PDF Summary:Algorithms to Live By, by Brian Christian and Tom Griffiths

Humans and computers face many of the same problems: We both have a never-ending stream of things we want to do and a limited amount of time and energy to do them. Is it possible that the age-old question of how best to live has already been solved by computer engineers? In Algorithms to Live By, science writer Brian Christian and Berkeley psychologist Tom Griffiths team up to prove that computer science is a fount of unconventional wisdom with practical value in many areas of human life.

In this guide, you’ll learn how to schedule your to-do list the same way computers do, why making random decisions is sometimes the smartest thing to do, and why you should reject the first 37% of jobs in your search for employment. We’ll also compare and contrast Christian and Griffiths’s advice to more traditional self-help perspectives on the same topics, such as the productivity advice in Brian Tracy’s Eat That Frog! and organizational advice in Marie Kondo’s The Life-Changing Magic of Tidying Up.

(continued)...

Minimalism: Stop Overfitting Your Life

Christian and Griffiths assert that to conquer overfitting, you must focus on what matters and ignore everything else. In Minimalism, Joshua Millburn and Ryan Nicodemus apply this logic to life itself.

Modern humans have a tendency to overfit, trying to make themselves happier by adding more to their lives instead of focusing on the few factors that matter. Goods like luxury cars, fancy homes, and picturesque vacations do nothing but distract us from the things in life that offer the most value, like personal health, loving relationships, and a sense of contribution to others.

In general, removing things in your life that don’t add value is a more sustainable path to happiness than constantly trying to add bigger and better new pleasures.

Organizational Algorithms

Algorithm #5: How to Schedule Your Time

Unlike in other chapters, Christian and Griffiths don’t offer a single algorithm to handle scheduling. Computers use algorithms chosen for their specific needs to determine what tasks to focus on first. Likewise, the authors state that your optimal scheduling algorithm differs based on your goals and priorities.

Christian and Griffiths assert that most of the time, your highest priority is to complete whatever tasks earn you the most value. They advise you to assign a “weight,” a numerical measurement of value, to every item on your to-do list. By dividing this weight by the amount of time it’ll take you to complete the task, you can easily calculate how much value you’re generating every hour that you’re working. Then, you should simply work at any given time on whatever gives you the most value per hour.

Another scheduling algorithm Christian and Griffiths recommend is “Shortest Processing Time,” which tells you to work on whatever task will take the shortest time to complete. The authors argue that you may choose to use this algorithm if you require motivation, or if you’re stressed and overwhelmed by a large quantity of tasks.

Do Your Hardest Tasks First

In the productivity bestseller Eat That Frog!, Brian Tracy places even more importance on the need to weigh your tasks by value. He argues that your most valuable tasks are almost always the most difficult to complete—as a result, most people procrastinate on these major tasks, filling their time with easy busywork that ends up accomplishing very little. Tracy’s thesis is that unless you intentionally tackle difficult high-value tasks first, life will hand you a never-ending supply of easy low-value tasks, and you’ll never get around to doing what’s truly important.

Tracy’s advice conflicts with Christian and Griffiths’s Shortest Processing Time algorithm in that he doesn’t find much use in tackling the shortest tasks first. He argues that, by breaking your most important tasks down into a series of shorter steps, you can translate everything you have to do into tasks that take approximately the same amount of time. Then, all you need to do is rank them by importance.

Algorithm #6: How to Organize Your Belongings

Christian and Griffiths’s next algorithm is intended to give you easy access to the things you need: To efficiently access any collection, segment it based on frequency of use.

Computers can efficiently search their vast stores of data by grouping what needs to be accessed most frequently and searching these “caches” first. In the same way, Christian and Griffiths recommend you “cache” your physical belongings by creating small piles of your most frequently used clothes, books, and files within arm’s reach of where you’ll need them.

Marie Kondo Rejects This Algorithm

In The Life-Changing Magic of Tidying Up, Marie Kondo argues that sorting your belongings by frequency of use is a common organizational mistake. In her eyes, the seconds you may save by storing everything in “caches” within arm’s reach incur a greater cost: the clutter of countless piles around the house.

