Algorithms to Live By: The Computer Science of Human Decisions
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Algorithms to Live By: The Computer Science of Human Decisions Audible Audiobook – Unabridged

Brian Christian (Author, Narrator), Tom Griffiths (Author), Brilliance Audio (Publisher)
4.5 4.5 out of 5 stars 5,900 ratings
Goodreads Choice Award nominee
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A fascinating exploration of how computer algorithms can be applied to our everyday lives, helping to solve common decision-making problems and illuminate the workings of the human mind

All our lives are constrained by limited space and time, limits that give rise to a particular set of problems. What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? What balance of new activities and familiar favorites is the most fulfilling? These may seem like uniquely human quandaries, but they are not: computers, too, face the same constraints, so computer scientists have been grappling with their version of such problems for decades. And the solutions they've found have much to teach us.

In a dazzlingly interdisciplinary work, acclaimed author Brian Christian (who holds degrees in computer science, philosophy, and poetry, and works at the intersection of all three) and Tom Griffiths (a UC Berkeley professor of cognitive science and psychology) show how the simple, precise algorithms used by computers can also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one's inbox to understanding the workings of human memory, Algorithms to Live By transforms the wisdom of computer science into strategies for human living.

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  1. Listening Length
    11 hours and 50 minutes
  2. Author
    Brian Christian, see all
  3. Narrator
    Brian Christian
  4. Audible release date
    April 19, 2016
  5. Language
    English
  6. Publisher
    Brilliance Audio
  7. ASIN
    B01D24NAL6
  8. Version
    Unabridged
  9. Program Type
    Audiobook
  10. See all details
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Product details

Listening Length 11 hours and 50 minutes
Author Brian Christian, Tom Griffiths
Narrator Brian Christian
Whispersync for Voice Ready
Audible.com Release Date April 19, 2016
Publisher Brilliance Audio
Program Type Audiobook
Version Unabridged
Language English
ASIN B01D24NAL6
Best Sellers Rank #2,708 in Audible Books & Originals (See Top 100 in Audible Books & Originals)
#2 in Computer Science (Audible Books & Originals)
#13 in Business Decision Making & Problem Solving
#22 in Cognitive Psychology (Books)

Customer reviews

4.5 out of 5 stars
4.5 out of 5
5,900 global ratings
Two different book sizes for paperback!
3 Stars
Two different book sizes for paperback!
Loved this book so much I ordered 8 for my team as a Christmas present. I ordered paperback the first and second time and to my horror the ones I got for gifts were so much smaller than the first one. Bummer! Print is so small it’s hard to read it and you can’t open the book properly even when force bending it. Hopefully someone from the publisher can see this comment hence the 3 star. Else- content wise I give it 5 stars! As a mathematician and computer scientist, I am happy this book exists. It has been a long time since a popular read has given me this much joy.
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Top reviews from the United States

Adam P.
5.0 out of 5 stars Very interesting read
Reviewed in the United States on April 30, 2024
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This text was listed as required reading in the syllabus for an Analytics course, so I purchased the book and started reading it. When I got into the course, the instructor informed us that it was in fact optional reading. However, it was very interesting and I chose to continue reading the book. Very nicely ties together analytics, computer science, and how humans make decisions, all while being not too technical and injecting a little bit of humor.
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Justinian Vonkost
4.0 out of 5 stars A brief intro into what algorithms are and are not
Reviewed in the United States on May 31, 2021
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When one thinks of algorithms, it is often in association with computers or machines. Not humans. It is also common to think algorithms are there to provide a simple, neat solution to complex problems only a machine could solve. Or that algorithms can, once fed enough information, predict one’s every action and solve every problem. The main premise of Algorithms to Live By is to disabuse one of such notions. Algorithms to Live By explores how regular people use algorithms without even realizing it in their day-to-day lives. By doing so, the authors hope to destigmatize the word and get people to see the concept differently. Though the book can be dry at times, the authors manage to write a book that is accessible to most people. And there are moments of insight that do make the book a fascinating read.

