Who is the author of Algorithms to Live By?

The authors of Algorithms to Live By are Brian Christian and Tom Griffiths.

Science

Algorithms to Live By Book Summary - Algorithms to Live By Book explained in key points

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Algorithms to Live By summary

Name: Algorithms to Live By
Rating: 3.8 (209 reviews)
Author: Brian Christian & Tom Griffiths

Brian Christian Tom Griffiths

The Computer Science of Human Decisions

3.8 (209 ratings)

22 mins

Brief summary

'Algorithms to Live By' by Brian Christian and Tom Griffiths explores how we can use algorithms from computer science to make better decisions in our daily lives. It offers practical insights on how to optimize time, prioritize tasks, and make choices with the help of an algorithmic approach.

Topics

Effectiveness Digital Transformation Mathematics Decision-Making

Table of Contents

Algorithms to Live By
Summary of 10 key ideas

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Key idea 1 of 10

What’s in it for me? Put algorithms to use in your own life.

Let’s take a second to think about that smartphone or tablet in front of you. How does it “think”? For instance, how does it “know” how to handle a heap of data and present it to you as the blinks you’re reading – or listening to – right now? The answer, of course, is that a series of instructions – or, in other words, an algorithm – enables it to do this.

But, as you’ll learn from these blinks, it’s not only computers that use algorithms. They also play a part in your own thinking, in your gut feelings and decisions. And here’s the best part: when your own algorithms aren’t up to the task of solving a difficult problem in your life, you can turn to simple but powerful computer algorithms for help.

In these blinks, you’ll learn

how math can tell you when to stop dating and settle for the partner you’re with;
how algorithms can help you sort your collection of zombie books; and
why that mess on your desk isn’t half as bad as it looks.

Algorithms help both humans and computers to solve problems.

If you’re someone who tries to stay abreast of today’s technology, you probably already know that computers use algorithms all the time to solve problems. But you may have asked yourself, “What exactly is an algorithm?”

The word actually dates back to the ninth century, when it was first used by the Persian mathematician Muhammad al-Khwarizmi. But the use of algorithms can be traced roughly four thousand years back, to the Sumerian civilization.

Simply put, an algorithm is a finite series of steps that help solve a problem – and it’s a technique we use all the time.

Even a recipe can be thought of as an algorithm: you follow a series of instructions to get the desired result, a delicious meal. The same can be said for the pattern you follow to knit a scarf or put together some Ikea furniture.

And when you’re putting together a list of pros and cons to decide whether or not to accept a job offer or to make a big decision, you’re also following what’s known as an intuitive algorithm.

By their very nature, the intuitive algorithms that humans use aren’t precise. We use them in times of uncertainty to make the best decision we can, like weighing the potential benefits against the risks of jumping into a new business investment.

Therefore, these intuitive algorithms may seem rather subjective and random compared to the mathematical algorithms a computer uses, though they basically provide the same solution.

Take the unpleasant task of apartment hunting. Most people go into this process with a set of criteria in mind: a minimum amount of space, a certain distance from school or work, a maximum amount of rent. When these conditions are met, that’s when you take the next step and sign the lease.

This is essentially the same method that computer algorithms use, and in the next blinks we’ll explore how these methods can work for you.

Most of the time, algorithms can tell us when to stop pressing our luck.

If you’ve been out searching for an apartment in a competitive market, you probably know how difficult it can be to decide when to take an offer and stop searching.

Our judgment is often clouded by the first thing that comes along, which we tend to perceive as the best option available. And, just as easily, the second option can seem like the next best thing.

Mathematical algorithms can help you decide when it’s time to explore something new.

Let’s stick with gambling for a minute and look at one of the most popular games at any casino: the slot machine, or “one-armed bandit.”

While some people are happy to sit at a single slot machine all day in the hopes of eventually hitting the jackpot, others prefer to explore their options, gather information and try to use it to their advantage. This same basic dilemma of how long to stick with a losing option before moving on applies to a number of situations in life, such as dating or investing. But what’s the winning strategy?

You don’t always need help sorting your files, but if you do, algorithms can help.

