Book SummaryAlgorithms to Live By, by Brian Christian and Tom Griffiths

1-Page Summary1-Page Book Summary of Algorithms to Live By

In Algorithms to Live By, Brian Christian and Tom Griffiths make the case that computer science, a field that’s typically seen as highly specialized, actually contains a wealth of practical knowledge we can use to improve our lives. Computers can process tasks with blinding efficiency and quickly come up with creative solutions to complex problems. The authors of Algorithms to Live By argue that, by utilizing the same strategies as computers, we can do the same.

The authors assert that this is true because humans and computers face very similar problems. Both humans and computers are motivated to use their limited resources (which include memory, attention, and time) as optimally as possible. Consequently, many of the algorithms, or sets of instructions, that computers use to solve their problems work just as well in our own lives.

We’re going to discuss all eleven of Christian and Griffiths’s “algorithms to live by,” which we’ve divided into four categories: First, we’ll take a look at algorithms intended to help you make better decisions. Second, we’ll detail some algorithms to help you organize your life. Third, we’ll show off algorithms to help you solve difficult problems. Finally, we’ll discuss a couple of miscellaneous algorithms that don’t fit into the other categories.

Is the Brain Really a Computer?

Christian and Griffiths aren’t the first to compare the brain to a computer as the basis for their argument—researchers have been using this analogy for decades. Today, the question of whether or not we should view the brain as a complex computer is at the heart of a fierce debate.

Some experts claim that the metaphor is limiting insights on the cutting edge of neuroscience more than it’s aiding them. They poke holes in the metaphor, pointing out ways in which the brain behaves unlike a computer and arguing that such inaccuracies will lead researchers to misguided assumptions.

On the other hand, other experts argue that the brain-as-computer metaphor has not yet outlived its usefulness. In their eyes, it doesn’t matter if the brain doesn’t act like a computer—what matters is the fact that the brain accomplishes many of the same functions as computers do: It intakes, processes, and exports information. The fact that your brain computes makes it a computer.

Decision-Making Algorithms

Algorithm #1: How to Know When to Settle

Christian and Griffiths’s first algorithm is: To choose the best from a series of options, explore without committing for the first 37%, then commit to the next top pick you see. This algorithm is designed to solve something mathematicians call an “optimal stopping problem”—when faced with a series of options, when do you settle down and commit to the opportunity in front of you if you don’t know what opportunities will be available in the future?

For example, imagine you’re looking for a job and know your skills are in high demand. After a couple of days of searching, you receive an offer out of the blue that’s better than any of the available positions you’ve seen so far. However, it doesn’t have everything you’re looking for. Do you take it or keep searching for better options?

According to Christian and Griffiths, statisticians have determined that the optimal way to solve this problem is to initially reject all opportunities, exploring your options to get a sense of what quality looks like. Then, at a certain point, commit to the next option that’s better than any you’ve seen so far. By calculating the probability that you pick the best option available for every possible “pivot point” from exploration to commitment, researchers have determined that you should explore for the first 37% of options, then commit to the next best opportunity.

This Optimal Solution Still Falls Short

Mathematician Hannah Fry pokes holes in Christian and Griffiths’s strategy, demonstrating how likely it is to fail: If, following the algorithm, you’re unlucky enough to encounter the best available option during your exploratory period, you’d have to reject it and go on to reject every other option available, as none will be better than what you’ve encountered already. Even though Christian and Griffiths are offering a mathematically optimal algorithm, the odds of you finding the best option, she states, are a dismal 37%.

Fry does, however, offer a solution. Christian and Griffiths define success as claiming the best opportunity available, but if you’re willing to accept an opportunity that’s good, but not the best, you can vastly increase your chance of ending up satisfied.

If you’re okay with an option in the top 5%, for example, you should begin your commitment period just 22% of the way through. According to Fry, this raises your chance of success from 37% to 57%. If you’re willing to accept an option in the top 15%, you can pivot 19% of the way through for a whopping 78% chance of success.

Algorithm #2: How to Optimize Your Life

Christian and Griffiths’s next algorithm is a broader directive that applies to any area of your life you want to improve: To optimize your life, pursue whatever opportunity has a chance to be the greatest.

The authors frame life as a complex “multi-armed bandit” problem, referring to a model computer scientists use in machine learning. The multi-armed bandit is a theoretical experiment in which a decision-making agent is presented with a row of slot machines (“one-armed bandits”) and must try out different machines, learning from the outcomes to figure out which will pay off the most.

Christian and Griffiths explain that the multi-armed bandit problem’s optimal solutions are called “Upper Confidence...