As aforementioned, the book explores how people use algorithms in their day-to-day to accomplish tasks. They focus on several elements: explore/exploit, or when it is best to continue to look for something better or make a choice from what one already knows; sorting and tradeoffs; and scheduling being among the subjects of focus. What makes these sections interesting is that they often talk about tradeoffs that one would seem counterintuitive. An example of this is in the scheduling section. The authors mention how the placement of a task on a schedule may be influenced by how much one knows about the task: by its duration or difficulty. This may increase the difficulty of scheduling if one were to know every detail of every task that must be done for the day. They also mention that while some may be tempted to schedule tasks based on how easy they are, this may also come with downsides. Especially if one decides to prioritize harder tasks before easier ones, only to realize that its completion requires completing an easier task. They give an example of a NASA Mars rover being frozen due to this fact. The rover was programmed to prioritize high priority tasks first in its queue over low priority tasks. However, one of the low-priority tasks kept being pulled from the bottom of the queue to the top. This caused the rover to freeze. Thus, even well-thought-out systems can lead to problems.

The above example with NASA shows another aspect of the book I like; the use of real world examples. The authors tell stories involving real world mathematicians and scientists struggling with these issues in their personal lives. This helps make the subjects feel personal and applicable to one's own life. In fact, I would argue that the only issue with the book is that these anecdotes seem to be an afterthought. This is due to the fact that the anecdotes become more prominent as the book progresses towards the end. Thus, the first few chapters can be somewhat dry in its presentation which may turn off a lay reader. Furthermore, the use of hypothetical scenarios in the earlier chapters feel like a pale imitation of the personal anecdotes of later chapters.

All in all, this book was fairly enjoyable. While having some rough patches, the authors did try and succeed in making an accessible book.
5 people found this helpful
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Kindle Customer
5.0 out of 5 stars Mathematically Challenged?
Reviewed in the United States on April 8, 2024
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At last a book that relates cognition to computers in an understandable way. Starting with hypothetical real life situations that attracted mathematicians or.computer scientists, the authors address the outcomes from simple to complex to insoluble. The more complex issues reveal the relative values of computers, society, experience, evolution and our own brains! A great way to approach machine learning.
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John MD.
5.0 out of 5 stars Practical Advice for Life Decisions
Reviewed in the United States on April 22, 2024
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A beautifully written book that combines Computer Science with everyday situations, and gives optimal solutions to many of these problems.
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fitzalling
5.0 out of 5 stars Mathematicians' contributions to everyday problems
Reviewed in the United States on June 19, 2017
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For me, the book takes intellectual effort to absorb. As I was preparing to write this review, I was further impressed with the range of information presented by the authors. I am personally undertaking an investigation of machine learning, artificial intelligence, data mining, etc; The book fit into this investigation. If you have interests in this area (or areas), I think you'll find the book useful. It probably shouldn't have, but the parallels between common human problems and computer programming surprised me. As the book has had a large number of reviewers already, I will highlight some, but far from all, of the topics of each chapter so you may see if they make you curious. While the book speaks of algorithms to live by, the mathematics in the book is highly limited.

Optimal stopping - how many people out of 100 possible candidates should one interview for a given position (including that of spouse)? 37%, Why? Read the book.

The Explore/Exploit dichotomy - Should one ask the question "What's new" or "What's best"? Your answer may depend on your time horizon. As your time horizon shortens, "what's best" may be the better question. The book explains why. The book also looks at the multi-armed bandit as an example of the explore/exploit dichotomy. What's a multi-armed bandit? Think of the one-armed bandit in Vegas and multiply its arms. Mathematicians do so. Their conclusions may be useful. The trials of music critics also fit into the explore/exploit dichotomy. The authors explain why music critics find exploration a chore.

Sorting - libraries are the metaphor for computer sorting. Human memory also requires sorting. Maybe the decline in memory as humans age may be due to the amount of information through which it must sort and not due to declining faculties. A five-year old has a lot less information to go through than a seventy-five year old. The authors consider sorting techniques with email, Yelp, and other common uses. There is much useful information.

Caching - when is forgetting necessary? According to the authors, the first computer cache was developed for a supercomputer in 1962 ub Manchester, England. I wonder how "super" that computer was? Caching allows some information to be stored for repetitive use and uncached information to be kept in the background.

Scheduling - many scheduling problems have "intractable" solutions. The authors suggest different solutions based on algorithms such as precedence constraints, earliest due date (one I personally use frequently, which I couple with a personal likely to get me in the most trouble the quickest test) and shortest processing time. The scheduling problem has received substantial effort from mathematicians.

