• Choose a category
• All
• Classic-Fiction

By Root 474 0

to systematic error. Kahneman and Tversky called such errors biases. We all use heuristics, and we all suffer from biases. But although optical illusions seldom have much relevance in our everyday world, cognitive biases play an important role in human decision making. And so in the late twentieth century a movement sprang up to study how randomness is perceived by the human mind. Researchers concluded that “people have a very poor conception of randomness; they do not recognize it when they see it and they cannot produce it when they try,”9 and what’s worse, we routinely misjudge the role of chance in our lives and make decisions that are demonstrably misaligned with our own best interests.10

Imagine a sequence of events. The events might be quarterly earnings or a string of good or bad dates set up through an Internet dating service. In each case the longer the sequence, or the more sequences you look at, the greater the probability that you’ll find every pattern imaginable—purely by chance. As a result, a string of good or bad quarters, or dates, need not have any “cause” at all. The point was rather starkly illustrated by the mathematician George Spencer-Brown, who wrote that in a random series of 101,000,007 zeroes and ones, you should expect at least 10 nonoverlapping subsequences of 1 million consecutive zeros.11 Imagine the poor fellow who bumps into one of those strings when attempting to use the random numbers for some scientific purpose. His software generates 5 zeros in a row, then 10, then 20, 1,000, 10,000, 100,000, 500,000. Would he be wrong to send back the program and ask for a refund? And how would a scientist react upon flipping open a newly purchased book of random digits only to find that all the digits are zeros? Spencer-Brown’s point was that there is a difference between a process being random and the product of that process appearing to be random. Apple ran into that issue with the random shuffling method it initially employed in its iPod music players: true randomness sometimes produces repetition, but when users heard the same song or songs by the same artist played back-to-back, they believed the shuffling wasn’t random. And so the company made the feature “less random to make it feel more random,” said Apple founder Steve Jobs.12

One of the earliest speculations about the perception of random patterns came from the philosopher Hans Reichenbach, who remarked in 1934 that people untrained in probability would have difficulty recognizing a random series of events.13 Consider the following printout, representing the results of a sequence of 200 tosses of a coin, with X representing tails and O representing heads: ooooxxxxoooxxxooooxxooxoooxxxooxxoooxxxxoooxooxoxoooooxooxoooooxxooxxxoxxoxoxxxxoooxxooxxoxooxxxooxooxoxoxxoxoooxoxooooxxxxoooxxooxoxxoooxoooxxoxooxxooooxooxxxxooooxxxoooxoooxxxxxxooxxxooxooxoooooxxxx. It is easy to find patterns in the data—for instance, the four Os followed by four Xs at the beginning and the run of six Xs toward the end. According to the mathematics of randomness, such runs are to be expected in 200 random tosses. Yet they surprise most people. As a result, when instead of representing coin tosses, strings of Xs and Os represent events that affect our lives, people seek meaningful explanations for the pattern. When a string of Xs represents down days on the stock market, people believe the experts who explain that the market is jittery. When a string of Os represents a run of accomplishments by your favorite sports star, announcers sound convincing when they drone on about the player’s “streakiness.” And when, as we saw earlier, the Xs or Os stood for strings of failed films made by Paramount and Columbia Pictures, everyone nodded as the industry rags proclaimed just who did and who did not have a finger on the pulse of the worldwide movie audience.

Academics and writers have devoted much effort to studying patterns of random success in the financial markets. There is much evidence, for instance, that the performance of stocks is random—or so close to being random