Algorithms to Live By Summary Shortform Introduction

Algorithms to Live By is an instruction manual for life: a collection of unconventional wisdom drawn from the field of computer science. Computers and humans share many of the same problems, and the same solutions that have allowed us to optimize the field of computing may be the ticket to optimizing our own lives. Bestselling author Brian Christian and cognitive scientist Tom Griffiths team up to create a library of “algorithms to live by”—sets of instructions to help you make smarter decisions, organize your life, and get the most out of your day.

About the Authors

Brian Christian is one of the rare few to find critical success in both scientific and artistic fields. His academic work on human biases has been published in periodicals such as Cognitive Science, and his popular science writing has been featured in Best American Science & Nature Writing. His poetry, on the other hand, has been nominated for the Pushcart Prize and adapted into a short film.

Christian has received the most public recognition for his non-fiction writing career—along with Algorithms to Live By, he is the author of the award-winning books _[The...

Algorithms to Live By Summary Part 1: Decision-Making Algorithms | Chapter 1: When to Settle

In Algorithms to Live By, Brian Christian and Tom Griffiths make the case that the field of computer science has given us mathematically-proven sets of instructions, or algorithms, that show us how best to live our lives.

We’ve divided this guide into parts, grouping these algorithms according to purpose. We’ll begin with Part 1, a collection of algorithms to help you make better decisions. In Part 2, we’ll discuss algorithms to help you organize your life; in Part 3, we’ll detail algorithms you can use to solve difficult problems; and in Part 4, we’ll cover a couple of miscellaneous algorithms that don’t fit into our other categories.

Within each chapter, we’ll start by defining an algorithm and providing necessary context from the field of computer science. Then, we’ll offer a set of additional instructions to help you best apply the algorithm in various situations.

In this chapter, we’ll include an introduction to the idea of algorithms to live by and explain why we’d want to use them. Then, we’ll discuss our first decision-making algorithm.

Why Should We Live by Algorithms?

Christian and Griffiths anticipate an objection to their argument for algorithms to...

Shortform Exercise: Evaluate Your Commitment Habits

Compare your habitual style of commitment to Christian and Griffiths’s “optimal” standard.

Think back to a time where you had to commit to one thing from a series of choices—for example, choosing a project at work or a vacation destination. Are you more the type of person to get excited and rush to commit too soon, or do you spend too much time painstakingly deliberating before committing to something? Explain your answer.

Algorithms to Live By Summary Chapter 2: How to Optimize Your Life

Christian and Griffiths’s next algorithm to help you make decisions is a broader directive that applies to any area of your life you want to improve: To optimize your life, pursue whatever opportunity has a chance to be the greatest.

Christian and Griffiths frame life as a complex “multi-armed bandit” problem, referring to a model computer scientists use in machine learning. The multi-armed bandit is a theoretical experiment in which one decision-making agent is presented with a row of slot machines (“one-armed bandits”) and must determine how to maximize their winnings without knowing the odds for any machine. The agent must try out different machines and learn from the outcomes to figure out which will pay off the most.

To do so, the agent strikes a balance between using the machines that have proven to pay out in the past and trying new machines to see if they pay out more—balancing “exploitation” and “exploration,” as they say in computer science.

Christian and Griffiths argue that life works in much the same way. The only way to know for certain if something will make you happy is if you try it for yourself. This could be a place to live, a relationship, or a...

Algorithms to Live By Summary Chapter 6: How to Predict the Future

The next decision-making algorithm posed by Christian and Griffiths addresses the problem of an unpredictable future: To make better predictions, combine prior knowledge with existing data.

One of the biggest obstacles preventing us from making good decisions is our inability to reliably predict the future. For example, it might be easy to know whether or not you should quit your job if you knew you would get a raise at work within the next three months. As it is, the uncertain world prevents us from making decisions with much confidence. However, Christian and Griffiths argue that by properly utilizing probability theory, you can come up with a surprisingly reasonable prediction in any situation. Let’s explore this idea in detail.

The Algorithm and Why It Works

The authors use a theorem of statistics called “Bayes’s Rule” to make predictions. Instead of diving into the formal mathematics behind the theorem, Christian and Griffiths simply use it to express the idea that you should first use your prior knowledge of the situation to estimate the chances of something happening, then adjust based on observable data. For example, if you want to predict when you’ll...

Shortform Exercise: Try Predicting the Future

Christian and Griffiths offer a specific step-by-step process to follow while making predictions—try it out now.

Think of a prediction to make about something in your life. Does this event follow a normal, power-law, or Erlang distribution? What led you to this conclusion? (For example, if you wanted to predict whether or not your sister is going to get engaged in the next six months, you would begin by noting that this event follows a normal distribution. This distribution isn’t power-law because it’s constrained by human life—no one gets engaged for a thousand years—and it’s not Erlang because other events can influence how long it will take for her to get engaged, such as a recent breakup.)

