Podcast
Introduction- Algorithms to Live By
Algorithms to Live By: The Computer Science of Human Decisions
- San Francisco Apartment Hunt Illustrates The Dilemma
- Brian Christian describes San Francisco apartment hunting as extreme: listings vanish in minutes and landlords favor first deposit.
- He frames the dilemma: sample enough to set a baseline, but risk losing the best if you wait too long. Transcript: Brian Christian Imagine you’re searching for an apartment in San Francisco, arguably the most harrowing American city in which to do so. The booming tech sector and tight zoning laws limiting new construction have conspired to make the city just as expensive as New York, and by many accounts more competitive. New listings go up and come down within minutes, open houses are mobbed, and often the keys end up in the hands of whoever can physically waste a deposit check on the landlord first. Such a savage market leaves little room for the kind of fact-finding and deliberation that is theoretically supposed to characterize the doings of the rational consumer. Unlike, say, a mall patron or an online shopper who can compare options before making a decision, the would-be San Franciscan has to decide instantly either way. You can take the apartment you are currently looking at, forsaking all others, or you can walk away, never to return. Let’s assume for a moment, for the sake of simplicity, that you care only about maximizing your chance of getting the very best apartment available. Your goal is reducing the twin slat and chrybdis regrets of the one that got away and the stone left unturned to the absolute minimum. You run into a dilemma right off the bat. How are you to know that an apartment is indeed the best unless you have a baseline to judge it by? And how are you to establish that baseline unless you look at and lose a number of apartments? The more information you gather, the better you’ll know the right opportunity when you see it, but the more likely you are to have already passed it by. So what do you do? How do you make an informed decision when the very act of informing it jeopardizes the outcome? It’s a cruel situation bordering on paradox. When presented with this kind of problem, most people will intuitively say something to the effect that it requires some sort of balance between looking and leaping, that you must Look at enough apartments to establish a standard, then take whatever satisfies the standard you’ve established. This notion of balance is in fact precisely correct. What most people don’t say with any certainty is what that balance is. Fortunately, there’s an answer. 37%. If you want the best odds of getting the best apartment, spend 37% of your apartment hunt. 11 days if you’ve given yourself a month for the search. Non-committally exploring options. Leave the checkbook at home, you’re just calibrating. But after that point, be prepared to immediately commit, deposit and all, to the very first place you see that beats whatever you’ve already seen. (Time 0:00:24)
- Use The 37% Rule For Optimal Stopping
- Use the 37% rule to decide when to stop searching and commit.
- Spend 37% of your search sampling options without committing, then take the next option that beats all you’ve seen. Transcript: Brian Christian This notion of balance is in fact precisely correct. What most people don’t say with any certainty is what that balance is. Fortunately, there’s an answer. 37%. If you want the best odds of getting the best apartment, spend 37% of your apartment hunt. 11 days if you’ve given yourself a month for the search. Non-committally exploring options. Leave the checkbook at home, you’re just calibrating. But after that point, be prepared to immediately commit, deposit and all, to the very first place you see that beats whatever you’ve already seen. This is not merely an intuitively satisfying compromise between looking and leaping. (Time 0:02:09)
- Optimal Stopping Turns Up Everywhere
- Optimal stopping problems apply whenever you see options sequentially and must decide to take or leave each one forever.
- Examples include apartments, parking, cashing out a venture, and dating, showing the rule’s broad applicability. Transcript: Brian Christian We know this because finding an apartment belongs to a class of mathematical problems known as optimal stopping problems. The 37% rule defines a simple series of steps, what computer scientists call an algorithm, for solving these problems. And as it turns out, apartment hunting is just one of the ways that optimal stopping rears its head in daily life. Committing to or foregoing a succession of options is a structure that appears in life again and again in slightly different incarnations. How many times to circle the box before pulling into a parking space? How far to push your luck with a risky business venture before cashing out? How long to hold out for a better offer on that house or car? The same challenge also appears in an even more fraught setting. Dating. Optimal stopping is the science of serial monogamy. Simple algorithms offer solutions not only to an apartment hunt, but to all such situations in life where we confront the question of optimal stopping. (Time 0:02:49)
- Life Problems Map To Computer Science Models
- Computer science frames everyday human constraints as problems of finite time, space, attention, and memory.
- Translating life dilemmas into scheduling, caching, and exploration problems reveals concrete solution structures. Transcript: Brian Christian How should a processor allocate its attention to perform all that the user asks of it with a minimum overhead in the least amount of time? When should it switch between different tests, and how many tasks should it take on in the first place? What is the best way for it to use its limited memory resources? Should it collect more data or take an action based on the data it already has? (Time 0:04:54)
- Algorithms Predate Computers And Shape Daily Practices
- Algorithms are not just modern code; they are sequences of steps humans have used for millennia.