Do you ever feel like you can’t find anything you’re looking for once your desk or office gets cleaned up? Well, if you’re the kind of person who prefers a certain amount of organized chaos, then you’ll be happy to hear this next bit of advice.

Don’t worry about always keeping everything neat and tidy.

When it comes to organizing data, there’s a lot you can learn from computers.

Now that your shelves are in order, let’s turn to those stacks of paper on your desk and all the notes and info that you keep close at hand. These are probably important things, like pending bills or letters needing an urgent reply – documents requiring easy access.

Computers have a pretty useful way of dealing with things that need to be quickly retrieved. They store their data either in a hard disk drive or a solid state drive. Each comes with its own advantages.

Algorithms can help us schedule our lives, but they also have their limits.

Aside from getting files in order, another key to productivity is organizing your time and figuring out how to get everything done. Every day has its challenges. It can be difficult to find the time to meet all your deadlines – and not forget to take the dog to the vet.

Fortunately, there are plenty of algorithms that deal with these kinds of scheduling problems.

The right algorithms can help you predict the future.

Most of us would love to be able to see into the future, but what about the next best thing: predicting what will probably happen?

With the help of algorithms, predicting probable outcomes isn’t so far-fetched.

Algorithms help us to exchange messages and handle data overload.

Here’s a classic thought experiment: Two generals are preparing to launch a joint attack on a city located in a fortified valley. The only problem is that each general is on a hill with the valley separating them, and before they can attack they need to agree on the exact time. However, the only way to get a message across is to send someone through the valley, where their messengers are likely to be captured. So how can they determine a time and know that the other has agreed to it?

This kind of problem is something computer scientists pondered in order to come up with algorithms to help ensure messages get to their destination safely.

There are algorithms to help figure out what people will do and guide them when making decisions.

Have you ever heard of the prisoner’s dilemma? Imagine you and a partner have successfully robbed a bank. Later, you’re both brought in for questioning by the police and placed in separate rooms. You know they don’t have sufficient evidence to convict either of you for the robbery, so if you keep your mouth shut, all they can do is give you a one-year sentence for a lesser charge. After that, you will get out and can enjoy your loot.

But here’s the twist: the cops offer both you and your partner a deal: If you testify against your partner and he stays silent, you’ll go free and he’ll go to jail for ten years. However, if you both turn on each other, you’ll each get a five-year sentence. What do you do?

It’s important to know that algorithms have their limits.

When you’re trying to model something complicated, complex models are generally better than simple ones.

For example, if you’re developing a model to explain the cause of obesity, you’ll want a complex one that takes many factors into account, from a poor diet to genetics to lack of exercise. A simple model might focus solely on diet, which would be insufficient.

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What is Algorithms to Live By about?

Algorithms to Live By (2016) is a practical and useful guide that shows how algorithms have much more to do with day-to-day life than you might think. And not just that; they can also lead to a better life by helping you solve problems, make decisions and get more things done.

Algorithms to Live By Review

Algorithms to Live By (2016) dives into the fascinating world of algorithms and how they can be applied to our everyday lives. Here's why this book is a must-read:

Packed with practical examples, it offers valuable insights into how algorithms can help us make better decisions, solve problems, and optimize our lives.
By combining computer science with psychology and philosophy, the book delves deeper into the human mind and behavior, offering a fresh perspective on decision-making.
The authors' engaging storytelling and relatable examples keep the content relatable and captivating, making it a thought-provoking and enjoyable read.

Best quote from Algorithms to Live By

Science is a way of thinking much more than it is a body of knowledge. - Carl Sagan

—Brian Christian & Tom Griffiths

Who should read Algorithms to Live By?

Busy people who want to improve their time management
Anyone interested in what algorithms do
People who want to get more organized

About the Author

Brian Christian is a best-selling author and renowned public speaker who specializes in cognitive science. His work has won numerous awards and he’s been honored by the Academy of American Poets. He’s also a contributor to The New Yorker, Wired, The Guardian and many scientific journals.

Tom Griffiths, an award-winning professor of cognitive science and psychology, leads the Computational Cognitive Science Lab at the University of Berkeley. He has published more than 150 scientific papers.

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