Algorithms to Live By Summary Chapter 7: Why You Should Make Less Informed Decisions

Christian and Griffiths’s final algorithm to aid decision-making is as follows: To make better decisions, consider less information.

With this algorithm, the authors address the problem of overfitting. In statistics and machine learning, “overfitting” is when a statistical model takes too many variables into account, resulting in faulty understanding that fails to successfully predict additional data. When programming AI, telling it what data to ignore is just as important as telling it what to learn from.

(Shortform note: What makes overfitting such a dangerous problem is the fact that its flaws are hidden. An overfitted model can predict the exact data you feed it, creating the illusion of success, but in the real world, any new influence will cause it to fall flat. It’s also possible to “underfit” a model, but it’s not nearly as big of a problem as overfitting because an underfitted model is obviously wrong—it can’t predict anything.)

The Algorithm and Why It Works

Christian and Griffiths establish that whenever you make a decision, you’re using the existing...

Algorithms to Live By Summary Part 2: Organizational Algorithms | Chapter 5: How to Schedule Your Time

So far, we’ve taken a look at four algorithms intended to help you make better decisions. Next, we’re going to explore algorithms related to an area of life that computers had to master long ago: organization.

Christian and Griffiths show that by arranging your schedule, belongings, and other collections in specific ways, you can manage your life with the efficiency and precision of a computer.

Scheduling Algorithms to Choose From

The first organizational algorithms we’ll discuss relate to scheduling. How should you optimally spend your working hours every day? What tasks should you complete first?

Unlike in other chapters, Christian and Griffiths don’t offer a single algorithm to handle scheduling. Rather, they state that your optimal scheduling algorithm differs based on your goals and priorities. Christian and Griffiths offer four different algorithms for you to choose from, each with a unique advantage. Computers use these same algorithms to dictate which tasks to process and the order to process them in. There, too, designers choose an algorithm according to the specific needs of the machine.

(Shortform note: While Christian and Griffiths’s scheduling...

Shortform Exercise: Pick a Scheduling Algorithm

Take some time to determine which of Christian and Griffiths’s scheduling algorithms is best suited for your pending tasks.

Evaluate your current scheduling habits. How do you typically decide which task to work on first? Are you already instinctively following any of Christian and Griffiths’s scheduling algorithms? (For example, you might habitually follow Earliest Due Date, avoiding difficult, important tasks until someone forces you to meet a deadline.)

Algorithms to Live By Summary Chapter 4: How to Efficiently Organize Your Belongings

The next algorithm to help you organize your life gives you easy access to the things you need: To efficiently access any collection, segment it based on frequency of use.

The Algorithm and Why It Works

Christian and Griffiths explain that a computer can store vast amounts of data, but the more memory it has, the more data it has to search and the longer it takes to retrieve anything specific. Engineers solved this problem by inventing the “cache.” By grouping the information that needs to be accessed most often and searching through that smaller cache first, computers can find the data they need much faster.

Christian and Griffiths argue that we can employ this same strategy with anything that needs to be organized in our lives—our closets, files, bookshelves, you name it. To “cache” any collection, create a smaller collection of frequently used items as close as possible to the place you’ll need them. Leave a couple of your favorite board games underneath the coffee table. Keep a bowl of fruit on the kitchen counter. Put the contacts you call and text the most on your phone’s “favorites” list. Real-life applications of caching are everywhere.

The authors argue...

Algorithms to Live By Summary Chapter 3: How to Sort Like a Computer

Christian and Griffiths’s final organizational algorithm we’ll be discussing dictates the most efficient way to sort a group of items into a specific order.

We ask computers to sort lists countless times a day—more than we’d realize. Every time Netflix offers you a menu of options or you search for a YouTube video, the results have to be sorted in a specific order. Christian and Griffiths argue that we should make use of these same sorting algorithms to efficiently sort collections in our own lives.

For most collections that you’d want to sort in life, computer-inspired algorithms are unlikely to save you more than a few minutes. However, if you’ve been tasked to sort an enormous group—for example, if your company asks you to organize twenty years’ worth of old archived meetings on VHS by date—the right algorithms will help you finish in a fraction of the time.

Automated Sorting in the Real World

Christian and Griffiths imply that since the majority of automated sorting only happens digitally, it’s valuable for us to learn how to efficiently sort physical objects. Soon, however, this may no longer be the case—advancements in robotics and artificial intelligence are...

Algorithms to Live By Summary Part 3: Problem-Solving Algorithms | Chapter 8: How to Solve Impossible Problems

So far, we’ve taken a look at decision-making algorithms to help you determine what to do in life and organizational algorithms to help you do it as efficiently as possible. The next two algorithms we’ll be discussing will help you get yourself unstuck when you’re faced with complex problems with no obvious solutions.