Bayes's Rule - how to use statistical inference to make useful predictions. Couple a well-defined problem with a range of prior outcomes and one can make accurate guesses. A .300 hitter comes to the plate against the same pitcher who has already struck the batter out twice and it may be a fair guess that the hitter is due for a hit.

Overfitting - don't overthink and over complicate a problem. The authors advise against practicing the idolatry of data. A more complex theorem may well lead to less accuracy rather than more. On the level of incentive compensation, the authors quote Steve Jobs for being careful that you include only those elements in your incentive package that matter; you will get what you measure.

Relaxatrion - the perfect is the enemy of the good. To get any useful answer from your mathematical model, it may be necessary to relax some of your constraints (insisting that your model never allow the traveling salesman to re-enter the same city twice may preclude any answer at all in a time period of less than the remaining life of the universe).

Randomness - mathematicians sometimes realize that the best answer comes from sampling and not from strict calculations. This may explain why I get so many survey requests. Algorithms for prime numbers use this technique. And, apparently, thousands of years ago the Greeks were already looking for prime numbers.

Networking - here the authors examine the "Byzantine generals" problem, which plays a part in explaining how computers communicate with each other.

Game Theory - Alan Turing investigated the "halting problem" in the 1930s. What if you give your computer a problem and it just keeps going? Rock, paper, scissors is a game with which most are familiar. It, too, is part of game theory. When a game seems to have no satisfactory answer, maybe it's time to change the game. What happens when you have an "information cascade"?

If any ot this interests you, I believe that you will enjoy the book. I recommend it highly.
27 people found this helpful
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Mark Ellins
3.0 out of 5 stars A few nice takeaways but not life moving
Reviewed in the United States on October 28, 2023
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If I ever wrote a non-fiction book, there is no doubt I would make the first chapter the best. As so was the case here. The first chapter was about the 37% Decision Rule and it was really and usable. All the takeaways in the book are useful but technically, you can use any social or scientific theories and apply it to life. For example, economic theories are really relevant for everyday life, especially when making decisions. Maybe I should write a book. As the chapters went on, the theories got blander and blander.
It is still an interesting read. I give it three stars only because, when you finish the book, you are only going to be slightly smarter.
4 people found this helpful
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Barbara Bishop
4.0 out of 5 stars good book for the analytical minded
Reviewed in the United States on August 11, 2023
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This book is fascinating but not the easiest read. Most STEM majors would dig it.

Rules by which to live by.
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Top reviews from other countries

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Moises Rangel Silva
5.0 out of 5 stars Gracias muy buen libro.
Reviewed in Mexico on December 30, 2022
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Buen enfoque de las decisiones humanas con la ciencia de computación.
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Ignas Selvestravičius
5.0 out of 5 stars Worth reading several times
Reviewed in Germany on January 7, 2024
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Worth reading several times
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Alessandro Mariani
5.0 out of 5 stars Excellent
Reviewed in Italy on January 2, 2024
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Very readable introduction to several algorithms and results in computational complexity which help us understand our daily life as well as policy decisions. This is popular science done well. Highly recommended.
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swordiron
5.0 out of 5 stars Excellent
Reviewed in France on August 8, 2023
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Pleins de bonnes idées et de bons concepts à la limite entre la philosophie et l'informatique. Je recommande très fortement.
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Jack Coles
5.0 out of 5 stars Fantastic for everyday decision-making
Reviewed in the United Kingdom on February 16, 2023
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This was a superb attempt to use the methods and working practices of computer scientists, and some less technical algorithms too, in everyday situations. As it states, ‘Algorithms to Live By’.

I’m generally not a fan of the sort of smart-thinking books which aggregate and cherry-pick research and force it through one unhelpful paradigm. They usually have an awful subtitle, and choose poor quality research to back up their points. They all end up saying the same thing, largely.

This book is not one of those. It explained things in a much easier and accessible way than I have ever seen them explained, such as caching, distributions and Bayesian probability. And it also introduced the reader to a number of ways computer scientists think and terms and framings that they use in everyday work that applies in real life.

This book could apply to a great many situations for the reader, from work problems to everyday life - like selling a house - and is simple, yet technical. The authors are good at communicating their thoughts, even if they do make one or two charitable assertions about some aspects that relate to what they are covering.

There is good life advice, good computer science advice, and a short primer on some of the more basic tenets of computer science, statistics and probability, and some analysis too. Fantastic stuff.
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