Algorithms To Live By: The Computer Science of Human Decisions

by Brian Christian

But an algorithm is just a finite sequence of steps used to solve a problem,

As the applicant pool grows, the exact place to draw the line between looking and leaping settles to 37% of the pool, yielding the 37% Rule: look at the first 37% of the applicants,* choosing none, then be ready to leap for anyone better than all those you’ve seen so far.

The full-information game thus offers an unexpected and somewhat bizarre takeaway. Gold digging is more likely to succeed than a quest for love. If you’re evaluating your partners based on any kind of objective criterion—say, their income percentile—then you’ve got a lot more information at your disposal than if you’re after a nebulous emotional response (“love”) that might require both experience and comparison to calibrate.

Of course, there’s no reason that net worth—or, for that matter, typing speed—needs to be the thing that you’re measuring. Any yardstick that provides full information on where an applicant stands relative to the population at large will change the solution from the Look-Then-Leap Rule to the Threshold Rule and will dramatically boost your chances of finding the single best applicant in the group.

Spend the afternoon. You can’t take it with you. —ANNIE DILLARD

As such, the explicit premise of the optimal stopping problem is the implicit premise of what it is to be alive. It’s this that forces us to decide based on possibilities we’ve not yet seen, this that forces us to embrace high rates of failure even when acting optimally. No choice recurs. We may get similar choices again, but never that exact one.

Intuitively, we think that rational decision-making means exhaustively enumerating our options, weighing each one carefully, and then selecting the best. But in practice, when the clock—or the ticker—is ticking, few aspects of decision-making (or of thinking more generally) are as important as one: when to stop.

2 Explore/Exploit The Latest vs. the Greatest

Seizing a day and seizing a lifetime are two entirely different endeavors. We have the expression “Eat, drink, and be merry, for tomorrow we die,” but perhaps we should also have its inverse: “Start learning a new language or an instrument, and make small talk with a stranger, because life is long, and who knows what joy could blossom over many years’ time.”

When balancing favorite experiences and new ones, nothing matters as much as the interval over which we plan to enjoy them.

“I’m more likely to try a new restaurant when I move to a city than ...

A sobering property of trying new things is that the value of exploration, of finding a new favorite, can only go down over time, as the remaining opportunities to savor it dwindle. Discovering an enchanting café on your last night in town doesn’t give you the opportunity to return.

The flip side is that the value of exploitation can only go up over time. The loveliest café that you know about today is, by definition, at least as lovely as the loveliest café you knew about last month. (And if you’ve found another favorite since then, it might just be more so.) So explore when you will have time to use the resulting knowledge, exploit when you’re ready to cash in. The interval makes the strategy.

By entering an almost purely exploit-focused phase, the film industry seems to be signaling a belief that it is near the end of its interval.

Robbins specifically considered the case where there are exactly two slot machines, and proposed a solution called the Win-Stay, Lose-Shift algorithm: choose an arm at random, and keep pulling it as long as it keeps paying off. If the arm doesn’t pay off after a particular pull, then switch to the other one.

Companies want to be around theoretically forever, and on the medical side a breakthrough could go on to help people who haven’t even been born yet. Nonetheless, the present has a higher priority: a cured patient today is taken to be more valuable than one cured a week or a year from now, and certainly the same holds true of profits. Economists refer to this idea, of valuing the present more highly than the future, as “discounting.”

GET A GIFT button turned out to be the best performer, even after the cost of sending the gifts was taken into account. For longtime newsletter subscribers who had never given money, PLEASE DONATE worked the best, perhaps appealing to their guilt. For visitors who had already donated in the past, CONTRIBUTE worked best at securing follow-up donations—the logic being perhaps that the person had already “donated” but could always “contribute” more. And in all cases, to the astonishment of the campaign team, a simple black-and-white photo of the Obama family outperformed any

In general, it seems that people tend to over-explore—to favor the new disproportionately over the best.

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.

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.

This move from “ordinal” numbers (which only express rank) to “cardinal” ones (which directly assign a measure to something’s caliber)

On what to keep, Martha Stewart says to ask yourself a few questions: “How long have I had it? Does it still function? Is it a duplicate of something I already own? When was the last time I wore it or used it?” On how to organize what you keep, she recommends “grouping like things together,” and her fellow experts agree.

Instead, the trio proposed what they believed to be the next best thing: “a hierarchy of memories, each of which has greater capacity than the preceding but which is less quickly accessible.” By having effectively a pyramid of different forms of memory—a small, fast memory and a large, slow one—maybe we could somehow get the best of both.

The optimal cache eviction policy—essentially by definition, Bélády wrote—is, when the cache is full, to evict whichever item we’ll need again the longest from now.

We could just try Random Eviction, adding new data to the cache and overwriting old data at random. One of the startling early results in caching theory is that, while far from perfect, this approach is not half bad.

Items you use often will end up back in the cache soon anyway. Another simple strategy is First-In, First-Out (FIFO), where you evict or overwrite whatever has been sitting in the cache the longest (as in Martha Stewart’s question “How long have I had it?”).