Kondo asserts that this kind of “organization” is really disorganization in disguise. More often than not, we’ll drop our belongings wherever we are, then build our routines around the location of these new caches. Additionally, this system lacks a way to easily memorize where everything is, so if you need something that’s stored in an unusual place, you’ll struggle to find it.

Algorithm #7: How to Sort Like a Computer

Christian and Griffiths’s final organizational algorithm dictates the most efficient way to sort a group of items into a specific order. They argue that we should use the same sorting algorithms computers use to arrange files to efficiently sort physical collections in our own lives.

The best sorting strategy Christian and Griffiths have to offer is to first divide the collection into small categories, then rearrange individual items—a computer algorithm known as “Bucket Sort.” This algorithm is based on the fact that sorting gets more difficult with scale. Sorting a large group takes significantly more time than sorting four groups one-fourth of its size.

For example, if your boss were to ask you to sort twenty years’ worth of old archived meetings on VHS by date, you would want to use a Bucket Sort: First, divide them into piles by year, then arrange the smaller piles by hand.

How Other Sorting Algorithms “Divide and Conquer”

Since sorting gets more difficult with size, many of the most efficient sorting algorithms involve separating the collection into smaller groups, just like Bucket Sort. These are known in computer science as “divide-and-conquer algorithms.”

One of the most popular divide-and-conquer algorithms that Christian and Griffiths chose to exclude is called “quicksort.” With quicksort, you pick an item to be your “pivot” and divide the entire collection into two groups based on whether they should be before or after the pivot. You repeat with a new “pivot” within each group until the whole list is sorted.

Divide-and-conquer algorithms come in handy in situations where Bucket Sort doesn’t work well. If many of the items in your collection are too similar, you won’t be able to come up with buckets that evenly divide them. Additionally, if the buckets you create don’t evenly divide your collection as well as you expect them to, the time you spend Bucket Sorting is a waste.

Problem-Solving Algorithms

Algorithm #8: How to Solve Impossible Problems

The world is incredibly complex, and many problems are literally impossible to precisely solve. Christian and Griffiths argue that the best way to solve problems like these is to strategically embrace imperfection.

Even when experts use computers to make exact calculations, they often trade away accuracy to save time. From this, Christian and Griffiths conclude that simply lowering your standard for success is often necessary to keep moving forward. In some cases where it’s impossible to find the perfect solution, getting close is just as good.

In other cases, Christian and Griffiths suggest you employ the mathematical problem-solving strategy of “constraint relaxation.” By removing some constraints and solving an easier version of your problem, you spark new ideas to help solve the original problem.

Quantum Computing Could Solve Impossible Problems

Christian and Griffiths argue that imperfect strategies such as constraint relaxation are necessary because some problems are simply impossible for us to solve. However, in the near future, we may not need to make this compromise. Some believe that we’re quickly approaching a watershed moment in computer science in which many of the problems we see as impossible will become solvable—thanks to “quantum computing.”

Instead of processing information in ones and zeroes like a traditional computer, quantum computers can perform calculations on data that are neither ones nor zeroes—each digit has a chance of being either value and behaves like something totally new. This isn’t just theory—quantum computers already exist, and they’re extremely efficient. For now, they make too many errors to be of any real use, but engineers are working hard to fix this in the near future.

Algorithm #9: How to Solve Your Problems by Acting Randomly

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

The authors explain that computers use something called the “hill-climbing” algorithm to solve problems. They calculate a solution, then slowly improve it by testing out small adjustments. When developing their problem-solving strategies, people naturally follow a similar process. However, both computers and humans run into the same issue with hill climbing: Eventually, they hit a “local maximum”—a solution that can’t be improved by small adjustments yet is far from the best solution available.

According to Christian and Griffiths, the way to escape local maxima is an injection of irrational randomness. By making a few random, intentionally suboptimal decisions, you can discover new solutions you couldn’t see before and get unstuck. Feeling stagnant and directionless in life? Pick up a random new hobby or move to a random new town.

