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Book Summary: Algorithms to Live By

What do computer algorithms have to do with our daily lives? More than you might think. In "Algorithms to Live By," authors Christian and Griffiths explore the ways in which we can use computer algorithms to optimize our own decision-making processes.

In this book, the writers argue that computer science, often viewed as a specialized field, actually holds valuable practical knowledge that can enhance our lives. Computers can perform some tasks very efficiently, and by using similar methods, we can also achieve this efficiency.

The authors believe this is true because both humans and computers face comparable challenges. Namely, both aim to use their limited resources (memory, attention, and time) in the best possible way. As a result, many computer algorithms, or sets of rules, can be useful if applied to our daily lives.

In this summary, I will cover all eleven of Christian and Griffith's "life algorithms," which I've grouped into four categories:

  1. Algorithms to aid in making better choices
  2. Algorithms to help arrange your life
  3. Algorithms to assist in tackling tough problems
  4. Algorithms that don't fit into the first three groups

Decision-Making Algorithms

Algorithm 1: When to stop looking for something better

Explore without committing during the first 37% of your options, then choose the next best option you find

This algorithm helps solve a challenge in math called the "optimal stopping problem." It asks: when should you commit to an option in front of you if you're unsure about future possibilities?

For instance, when searching for a new home, this algorithm recommends checking 37% of available options to increase the chance of finding the best place. After that, make an offer on the next place better than what you've seen so far. In practice, if you have a month to find a home, use 37% of the time (11 days) to explore and set a standard. After 11 days, purchase the next home better than the ones in your "standard" (the homes seen in the first 11 days).

This is known because finding a place to live is an example of an optimal stopping problem, which mathematicians and computer scientists have studied extensively. From choosing a restaurant to deciding on a life partner, our lives are full of complex problems that can't be solved just by putting in more effort.

Minimizing Decision Fatigue: Relying on a carefully devised algorithm for life choices might seem too limiting, as it doesn't offer much room for adjusting to unexpected situations. However, Christian and Griffith's strict guidelines could help by decreasing the number of decisions you need to make. Studies indicate that making decisions throughout the day can deplete your mental energy, increasing the chances of succumbing to unhealthy impulses, such as watching TV instead of going for a nighttime jog. By reducing decisions in your life, you conserve mental energy for important choices. This "decision fatigue" concept supports the authors' overall goal with the book: enhancing life through algorithms. Once you commit to following an algorithm's predetermined steps, decisions are made for you, theoretically easing the mental load and simplifying your life.

Algorithm 2: How to optimize your life

To optimize your life, follow any opportunity with the potential to be the greatest

The authors depict life as a complex "multi-armed bandit" problem, a model used in machine learning by computer scientists. This classic problem in probability theory and decision-making involves an agent choosing between multiple actions or "arms" with different and uncertain reward outcomes. Each arm represents a decision or action, and the agent receives a reward based on that arm's distribution. The agent's challenge is to maximize its total reward over time, balancing the exploration of unknown arms to learn their reward distribution and exploiting known high-reward arms for the maximum possible reward.

The authors explain that the multi-armed bandit problem's optimal solutions are called "Upper Confidence Bound" algorithms, which suggest making decisions based on the best-case scenarios of your options. In practice, this means pursuing any opportunity in life that you think could offer the highest reward, even if it seems highly unlikely. This is because the only way to accurately evaluate the probability of a payout is to test it yourself and decide if it's worthwhile as you gather more information and insight. If you've tried something and found it's not worth your time, adjust accordingly and aim high elsewhere.

Preparing for Black Swans: Nassim Taleb supports the idea of pursuing opportunities with a low chance of extraordinary success in his book The Black Swan. He takes this concept even further, suggesting that you should disregard an opportunity's historical performance and anticipated gains, and instead concentrate on the range of possible outcomes. This includes the possibility of extremely negative results, unlike Christian and Griffiths, who focus on positive ones. For instance, if a bank has consistently made millions giving out loans for the past 40 years, you might think it's a profitable "bandit" worth investing in. However, Taleb argues that this track record is meaningless, and the nature of loans always carries the significant risk of borrowers defaulting. In other words, even if an opportunity offers an excellent best-case scenario, it should be avoided if it also has an equally extreme worst-case scenario – a point Christian and Griffiths don't consider.

Algorithm 3: Making better predictions

To enhance your predictions, start by using your previous understanding of the situation to estimate the likelihood of an event, and then adjust based on the observed data

This approach, which involves using your prior knowledge to examine the evidence you have, is known as "Bayes's Rule."

For instance, if you want to estimate when you'll get a raise at work, you could start by asking a colleague how long it took them to receive a raise and then adjust the estimate considering your boss's perception of your performance.