A third approach is Least Recently Used (LRU): evicting the item that’s gone the longest untouched (Stewart’s “When was the last time I wore it or used it?”).

Bélády compared Random Eviction, FIFO, and variants of LRU in a number of scenarios and found that LRU consistently performed the closest to clairvoyance.

The LRU principle is effective because of something computer scientists call “temporal locality”: if a program has called for a particular piece of information once, it’s likely to do so again in the near future.

Its like present bias. Or logarithmic spacing

In fact, this principle is even implicit in the interface that computers show to their users. The windows on your computer screen have what’s called a “Z-order,” a simulated depth that determines which programs are overlaid on top of which. The least recently used end up at the bottom.

An Australian who streams video from the BBC, for instance, is probably hitting local Akamai servers in Sydney; the request never makes it to London at all. It doesn’t have to. Says Akamai’s chief architect, Stephen Ludin, “It’s our belief—and we build the company around the fact—that distance matters.”

First, when you are deciding what to keep and what to throw away, LRU is potentially a good principle to use—much better than FIFO. You shouldn’t necessarily toss that T-shirt from college if you still wear it every now and then. But the plaid pants you haven’t worn in ages? Those can be somebody else’s thrift-store bonanza.

Second, exploit geography. Make sure things are in whatever cache is closest to the place where they’re typically used. This isn’t a concrete recommendation in most home-organization books, but it consistently turns up in the schemes that actual people describe as working well for them. “I keep running and exercise gear in a crate on the floor of my front coat closet,” says one person quoted in Julie Morgenstern’s Organizing from the Inside Out, for instance. “I like having it close to the front door.”

A doctor told me about her approach to keeping things. “My kids think I’m whacky, but I put things where I think I’ll need them again later, even if it doesn’t make much sense.” As an example of her system, she told me that she keeps extra vacuum cleaner bags behind the couch in the living room. Behind the couch in the living room? Does that make any sense?… It turns out that when the vacuum cleaner is used, it is usually used for the carpet in the living room.… When a vacuum cleaner bag gets full and a new one is needed, it’s usually in the living room. And that’s just where the vacuum cleaner bags are.

Noguchi is an economist at the University of Tokyo, and the author of a series of books that offer “super” tricks for sorting out your office and your life. Their titles translate roughly to Super Persuasion Method, Super Work Method, Super Study Method—and, most relevantly for us, Super Organized Method.

In short, the mathematics of self-organizing lists suggests something radical: the big pile of papers on your desk, far from being a guilt-inducing fester of chaos, is actually one of the most well-designed and efficient structures available. What might appear to others to be an unorganized mess is, in fact, a self-organizing mess.

The key idea behind Anderson’s new account of human memory is that the problem might be not one of storage, but of organization. According to his theory, the mind has essentially infinite capacity for memories, but we have only a finite amount of time in which to search for them.

The key to a good human memory then becomes the same as the key to a good computer cache: predicting which items are most likely to be wanted in the future.

The question was straightforward: what patterns characterize the way the world itself “forgets”—the way that events and references fade over time?

In all domains, they found that a word is most likely to appear again right after it had just been used, and that the likelihood of seeing it again falls off as time goes on.

So as you age, and begin to experience these sporadic latencies, take heart: the length of a delay is partly an indicator of the extent of your experience. The effort of retrieval is a testament to how much you know.

In 1874, Frederick Taylor, the son of a wealthy lawyer, turned down his acceptance at Harvard to become an apprentice machinist at Enterprise Hydraulic Works in Philadelphia. Four years later, he completed his apprenticeship and began working at the Midvale Steel Works, where he rose through the ranks from lathe operator to machine shop foreman and ultimately to chief engineer.

His answer was that you should begin by finding the single step that takes the least amount of time—the load that will wash or dry the quickest. If that shortest step involves the washer, plan to do that load first. If it involves the dryer, plan to do it last. Repeat this process for the remaining loads, working from the two ends of the schedule toward the middle.

Johnson’s analysis had yielded scheduling’s first optimal algorithm: start with the lightest wash, end with the smallest hamper.

If you have only a single machine, and you’re going to do all of your tasks, then any ordering of the tasks will take you the same amount of time.

If you’re concerned with minimizing maximum lateness, then the best strategy is to start with the task due soonest and work your way toward the task due last.

For instance, consider the refrigerator. If you’re one of the many people who have a community-supported agriculture (CSA) subscription, then every week or two you’ve got a lot of fresh produce coming to your doorstep all at once. Each piece of produce is set to spoil on a different date—so eating them by Earliest Due Date, in order of their spoilage schedule, seems like a reasonable starting point. It’s not, however, the end of the story.

Due Date is optimal for reducing maximum lateness, which means it will minimize the rottenness of the single most rotten thing you’ll have to eat; that may not be the most appetizing metric to eat by. Maybe instead we want to minimize the number of foods that spoil.

If you deliver the bigger project on Thursday afternoon (4 days elapsed) and then the small one on Friday afternoon (5 days elapsed), the clients will have waited a total of 4 + 5 = 9 days. If you reverse the order, however, you can finish the small project on Monday and the big one on Friday, with the clients waiting a total of only 1 + 5 = 6 days.