- Examples range from recipes and knitting patterns to Sumerian long division and Al-Khwarizmi’s algebraic techniques. Transcript: Brian Christian But an algorithm is just a finite sequence of steps used to solve a problem, And algorithms are much broader and older by far than the computer. Long for algorithms were ever used by machines, they were used by people. The word algorithm comes from the name of Persian mathematician Al-Khwarizmi, author of a 9th century book of techniques for doing mathematics by hand. His book was called Al-Jabbar Wal Mukabbala, and the Al-Jabbar of the title in turn provides the source of our word algebra. The earliest known mathematical algorithms, however, predate even Al-Hoyrizmi’s work. A 4,000 Sumerian clay tablet found near Baghdad describes a scheme for long division. But algorithms are not confined to mathematics alone. When you cook bread from a recipe, you’re following an algorithm. When you knit a sweater from a pattern, you’re following an algorithm. When you put a sharp edge on a piece of flint by executing a precise sequence of strikes with the end of an antler, a key step in making fine stone tools, you’re following an algorithm. (Time 0:05:41)
- Apply Specific Algorithms To Everyday Tasks
- Borrow specific algorithmic strategies to handle human tasks like exploring, exploiting, sorting, caching, and scheduling.
- Apply explore-exploit to try new things, sorting to arrange spaces, and caching to organize closets. Transcript: Brian Christian Optimal stopping tells us when to look and when to leap. The explore-exploit trade-off tells us how to find the balance between trying new things and enjoying our favorites. Sorting theory tells us how and whether to arrange our offices. Caching theory tells us how to fill our closets. Scheduling theory tells us how to fill our time. (Time 0:07:06)
- Computers Now Trade Accuracy For Practicality
- Modern algorithms embrace chance, approximations, and trade-offs between time and accuracy rather than exhaustive calculation.
- This shift makes computer solutions more relevant as models for human decision-making under uncertainty. Transcript: Brian Christian Straightforward arithmetic, of course, isn’t particularly challenging for a modern computer. Rather, it’s tasks like conversing with people, fixing a corrupted file, or winning a game of Go, problems where the rules aren’t clear, some of the required information is missing, Or finding exactly the right answer would require considering an astronomical number of possibilities that now pose the biggest challenges in computer science. And the algorithms that researchers have developed to solve the hardest classes of problems have moved computers away from an extreme reliance on exhaustive calculation. Instead, tackling real-world tasks requires being comfortable with chance, trading off time with accuracy, and using approximations. (Time 0:09:09)
- Mistakes Reveal Problem Hardness Not Just Human Flaws
- Human ‘errors’ often reflect intrinsic problem difficulty, not just flawed brains.
- Studying algorithmic solutions reframes human cognition as confronting genuinely hard computational problems. Transcript: Brian Christian This self-deprecating story has become increasingly familiar, but certain questions remain vexing. Why are four-year for instance, still better than million-dollar supercomputers at a host of cognitive tasks, including vision, language, and causal reasoning? The solutions to everyday problems that come from computer science tell a different story about the human mind. Like this is full of problems that are quite simply hard. And mistakes made by people often say more about the intrinsic difficulties of the problem than about the fallibility of human brains. Thinking algorithmically about the world, learning about the fundamental structures of the problems we face and about the properties of their solutions, can help see how good we Actually are and better understand the errors that we make. In fact, human beings turn out to consistently confront some of the hardest cases of the problems studied by computer scientists. (Time 0:10:09)
- Follow Counterintuitive Algorithmic Rules For Hard Choices
- Embrace counterintuitive algorithmic wisdom: don’t always consider every option, make occasional messes, travel light, and trust instincts.
- These principles come with proofs and often outperform exhaustive thinking in hard problems. Transcript: Brian Christian These hard-won precepts are at odds with our intuitions about rationality, and they don’t sound anything like the narrow prescriptions of mathematicians trying to force the world Into clean, formal lines. They say, don’t always consider all your options. Don’t necessarily go for the outcome that seems best every time. Make a mess on occasion. Travel light. Let things wait. Trust your instincts and don’t think too long. Relax. Toss a coin. Forgive, but don’t forget. To thine own self be true. (Time 0:11:25)
- Authors Consult Algorithm Pioneers For Real Life
- The authors interviewed leading algorithm designers about applying their work to life, from finding spouses to sorting socks.
- Brian Christian and Tom Griffiths leverage interdisciplinary expertise to translate research into human algorithms. Transcript: Brian Christian Tom studied psychology and statistics before becoming a professor at UC Berkeley, where he spends most of his time thinking about the relationship between human cognition and computation. I studied computer science and philosophy before going on to graduate work in English and a career at the intersection of the three. But nobody can be an expert in all of the fields that are relevant to design better algorithms for humans. So as part of our quest for algorithms to live by, we talked to the people who came up with some of the most famous algorithms of the last 50 years. And we asked them, some of the smartest people in the world, how their research influenced the way they approached their own lives, from finding their spouses to sorting their socks. (Time 0:12:37)