Focusing on relevant information: In Superforecasting, Philip Tetlock and Dan Gardner concur that applying Bayes's Rule correctly is essential for making accurate predictions. However, they argue that most people struggle with this type of thinking because proper Bayesian inference requires not only precise knowledge of the situation, but also an understanding of the significance of each piece of information, something humans are generally not good at. In our example, you might overestimate the influence of your job performance on your pay raise and assume you'll get a raise much sooner than you actually will. Tetlock and Gardner explain that the best "superforecasters" make considerably smaller adjustments based on new information compared to the average predictor. In most situations, only a few key facts will significantly impact your forecast, so when adjusting your prediction, you should disregard the majority of the observable evidence.

Algorithm 4: The case for less-informed decisions

For better decision-making, take into account fewer details

Overfitting is a phenomenon in statistics and machine learning where a model becomes excessively trained on a specific dataset, capturing noise or irrelevant patterns in the data rather than the actual relationships between the features and target variable. Christian and Griffiths argue that, similarly, if you consider too many factors when making a decision, you'll "overfit," overvaluing the impact of unimportant information and undervaluing the aspects that truly matter. The authors suggest that the key to overcoming overfitting is to deliberately limit the amount of information you take into account when making decisions. Focus on one or two crucial factors and disregard everything else. For instance, you could choose a job based solely on how much you expect to enjoy the work.

Minimalism - Avoid Overfitting Your Life: Christian and Griffiths propose that to overcome overfitting, you should concentrate on what's important and disregard the rest. In Minimalism, Joshua Millburn and Ryan Nicodemus extend this principle to life itself. They contend that modern humans tend to overfit, striving to increase happiness by accumulating more in their lives, rather than focusing on the few essential factors. Possessions like luxury cars, lavish homes, and idyllic vacations only distract us from the truly valuable aspects of life, such as personal health, loving relationships, and a sense of contributing to others. Generally, eliminating things in your life that don't provide value is a more sustainable route to happiness than continually pursuing bigger and better new pleasures.

Organizational Algorithms

Algorithm 5: How to schedule your time effectively

Your ideal scheduling algorithm depends on your objectives and priorities

Instead of providing a single algorithm for scheduling, the authors emphasize that computers use algorithms tailored to their specific needs to determine which tasks to prioritize. Similarly, they argue that the best way to schedule your time is closely tied to your goals and priorities.

Most of the time, your top priority is to finish tasks that yield the most value. The authors suggest assigning a "weight," a numerical measure of value, to each item on your to-do list. By dividing this weight by the time required to complete the task, you can easily calculate the value generated per hour of work. Then, you should focus on whatever task offers the most value per hour at any given time.

Another scheduling algorithm the authors recommend is "Shortest Processing Time," which advises you to work on the task that takes the least time to finish. The authors argue that you might choose this algorithm if you need motivation or if you feel stressed and overwhelmed by a large number of tasks.

Tackle Your Toughest Tasks First: In the bestselling productivity book "Eat That Frog!", Brian Tracy emphasizes the importance of prioritizing tasks based on their value. He contends that your most valuable tasks are often the most challenging to complete, leading many people to procrastinate and fill their time with easy, low-value tasks that accomplish little. Tracy's core idea is that unless you deliberately tackle difficult, high-value tasks first, life will constantly present you with easy, low-value tasks, preventing you from ever addressing what truly matters. Tracy disagrees with the "Shortest Processing Time" algorithm, arguing that breaking down your most important tasks into a series of shorter steps allows you to convert everything you need to do into tasks that require roughly the same amount of time.

Algorithm 6: Organizing your belongings

To access any collection effectively, categorize it based on usage frequency.

Computers efficiently search their extensive data stores by organizing frequently accessed items and checking these "caches" first. Similarly, Christian and Griffiths suggest "caching" your personal belongings by creating small stacks of your most frequently used clothes, books, and files within easy reach of where you'll need them.

Marie Kondo Disagrees with This Algorithm: In The Life-Changing Magic of Tidying Up, Marie Kondo argues that organizing belongings based on usage frequency is a common organizational error. According to her, the few seconds saved by having everything in "caches" within easy reach come at a greater cost: clutter from numerous piles around the house. Kondo believes that this type of "organization" is actually disorganization in disguise. More often than not, we leave our belongings wherever we are and then adapt our routines to the locations of these new caches. Moreover, this system lacks an efficient way to remember where everything is, making it difficult to find items stored in unusual places.

Algorithm 7: Sorting like a computer

Use a "Bucket Sort" algorithm to efficiently arrange a set of items in a specific order

The authors suggest that we should apply the same sorting algorithms computers use for organizing files to effectively sort physical collections in our lives. The most efficient sorting strategy Christian and Griffiths recommend is dividing the collection into smaller categories first, then reordering individual items - a computer algorithm known as "Bucket Sort." This algorithm is based on the principle that sorting becomes more challenging with scale. Sorting a larger group takes considerably more time than sorting four smaller groups each one-fourth its size.