In this chapter, Christian and Griffiths cite “constrained optimization problems.” These are problems in which you need to find the optimal arrangement of a set of variables to achieve the best outcome under specific constraints. If you’re trying to purchase the cheapest airline tickets available that get you the most vacation days with good weather or trying to minimize risk in your investment portfolio, you’re solving an optimization problem.

Christian and Griffiths explain that when optimization problems reach a certain level of complexity, they become impossible to solve in the sense that they would take too long for even the fastest computer on earth to calculate the perfect solution. Since we don’t have access to this level of computing power in our lives, perfect solutions are often far out of our reach, even for relatively simple optimization...

Shortform Exercise: Solve a Problem Through Relaxation

Christian and Griffiths argue that the relaxation strategies mathematicians use to solve their complex problems are a useful tool in any context. Try using constraint relaxation to solve one of your ongoing personal problems.

Describe an ongoing problem in your life that doesn’t seem to have a solution. What makes it so difficult to solve? (For example, if you feel like you’re being ignored at work, you may find the problem difficult to solve because you don’t want to confront your coworkers and come across as needy or insecure.)

Algorithms to Live By Summary Chapter 9: How to Solve Your Problems by Acting Randomly

Christian and Griffiths’s next problem-solving algorithm is all about the power of randomness: To move past dead ends, act randomly.

Just like a traveler who points to a globe to decide where to live, computers act randomly when they don’t know what else to do. Such behavior seems like an irrational human quirk, but Christian and Griffiths explain that pure randomness has several unexpected uses, in and outside of computer science.

(Shortform note: Even though computer scientists use it frequently, randomness doesn’t come naturally to computers. Most “random number generators” are actually “pseudorandom number generators,” because their random-looking numbers are actually created by performing the same mathematical calculations on a predetermined number called a “seed.” True random number generators typically draw their numbers from naturally-occurring random phenomena, such as atmospheric noise.)

Additional Background: The Hill-Climbing Algorithm

To understand why randomness is such a useful tool in our lives, we first need to explain why it’s so useful...

Shortform Exercise: Make a Random Decision

Christian and Griffiths argue that when you don’t know what else to do, random action is better than nothing. Try using a random suggestion to solve one of your problems.

Describe a problem you’ve been unable to solve or an area in life in which you feel stuck. Then, pick a number from one to six.

Algorithms to Live By Summary Part 4: Miscellaneous Algorithms | Chapter 11: How to Use Game Theory

To conclude, we’ll take a look at a few of Christian and Griffiths’s algorithms that quite don’t fit into any of our previous categories.

This next algorithm shows us how we should view the rules that govern our society: To prevent collective harm, design the rules of the game to create win-win scenarios. Here, Christian and Griffiths introduce us to the idea of game theory, the mathematical study of competition between strategic decision-makers—people, computers, or even animals.

In these competitions, each player is trying to maximize their benefits, but importantly, their behavior has a direct impact on the other players. This typically results in endless strategizing and re-strategizing as each player attempts to predict what the others will do, knowing that everyone else is trying to do the same thing.

The Impact of Game Theory on Economics

Christian and Griffiths claim that game theory was one of the most influential theoretical advances of the twentieth century. What made it such a significant discovery?

By far, game theory’s largest impact has been in the field of economics. For most of modern history,...

Algorithms to Live By Summary Chapter 10: How to Enhance Communication With Network Algorithms

To conclude, we’ll cover a set of algorithms that Christian and Griffiths have adapted from the Internet’s networking protocols. Transmission Control Protocol (“TCP”) is the main body of rules dictating how computers communicate over the Internet. These algorithms ensure that both sides understand the information being sent, make sure it’s successfully received, and manage congestion resulting from unpredictable quantities of transmitted data.

(Shortform note: Even though this protocol was established way back in 1974, its basic structure is exactly the same today. According to Vint Cerf, co-creator of TCP, the protocol has lasted this long because they specifically designed it to never become obsolete—they wrote a language that could be used regardless of what medium was transmitting it. This way, the protocol would continue to function effectively through networking technology that hadn’t been invented yet, like Satellite Internet.)

In this chapter, we’ll discuss four algorithms from TCP that, according to Christian and Griffiths, we can and should apply in various areas of human life.

Algorithm #1: To...

Shortform Exercise: Reflect on the 11 Algorithms to Live By

Reflect on Algorithms to Live By as a whole. Self-help computer science is a bold, unusual idea—decide whether or not you think the authors were successful.

Recall the 11 algorithms we discussed. Which algorithm are you most eager to implement in your life, and why? How will it change the way you live?