews of William Gaddis’s novel The Recognitions. Green shows clearly how book reviewers anchor on other reviews and reveals powerful mutual influence, even in their wording. This phenomenon is reminiscent of the herding of financial analysts I discussed in Chapter 10.

The advent of the modern media has accelerated these cumulative advantages. The sociologist Pierre Bourdieu noted a link between the increased concentration of success and the globalization of culture and economic life. But I am not trying to play sociologist here, only show that unpredictable elements can play a role in social outcomes.

Merton’s cumulative-advantage idea has a more general precursor, “preferential attachment,” which, reversing the chronology (though not the logic), I will present next. Merton was interested in the social aspect of knowledge, not in the dynamics of social randomness, so his studies were derived separately from research on the dynamics of randomness in more mathematical sciences.

Lingua Franca

The theory of preferential attachment is ubiquitous in its applications: it can explain why city size is from Extremistan, why vocabulary is concentrated among a small number of words, or why bacteria populations can vary hugely in size.

The scientists J. C. Willis and G. U. Yule published a landmark paper in Nature in 1922 called “Some Statistics of Evolution and Geographical Distribution in Plants and Animals, and Their Significance.” Willis and Yule noted the presence in biology of the so-called power laws, atractable versions of the scalable randomness that I discussed in Chapter 3. These power laws (on which more technical information in the following chapters) had been noticed earlier by Vilfredo Pareto, who found that they applied to the distribution of income. Later, Yule presented a simple model showing how power laws can be generated. His point was as follows: Let’s say species split in two at some constant rate, so that new species arise. The richer in species a genus is, the richer it will tend to get, with the same logic as the Mathew effect. Note the following caveat: in Yule’s model the species never die out.