For instance, if your boss asked you to organize twenty years' worth of old archived meeting VHS tapes by date, you would use a Bucket Sort as follows: First, separate 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 collections 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 the authors 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 so 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 divide your collection as well as you expect them to, the time you spend sorting is a waste.

Problem-Solving Algorithms

Algorithm 8: Tackling seemingly impossible problems

Strategically embracing imperfection is often the best way to solve problems that appear impossible to solve precisely

The world is incredibly complex, and many problems are virtually impossible to solve with exact precision. Even when experts utilize computers for precise calculations, they frequently sacrifice accuracy to save time. From this observation, the authors deduce that sometimes lowering your standard for success is necessary to keep making progress. In certain situations where finding the perfect solution is impossible, getting close to it is just as valuable. In other instances, the authors suggest employing the mathematical problem-solving technique of "constraint relaxation." By eliminating some constraints and solving a simpler version of the problem, you can spark new ideas to help tackle the original problem.

Algorithm 9: Using randomness to solve problems

To move past dead ends, act randomly

The authors describe how computers use the "hill-climbing" algorithm to solve problems. They compute a solution and then incrementally improve it by testing small adjustments. Humans naturally follow a similar process when developing problem-solving strategies. However, both computers and humans encounter the same issue with hill climbing: they eventually reach a "local maximum"—a solution that can't be improved by minor adjustments but is far from the best solution available.

Christian and Griffiths contend that the key to escaping local maxima is introducing a dose of irrational randomness. By making a few random, intentionally suboptimal decisions, you can uncover new solutions that were previously invisible, allowing you to break free from being stuck. Feeling stagnant and lacking direction in life? Try taking up a random new hobby or relocating to a random new town.

In How to Live, Derek Sivers takes the argument of Christian and Griffiths to the extreme, advocating for a fulfilling life constructed entirely around randomness. Sivers highlights that random decision-making enables you to encounter valuable experiences that you would never have intentionally chosen. According to Sivers, these random experiences will transform you. You'll no longer anchor your identity or self-worth on your career or your appearance, as you didn't choose them. In fact, he posits that by making decisions randomly, you can live a life completely devoid of ego. You'll never need to worry about whether you're making the responsible choice or if you're doing everything possible to secure a promising future. Instead, you'll be free to live entirely in the present, appreciating life as it is rather than how it could be.

Miscellaneous Algorithms

Algorithm 10: Using Game Theory

To prevent collective harm, design the rules of the game to create win-win scenarios

To avoid collective harm, design the rules of the game to establish win-win scenarios Christian and Griffiths elucidate that numerous systems in our society can be viewed as competitive games and can be analyzed using game theory. In game theory, a "Nash equilibrium" arises when every player adopts the best possible strategy available to them, stabilizing the outcome. According to the authors, it is the responsibility of policymakers to ensure that the system's Nash equilibrium results in a mutually beneficial outcome – a process known as "mechanism design."

For instance, laws regulating overfishing are intended to adjust the Nash equilibrium of the "game" of commercial fishing. Without regulation, the optimal strategy for each fisher is to catch and sell as many fish as possible. Regrettably, this Nash equilibrium could drive a fish population to extinction, harming all players. By penalizing overfishing, the laws make sustainable fishing the new optimal strategy, establishing a new Nash equilibrium.

Algorithm 11: Communicating effectively

To communicate effectively, listeners need to signal that they've received the message

To communicate effectively, listeners must signal that they have received the message The authors explain that when a computer connects to a server, both sides exchange "acknowledge packets," or "ACKs," to ensure a stable connection. These brief messages inform the other computer that its message has been received. ACKs are a vital component of the communication process and constitute a significant portion of all uploaded data. Similarly, the authors assert that acknowledgement is an essential aspect of human communication, which is often overlooked. Recent research in linguistics has placed renewed emphasis on "backchannels," a listener's brief interjections that acknowledge a speaker's message without interrupting their turn to speak. Being a good listener requires more than just silence and politeness – if you don't provide active feedback, communication breaks down.

Christian and Griffith's perspective on effective listening differs from some traditional advice - for instance, Dale Carnegie's renowned book How to Win Friends and Influence People suggests that the best conversationalists simply listen attentively while the other person speaks. However, recent research supports Christian and Griffiths' stance, indicating that the best listeners are more active in conversation than Carnegie suggests. The challenge lies in the fact that certain types of listener interjections can be unwelcome. A common complaint about poor listeners is their tendency to offer solutions immediately, rather than just listening - akin to network transmission, not all "acknowledgement packets" help the speaker feel understood. A helpful guideline is to verbally confirm your comprehension of the situation and the speaker's feelings before providing suggestions or advice. By rephrasing the speaker's message to demonstrate your understanding, you not only show that you've been listening attentively but also assist the speaker in better grasping their own situation. Your conversation partner will appreciate your efforts on both fronts.