to be a deliberate choice: as people approach the end of their lives, they want to focus more on the connections that are the most meaningful.
In an experiment testing this hypothesis, Carstensen and her collaborator Barbara Fredrickson asked people to choose who they’d rather spend thirty minutes with: an immediate family member, the author of a book they’d recently read, or somebody they had met recently who seemed to share their interests. Older people preferred the family member; young people were just as excited to meet the author or make a new friend. But in a critical twist, if the young people were asked to imagine that they were about to move across the country, they preferred the family member too. In another study, Carstensen and her colleagues found the same result in the other direction as well: if older people were asked to imagine that a medical breakthrough would allow them to live twenty years longer, their preferences became indistinguishable from those of young people. The point is that these differences in social preference are not about age as such—they’re about where people perceive themselves to be on the interval relevant to their decision.
Being sensitive to how much time you have left is exactly what the computer science of the explore/exploit dilemma suggests. We think of the young as stereotypically fickle; the old, stereotypically set in their ways. In fact, both are behaving completely appropriately with respect to their intervals. The deliberate honing of a social network down to the most meaningful relationships is the rational response to having less time to enjoy them.
Recognizing that old age is a time of exploitation helps provide new perspectives on some of the classic phenomena of aging. For example, while going to college—a new social environment filled with people you haven’t met—is typically a positive, exciting time, going to a retirement home—a new social environment filled with people you haven’t met—can be painful. And that difference is partly the result of where we are on the explore/exploit continuum at those stages of our lives.
The explore/exploit tradeoff also tells us how to think about advice from our elders. When your grandfather tells you which restaurants are good, you should listen—these are pearls gleaned from decades of searching. But when he only goes to the same restaurant at 5:00 p.m. every day, you should feel free to explore other options, even though they’ll likely be worse.
Perhaps the deepest insight that comes from thinking about later life as a chance to exploit knowledge acquired over decades is this: life should get better over time. What an explorer trades off for knowledge is pleasure. The Gittins index and the Upper Confidence Bound, as we’ve seen, inflate the appeal of lesser-known options beyond what we actually expect, since pleasant surprises can pay off many times over. But at the same time, this means that exploration necessarily leads to being let down on most occasions. Shifting the bulk of one’s attention to one’s favorite things should increase quality of life. And it seems like it does: Carstensen has found that older people are generally more satisfied with their social networks, and often report levels of emotional well-being that are higher than those of younger adults.
So there’s a lot to look forward to in being that late-afternoon restaurant regular, savoring the fruits of a life’s explorations.
*The basic summary of this section: git while the Gittins’s good.
Nowe if the word, which thou art desirous to finde, begin with (a) then looke in the beginning of this Table, but if with (v) looke towards the end. Againe, if thy word beginne with (ca) looke in the beginning of the letter (c) but if with (cu) then looke toward the end of that letter. And so of all the rest. &c.
—ROBERT CAWDREY, A TABLE ALPHABETICALL (1604)
Before Danny Hillis founded the Thinking Machines corporation, before he invented the famous Connection Machine parallel supercomputer, he was an MIT undergraduate, living in the student dormitory, and horrified by his roommate’s socks.
What horrified Hillis, unlike many a college undergraduate, wasn’t his roommate’s hygiene. It wasn’t that the roommate didn’t wash the socks; he did. The problem was what came next.
The roommate pulled a sock out of the clean laundry hamper. Next he pulled another sock out at random. If it didn’t match the first one, he tossed it back in. Then he continued this process, pulling out socks one by one and tossing them back until he found a match for the first.
With just 10 different pairs of socks, following this method will take on average 19 pulls merely to complete the first pair, and 17 more pulls to complete the second. In total, the roommate can expect to go fishing in the hamper 110 times just to pair 20 socks.
It was enough to make any budding computer scientist request a room transfer.
Now, just how socks should be sorted is a good way get computer scientists talking at surprising length. A question about socks posted to the programming website Stack Overflow in 2013 prompted some twelve thousand words of debate.
“Socks confound me!” confessed legendary cryptographer and Turing Award–winning computer scientist Ron Rivest to the two of us when we brought up the topic.
He was wearing sandals at the time.
The Ecstasy of Sorting
Sorting is at the very heart of what computers do. In fact, in many ways it was sorting that brought the computer into being.
In the late nineteenth century, the American population was growing by 30% every decade, and the number of “subjects of inquiry” in the US Census had gone from just five in 1870 to more than two hundred in 1880. The tabulation of the 1880 census took eight years—just barely finishing by the time the 1890 census began. As a writer at the time put it, it was a wonder “the clerks who toiled at the irritating slips of tally paper … did not go blind and crazy.” The whole enterprise was threatening to collapse under its own weight. Something had to be done.
Inspired by the punched railway tickets of the time, an inventor by the name of Herman Hollerith devised a system of punched manila cards to store information, and a machine, which he called the Hollerith Machine, to count and sort them. Hollerith was awarded a patent in 1889, and the government adopted the Hollerith Machine for the 1890 census. No one had ever seen anything like it. Wrote one awestruck observer, “The apparatus works as unerringly as the mills of the Gods, but beats them hollow as to speed.” Another, however, reasoned that the invention was of limited use: “As no one will ever use it but governments, the inventor will not likely get very rich.” This prediction, which Hollerith clipped and saved, would not prove entirely correct. Hollerith’s firm merged with several others in 1911 to become the Computing-Tabulating-Recording Company. A few years later it was renamed—to International Business Machines, or IBM.
Sorting continued to spur the development of the computer through the next century. The first code ever written for a “stored program” computer was a program for efficient sorting. In fact, it was the computer’s ability to outsort IBM’s dedicated card-sorting machines that convinced the US government their enormous financial investment in a general-purpose machine was justified. By the 1960s, one study estimated that more than a quarter of the computing resources of the world were being spent on sorting. And no wonder—sorting is essential to working with almost any kind of information. Whether it’s finding the largest or the smallest, the most common or the rarest, tallying, indexing, flagging duplicates, or just plain looking for the thing you want, they all generally begin under the hood with a sort.