Enlightenment Through Extreme Randomness

In How to Live, Derek Sivers takes Christian and Griffiths’s argument to the extreme, advocating for a fulfilling life entirely built around randomness.

Like Christian and Griffiths, Sivers points out that random decision-making lets you encounter valuable experiences that you never would have intentionally chosen. According to Sivers, these random experiences will transform you. You’ll no longer base your identity or self-worth on your career or the way you dress since you didn’t choose them.

In fact, he argues that by making your decisions randomly, you can live a life entirely without ego. You never need to worry about whether or not you’re making the responsible choice, or if you’re doing everything you can to ensure a good future. Instead, you’re free to live wholly in the present, enjoying life as it is instead of how it could be.

Miscellaneous Algorithms

Algorithm #10: How to Use Game Theory

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.

Christian and Griffiths explain that we can view many systems in our society as competitive games and analyze them using game theory. In game theory, a “Nash equilibrium” occurs when every player has settled into the best possible strategy available to them, stabilizing its outcome. According to the authors, it’s the job of policymakers to ensure that the system’s Nash equilibrium results in an outcome that benefits all players—a process known as “mechanism design.”

For example, laws that regulate overfishing are meant to adjust the Nash equilibrium of the “game’ of commercial fishing. If unregulated, the optimal strategy for each fisher is to catch and sell as many fish as possible. Unfortunately, the fishers in this Nash equilibrium may drive a population of fish to extinction, which harms all players. By penalizing overfishing, the laws cause sustainable fishing to become the new optimal strategy, creating a new Nash equilibrium.

Nash Equilibria Are Imprecise Tools

Christian and Griffiths frame the Nash equilibrium as a useful tool for policymakers. However, some argue that Nash equilibria are nearly useless for this purpose. Even Christian and Griffiths admit that most of the time, Nash equilibria are impossible to predict or calculate algorithmically, hindering their practical use.

On the other hand, the concept of Nash equilibria is, at the very least, useful as a general intellectual framework, even if they can’t be precisely calculated. The concepts and vocabulary of Nashian game theory help decision-makers ask the right questions and glean new insights. For example, a lawmaker introducing a new policy doesn’t need to mathematically calculate its exact equilibrium—all game theory needs to do is spark the question: “Will following this policy be the optimal strategy for everyone?” If not, others will find a way around it, and the policy likely won’t function properly. In short: Vague, imprecise game theory is still useful.

Algorithm #11: How to Enhance Communication

To conclude, we’ll cover an algorithm that Christian and Griffiths draw from the Internet’s networking protocols: To communicate effectively, listeners need to signal that they’ve received the message.

The authors explain that when a computer connects to a server, both sides exchange “acknowledgment packets,” or “ACKs,” to ensure that the connection is stable. These are short messages that tell the other computer that its message has been received. These ACKs are a vitally necessary part of the communication process, and they make up a huge portion of all uploaded data.

Christian and Griffiths assert that, similarly, acknowledgment is an extremely important part of human communication, and it’s one that we often overlook. Recent research in the field of linguistics has put renewed focus on “backchannels,” a listener’s short interjections that acknowledge a speaker’s message without ending their turn to speak. It takes more than being quiet and polite to be a good listener—if you don’t give active feedback, communication falls apart.

What Does a Good Listener Really Look Like?

Common advice on being a good listener often contradicts Christian and Griffiths’s perspective—for example, Dale Carnegie’s classic How to Win Friends and Influence People argues that the best conversationalists do nothing but listen attentively while the other person talks. However, recent research indicates that this isn’t the whole picture: As Christian and Griffiths maintain, the best listeners are much more active in conversation than Carnegie claims.

What makes the situation confusing is the fact that many kinds of listener interjections are unwelcome. For example, a common criticism of bad listeners is that they attempt to solve problems right away instead of just listening—unlike in network transmission, not all “acknowledgment packets” make your conversation partner feel heard.

A good rule of thumb is to verbally confirm you understand the situation and how the listener feels before offering any suggestions or advice. By rephrasing the speaker’s message to confirm your understanding, you not only demonstrate that you were listening with care, but you also help the speaker better understand their situation. Your conversation partner will appreciate you on both counts.